Journal articles: 'Noise Electroencephalography'

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Huynh, Tuan Van, and Vu Quang Huynh. "Study on method of filtering noises from electroencephalography signals and its application for identification of several electroencephalography signals." Science and Technology Development Journal - Natural Sciences 1, T4 (December 31, 2017): 95–104. http://dx.doi.org/10.32508/stdjns.v1it4.497.

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Electroencephalographic (EEG) signals have usually been affected by different types of noise as 50 Hz noise, mechanical noise caused by body movements, heart disturbance, eye noise... In this paper, methods such as: independent component analysis (independent component analysis-ICA), discrete wavelet transform and design of digital filters, were used to filter the noises, to classify the basic components for EEG signals. Then the mean of energy value was calculated to identify the status of the EEG signals such as blink, thoughts, emotion, smoking and blood pressure. The results of calculations and simulations of signals EEG could demonstrate the efficiency of the method.

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McMillan, Rebecca, Anna Forsyth, Doug Campbell, Gemma Malpas, Elizabeth Maxwell, Juergen Dukart, Joerg F. Hipp, and Suresh Muthukumaraswamy. "Temporal dynamics of the pharmacological MRI response to subanaesthetic ketamine in healthy volunteers: A simultaneous EEG/fMRI study." Journal of Psychopharmacology 33, no. 2 (January 21, 2019): 219–29. http://dx.doi.org/10.1177/0269881118822263.

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Background: Pharmacological magnetic resonance imaging has been used to investigate the neural effects of subanaesthetic ketamine in healthy volunteers. However, the effect of ketamine has been modelled with a single time course and without consideration of physiological noise. Aims: This study aimed to investigate ketamine-induced alterations in resting neural activity using conventional pharmacological magnetic resonance imaging analysis techniques with physiological noise correction, and a novel analysis utilising simultaneously recorded electroencephalography data. Methods: Simultaneous electroencephalography/functional magnetic resonance imaging and physiological data were collected from 30 healthy male participants before and during a subanaesthetic intravenous ketamine infusion. Results: Consistent with previous literature, we show widespread cortical blood-oxygen-level dependent signal increases and decreased blood-oxygen-level dependent signals in the subgenual anterior cingulate cortex following ketamine. However, the latter effect was attenuated by the inclusion of motion regressors and physiological correction in the model. In a novel analysis, we modelled the pharmacological magnetic resonance imaging response with the power time series of seven electroencephalography frequency bands. This showed evidence for distinct temporal time courses of neural responses to ketamine. No electroencephalography power time series correlated with decreased blood-oxygen-level dependent signal in the subgenual anterior cingulate cortex. Conclusions: We suggest the decrease in blood-oxygen-level dependent signals in the subgenual anterior cingulate cortex typically seen in the literature is the result of physiological noise, in particular cardiac pulsatility. Furthermore, modelling the pharmacological magnetic resonance imaging response with a single temporal model does not completely capture the full spectrum of neuronal dynamics. The use of electroencephalography regressors to model the response can increase confidence that the pharmacological magnetic resonance imaging is directly related to underlying neural activity.

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Bruhn, Jörgen, Thomas W. Bouillon, Andreas Hoeft, and Steven L. Shafer. "Artifact Robustness, Inter- and Intraindividual Baseline Stability, and Rational EEG Parameter Selection." Anesthesiology 96, no. 1 (January 1, 2002): 54–59. http://dx.doi.org/10.1097/00000542-200201000-00015.

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Background Artifact robustness (i.e., size of deviation of an electroencephalographic parameter value from baseline caused by artifacts) and baseline stability (i.e., consistency of median baseline values) of electroencephalographic parameters profoundly influence electroencephalography-based pharmacodynamic parameter estimation and the usefulness of the processed electroencephalogram as measure of the arousal state of the central nervous system (depth of anesthesia). In this study, the authors compared the artifact robustness and the interindividual and intraindividual baseline stability of several univariate descriptors of the electroencephalogram (Shannon entropy, approximate entropy, spectral edge frequency 95, delta ratio, and canonical univariate parameter). Methods Electroencephalographic data of 16 healthy volunteers before and after administration of an intravenous bolus of propofol (2 mg/kg body weight) were analyzed. Each volunteer was studied twice. The baseline electroencephalogram was recorded for a median of 18 min before drug administration. For each electroencephalographic descriptor, the authors calculated the following: (1) baseline variability (= (median baseline - median effect) [i.e., signal]/SD baseline [i.e., noise]) without artifact rejection; (2) baseline variability with artifact rejection; and (3) baseline stability within and between individuals (= (median baseline - median effect) averaged over all volunteers/SD of all median baselines). Results Without artifact rejection, Shannon entropy and canonical univariate parameter displayed the highest signal-to-noise ratio. After artifact rejection, approximate entropy, Shannon entropy, and the canonical univariate parameter displayed the highest signal-to-noise ratio. Baseline stability within and between individuals was highest for approximate entropy. Conclusions With regard to robustness against artifacts, the electroencephalographic entropy parameters and the canonical univariate parameter were superior to spectral edge frequency 95 and delta ratio. Electroencephalographic approximate entropy displayed the best interindividual and intraindividual baseline stability.

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Jamison, Caroline, Steve J. Aiken, Michael Kiefte, Aaron J. Newman, Manohar Bance, and Lauren Sculthorpe-Petley. "Preliminary Investigation of the Passively Evoked N400 as a Tool for Estimating Speech-in-Noise Thresholds." American Journal of Audiology 25, no. 4 (December 2016): 344–58. http://dx.doi.org/10.1044/2016_aja-15-0080.

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PurposeSpeech-in-noise testing relies on a number of factors beyond the auditory system, such as cognitive function, compliance, and motor function. It may be possible to avoid these limitations by using electroencephalography. The present study explored this possibility using the N400.MethodEleven adults with typical hearing heard high-constraint sentences with congruent and incongruent terminal words in the presence of speech-shaped noise. Participants ignored all auditory stimulation and watched a video. The signal-to-noise ratio (SNR) was varied around each participant's behavioral threshold during electroencephalography recording. Speech was also heard in quiet.ResultsThe amplitude of the N400 effect exhibited a nonlinear relationship with SNR. In the presence of background noise, amplitude decreased from high (+4 dB) to low (+1 dB) SNR but increased dramatically at threshold before decreasing again at subthreshold SNR (−2 dB).ConclusionsThe SNR of speech in noise modulates the amplitude of the N400 effect to semantic anomalies in a nonlinear fashion. These results are the first to demonstrate modulation of the passively evoked N400 by SNR in speech-shaped noise and represent a first step toward the end goal of developing an N400-based physiological metric for speech-in-noise testing.

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Alyan, Emad, Naufal M. Saad, Nidal Kamel, Mohd Zuki Yusoff, Mohd Azman Zakariya, Mohammad Abdul Rahman, Christophe Guillet, and Frederic Merienne. "Frontal Electroencephalogram Alpha Asymmetry during Mental Stress Related to Workplace Noise." Sensors 21, no. 6 (March 11, 2021): 1968. http://dx.doi.org/10.3390/s21061968.

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This study aims to investigate the effects of workplace noise on neural activity and alpha asymmetries of the prefrontal cortex (PFC) during mental stress conditions. Workplace noise exposure is a pervasive environmental pollutant and is negatively linked to cognitive effects and selective attention. Generally, the stress theory is assumed to underlie the impact of noise on health. Evidence for the impacts of workplace noise on mental stress is lacking. Fifteen healthy volunteer subjects performed the Montreal imaging stress task in quiet and noisy workplaces while their brain activity was recorded using electroencephalography. The salivary alpha-amylase (sAA) was measured before and immediately after each tested workplace to evaluate the stress level. The results showed a decrease in alpha rhythms, or an increase in cortical activity, of the PFC for all participants at the noisy workplace. Further analysis of alpha asymmetry revealed a greater significant relative right frontal activation of the noisy workplace group at electrode pairs F4-F3 but not F8-F7. Furthermore, a significant increase in sAA activity was observed in all participants at the noisy workplace, demonstrating the presence of stress. The findings provide critical information on the effects of workplace noise-related stress that might be neglected during mental stress evaluations.

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Alkhorshid, Daniel Rostami, Seyyedeh Fatemeh Molaeezadeh, and Mikaeil Rostami Alkhorshid. "Analysis: Electroencephalography Acquisition System: Analog Design." Biomedical Instrumentation & Technology 54, no. 5 (September 1, 2020): 346–51. http://dx.doi.org/10.2345/0899-8205-54.5.346.

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Abstract Electroencephalography (EEG) is a sensitive and weak biosignal that varies from person to person. It is easily affected by noise and artifacts. Hence, maintaining the signal integrity to design an EEG acquisition system is crucial. This article proposes an analog design for acquiring EEG signals. The proposed design consists of eight blocks: (1) a radio-frequency interference filter and electro-static discharge protection, (2) a preamplifier and second-order high-pass filter with feedback topology and an unblocking mechanism, (3) a driven right leg circuit, (4) two-stage main and variable amplifiers, (5) an eight-order anti-aliasing filter, (6) a six-order 50-Hz notch filter (optional), (7) an opto-isolator circuit, and (8) an isolated power supply. The maximum gain of the design is approximately 94 dB, and its bandwidth ranges from approximately 0.18 to 120 Hz. The depth of the 50-Hz notch filter is −35 dB. Using this filter is optional because it causes EEG integrity problems in frequencies ranging from 40 to 60 Hz.

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Frescura, Alessia, Pyoung Jik Lee, Jeong-Ho Jeong, and Yoshiharu Soeta. "Electroencephalogram (EEG) responses to indoor sound sources in wooden residential buildings." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 263, no. 4 (August 1, 2021): 1989–98. http://dx.doi.org/10.3397/in-2021-2021.

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The present study aimed to explore relationships between physiological and subjective responses to indoor sounds. Specifically, The electroencephalograms (EEG) responses to neighbour sounds in wooden dwellings were investigated. Listening tests were performed to collect EEG data in distinct acoustics scenarios. Experimental work was carried out in a laboratory with a low background noise level. A series of impact and airborne sounds were presented through loudspeakers and subwoofer, while participants sat comfortably in the simulated living room wearing the EEG headset (B-alert X24 system). The impact sound sources were an adult walking and a child running recorded in a laboratory equipped with different floor configurations. Two airborne sounds (a live conversation and a piece of classical piano music) were digitally filtered to resemble good and poor sound insulation performances of vertical partitions. The experiment consisted of two sessions, namely, the evaluation of individual sounds and the evaluation of the combined noise sources. In the second session, pairs of an impact and an airborne sound were presented. During the listening test, electroencephalography alpha reactivity (α-EEG) and electroencephalography beta reactivity (β-EEG) were monitored. In addition, participants were asked to rate noise annoyance using an 11-point scale.

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Shim, Allison I., Bruce G. Berg, and Ramesh Srinivasan. "Auditory detection of amplitude modulation in psychophysical notched noise task and electroencephalography." Journal of the Acoustical Society of America 122, no. 5 (2007): 3064. http://dx.doi.org/10.1121/1.2942935.

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Choi, Jee Hyun, Klaus Peter Koch, Wigand Poppendieck, Mina Lee, and Hee-Sup Shin. "High Resolution Electroencephalography in Freely Moving Mice." Journal of Neurophysiology 104, no. 3 (September 2010): 1825–34. http://dx.doi.org/10.1152/jn.00188.2010.

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Electroencephalography (EEG) is a standard tool for monitoring brain states in humans. Understanding the molecular and cellular mechanisms underlying diverse EEG rhythms can be facilitated by using mouse models under molecular, pharmacological, or electrophysiological manipulations. The small size of the mouse brain, however, poses a severe limitation in the spatial information of EEG. To overcome this limitation, we devised a polyimide based microelectrode array (PBM array) with nanofabrication technologies. The microelectrode contains 32 electrodes, weighs 150 mg, and yields noise-insensitive signals when applied on the mouse skull. The high-density microelectrode allowed both global and focused mapping of high resolution EEG (HR-EEG) in the mouse brain. Mapping and dynamical analysis tools also have been developed to visualize the dynamical changes of spatially resolved mouse EEG. We demonstrated the validity and utility of mouse EEG in localization of the seizure onset in absence seizure model and phase dynamics of abnormal theta rhythm in transgenic mice. Dynamic tracking of the EEG map in genetically modified mice under freely moving conditions should allow study of the molecular and cellular mechanisms underlying the generation and dynamics of diverse EEG rhythms.

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Goldenholz, Daniel M., Seppo P. Ahlfors, Matti S. Hämäläinen, Dahlia Sharon, Mamiko Ishitobi, Lucia M. Vaina, and Steven M. Stufflebeam. "Mapping the signal-to-noise-ratios of cortical sources in magnetoencephalography and electroencephalography." Human Brain Mapping 30, no. 4 (April 2009): 1077–86. http://dx.doi.org/10.1002/hbm.20571.

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Nagal, Rachana, Pradeep Kumar, and Poonam Bansal. "AN OPTIMAL APPROACH FOR EEG/ERP NOISE CANCELLATION USING ADAPTIVE FILTER WITH OPPOSITIONAL WHALE OPTIMIZATION ALGORITHM." Biomedical Engineering: Applications, Basis and Communications 31, no. 05 (September 9, 2019): 1950035. http://dx.doi.org/10.4015/s1016237219500352.

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In this paper, the Oppositional Whale Optimization Algorithm (OWOA) is applied to Adaptive Noise Canceller (ANC) for the filtering of Electroencephalography/Event-Related Potentials (EEG/ERP) signals. Performance of ANC will be improved by calculating the optimal weight value and proposed OWOA technique is used to update weight value. Adaptive filter’s noise reduction capability has been tested through consideration of White Gaussian Noise (WGN) over contaminated EEG signals at various SNR levels ([Formula: see text]10[Formula: see text]dB, [Formula: see text]15[Formula: see text]dB and [Formula: see text]20[Formula: see text]dB). The performance of the proposed OWOA algorithm is assessed in terms of Signal to Noise Ratio (SNR) in dB, mean value, and the correlation between resultant and input ERP. In this work, ANCs are also implemented by utilizing conventional gradient-based techniques like Recursive Least Square (RLS), Least Mean Square (LMS) and other optimization algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and WOA techniques. In average cases of noisy environment, comparative analysis shows that the proposed OWOA technique provides higher SNR value and significantly lower mean, and correlation as compared to gradient-based and swarm-based techniques. The comparative results show that extracting the desired EEG component is more effective in the proposed OWOA method. So, it has seen that OWOA-based noise reduction technique removing the artifacts and improving the quality of EEG signals significantly for biomedical analysis.

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Tseng, Li Ho, Ming Tai Cheng, Shih Tsung Chen, Jyi Faa Hwang, Chia Ju Chen, and Chia Yi Chou. "An EEG Investigation of the Impact of Noise on Attention." Advanced Materials Research 779-780 (September 2013): 1731–36. http://dx.doi.org/10.4028/www.scientific.net/amr.779-780.1731.

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During the past two decades, most researchers employed a questionnaire to characterize the effect of noise on psychosomatic responses. Developments in physiological techniques offer a non-invasive method for recording brain activity with electroencephalography (EEG). This method for assessing the impact of noise on attention is growing in popularity. The aim of this study was to investigate brain activity changes in response to noise exposure during attention-demanding tasks by using EEG power and phase coherence estimation. We hypothesized that brain rhythms could be affected by environmental stimuli and would be reflected in the EEG power and phase coherence. Nineteen healthy right-handed university students (mean age = 21.5 ± 2.0 years) participated in this study. The experiment comprised recording EEG data for participants in the following steps: rest with eyes closed (< 50 dBA), rest with eyes open, listening in a noisy environment (85 dBA), performance on an attention-demanding task in a quiet environment (< 50 dBA), and performance on an attention-demanding task in a noisy environment (85 dBA). Significant differences were observed between stages, and the participants performed more effectively in the quiet environment, where they showed higher rates of correct responses (p <.05). From the assessment of the EEG power and phase coherence estimation, the study demonstrated the following: (1) Alpha-2 (10-13 Hz) power and phase coherence decreased when participants shifted from closed eyes to open eyes, while theta power increased. (2) In contrast, during the noise exposure phase, whether during an attention-demanding task or not, beta (13-30 Hz) phase coherence decreased in the brain, but theta phase coherence was not affected compared to the results in the quiet environment. We suggest that the high frequency of neural synchronization is relevant for cognitive performance, and that participants at risk for selective attention are affected by noise exposure.

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Ke, Jinjing, Ming Zhang, Xiaowei Luo, and Jiayu Chen. "Monitoring distraction of construction workers caused by noise using a wearable Electroencephalography (EEG) device." Automation in Construction 125 (May 2021): 103598. http://dx.doi.org/10.1016/j.autcon.2021.103598.

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Ke, Jinjing, Jing Du, and Xiaowei Luo. "The effect of noise content and level on cognitive performance measured by electroencephalography (EEG)." Automation in Construction 130 (October 2021): 103836. http://dx.doi.org/10.1016/j.autcon.2021.103836.

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Ku, Yixuan, and Martine R. van Schouwenburg. "Explaining attention-related changes in behavior and electroencephalography data through computational modeling." Journal of Neurophysiology 114, no. 4 (October 2015): 2087–89. http://dx.doi.org/10.1152/jn.01026.2014.

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In a recent article, Itthipuripat and colleagues combined psychophysics, neurophysiology, and mathematical modeling to investigate the neural mechanism underlying behavioral benefits of spatial attention (Itthipuripat S, Ester EF, Deering S, Serences JT. J Neurosci 34: 13384–13398, 2014). They found that attention-related effects on behavior as well as neural signals could be better explained by a response gain model than by a noise reduction model or an efficient read-out model. In this Neuro Forum we discuss these results and raise several interesting questions and potential interpretations.

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Nagulapalli, R., K. Hayatleh, S. Barker, A. A. Tammam, N. Yassine, B. Yassine, and M. Ben-Esmael. "A Low Noise Amplifier Suitable for Biomedical Recording Analog Front-End in 65nm CMOS Technology." Journal of Circuits, Systems and Computers 28, no. 08 (July 2019): 1950137. http://dx.doi.org/10.1142/s0218126619501378.

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This paper presents a fully integrated front-end, low noise amplifier (LNA), dedicated to the processing of various types of bio-medical signals, such as Electrocardiogram (ECG), Electroencephalography (EEG), Axon Action Potential (AAP). A novel noise reduction technique, for an operational transconductance amplifier (OTA), has been proposed. This adds a current steering branch parallel to the differential pair, with a view to reducing the noise contribution by the cascode current sources. Hence, this reduces the overall input-referred noise of the LNA, without adding any additional power. The proposed technique implemented in 65[Formula: see text]nm CMOS technology achieves 30 dB closed-loop voltage gain, 0.05[Formula: see text]Hz lower cut-off frequency and 100 MHz 3-dB bandwidth. It operates at 1.2[Formula: see text]V power supply and draws 1[Formula: see text][Formula: see text]A static current. The prototype described in this paper occupies 3300[Formula: see text][Formula: see text]m2 silicon area.

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Kwon, Moonyoung, Sangjun Han, Kiwoong Kim, and Sung Chan Jun. "Super-Resolution for Improving EEG Spatial Resolution using Deep Convolutional Neural Network—Feasibility Study." Sensors 19, no. 23 (December 3, 2019): 5317. http://dx.doi.org/10.3390/s19235317.

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Electroencephalography (EEG) has relatively poor spatial resolution and may yield incorrect brain dynamics and distort topography; thus, high-density EEG systems are necessary for better analysis. Conventional methods have been proposed to solve these problems, however, they depend on parameters or brain models that are not simple to address. Therefore, new approaches are necessary to enhance EEG spatial resolution while maintaining its data properties. In this work, we investigated the super-resolution (SR) technique using deep convolutional neural networks (CNN) with simulated EEG data with white Gaussian and real brain noises, and experimental EEG data obtained during an auditory evoked potential task. SR EEG simulated data with white Gaussian noise or brain noise demonstrated a lower mean squared error and higher correlations with sensor information, and detected sources even more clearly than did low resolution (LR) EEG. In addition, experimental SR data also demonstrated far smaller errors for N1 and P2 components, and yielded reasonable localized sources, while LR data did not. We verified our proposed approach’s feasibility and efficacy, and conclude that it may be possible to explore various brain dynamics even with a small number of sensors.

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Kumar, R. Suresh, and P. Manimegalai. "Detection and Separation of Eeg Artifacts Using Wavelet Transform." International Journal of Informatics and Communication Technology (IJ-ICT) 7, no. 3 (December 1, 2018): 149. http://dx.doi.org/10.11591/ijict.v7i3.pp149-156.

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Bio-medical signal processing is one of the most important techniques of multichannel sensor network and it has a substantial concentration in medical application. However, the real-time and recorded signals in multisensory instruments contains different and huge amount of noise, and great work has been completed in developing most favorable structures for estimating the signal source from the noisy signal in multichannel observations. Methods have been developed to obtain the optimal linear estimation of the output signal through the Wide-Sense-Stationary (WSS) process with the help of time-invariant filters. In this process, the input signal and the noise signal are assumed to achieve the linear output signal. During the process, the non-stationary signals arise in the bio-medical signal processing in addition to it there is no effective structure to deal with them. Wavelets transform has been proved to be the efficient tool for handling the non-stationary signals, but wavelet provide any possible way to approach multichannel signal processing. Based on the basic structure of linear estimation of non-stationary multichannel data and statistical models of spatial signal coherence acquire through the wavelet transform in multichannel estimation. The above methods can be used for Electroencephalography (EEG) signal denoising through the original signal and then implement the noise reduction technique to evaluate their performance such as SNR, MSE and computation time.

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Daud, Syarifah Noor Syakiylla Sayed, and Rubita Sudirman. "Evaluating the Effect of Mozart Music and White Noise on Electroencephalography Pattern toward Visual Memory." Advances in Science, Technology and Engineering Systems Journal 2, no. 3 (August 2017): 1372–80. http://dx.doi.org/10.25046/aj0203173.

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Waterstraat, Gunnar, Rainer Körber, Jan-Hendrik Storm, and Gabriel Curio. "Noninvasive neuromagnetic single-trial analysis of human neocortical population spikes." Proceedings of the National Academy of Sciences 118, no. 11 (March 11, 2021): e2017401118. http://dx.doi.org/10.1073/pnas.2017401118.

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Neuronal spiking is commonly recorded by invasive sharp microelectrodes, whereas standard noninvasive macroapproaches (e.g., electroencephalography [EEG] and magnetoencephalography [MEG]) predominantly represent mass postsynaptic potentials. A notable exception are low-amplitude high-frequency (∼600 Hz) somatosensory EEG/MEG responses that can represent population spikes when averaged over hundreds of trials to raise the signal-to-noise ratio. Here, a recent leap in MEG technology—featuring a factor 10 reduction in white noise level compared with standard systems—is leveraged to establish an effective single-trial portrayal of evoked cortical population spike bursts in healthy human subjects. This time-resolved approach proved instrumental in revealing a significant trial-to-trial variability of burst amplitudes as well as time-correlated (∼10 s) fluctuations of burst response latencies. Thus, ultralow-noise MEG enables noninvasive single-trial analyses of human cortical population spikes concurrent with low-frequency mass postsynaptic activity and thereby could comprehensively characterize cortical processing, potentially also in diseases not amenable to invasive microelectrode recordings.

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Ko, Li-Wei, Rupesh Kumar Chikara, Po-Yin Chen, Ying-Chun Jheng, Chien-Chih Wang, Yi-Chiang Yang, Lieber Po-Hung Li, Kwong-Kum Liao, Li-Wei Chou, and Chung-Lan Kao. "Noisy Galvanic Vestibular Stimulation (Stochastic Resonance) Changes Electroencephalography Activities and Postural Control in Patients with Bilateral Vestibular Hypofunction." Brain Sciences 10, no. 10 (October 15, 2020): 740. http://dx.doi.org/10.3390/brainsci10100740.

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Patients with bilateral vestibular hypofunction (BVH) often suffer from imbalance, gait problems, and oscillopsia. Noisy galvanic vestibular stimulation (GVS), a technique that non-invasively stimulates the vestibular afferents, has been shown to enhance postural and walking stability. However, no study has investigated how it affects stability and neural activities while standing and walking with a 2 Hz head yaw turning. Herein, we investigated this issue by comparing differences in neural activities during standing and walking with a 2 Hz head turning, before and after noisy GVS. We applied zero-mean gaussian white noise signal stimulations in the mastoid processes of 10 healthy individuals and seven patients with BVH, and simultaneously recorded electroencephalography (EEG) signals with 32 channels. We analyzed the root mean square (RMS) of the center of pressure (COP) sway during 30 s of standing, utilizing AMTI force plates (Advanced Mechanical Technology Inc., Watertown, MA, USA). Head rotation quality when walking with a 2 Hz head yaw, with and without GVS, was analyzed using a VICON system (Vicon Motion Systems Ltd., Oxford, UK) to evaluate GVS effects on static and dynamic postural control. The RMS of COP sway was significantly reduced during GVS while standing, for both patients and healthy subjects. During walking, 2 Hz head yaw movements was significantly improved by noisy GVS in both groups. Accordingly, the EEG power of theta, alpha, beta, and gamma bands significantly increased in the left parietal lobe after noisy GVS during walking and standing in both groups. GVS post-stimulation effect changed EEG activities in the left and right precentral gyrus, and the right parietal lobe. After stimulation, EEG activity changes were greater in healthy subjects than in patients. Our findings reveal noisy GVS as a non-invasive therapeutic alternative to improve postural stability in patients with BVH. This novel approach provides insight to clinicians and researchers on brain activities during noisy GVS in standing and walking conditions in both healthy and BVH patients.

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Kaur, Chamandeep, Preeti Singh, and Sukhtej Sahni. "Electroencephalography-Based Source Localization for Depression Using Standardized Low Resolution Brain Electromagnetic Tomography – Variational Mode Decomposition Technique." European Neurology 81, no. 1-2 (2019): 63–75. http://dx.doi.org/10.1159/000500414.

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Background: Electroencephalography (EEG) may be used as an objective diagnosis tool for diagnosing various disorders. Recently, source localization from EEG is being used in the analysis of real-time brain monitoring applications. However, inverse problem reduces the accuracy in EEG signal processing systems. Objectives: This paper presents a new method of EEG source localization using variational mode decomposition (VMD) and standardized the low resolution brain electromagnetic tomography (sLORETA) inverse model. The focus is to compare the effectiveness of the proposed approach for EEG signals of depression patients. Method: As the first stage, real EEG recordings corresponding to depression patients are decomposed into various mode functions by applying VMD. Then, closely related functions are analyzed using the inverse modelling-based source localization procedures such as sLORETA. Simulations have been carried out on real EEG databases for depression to demonstrate the effectiveness of the proposed techniques. Results: The performance of the algorithm has been assessed using localization error (LE), mean square error and signal to noise ratio output corresponding to simulated EEG dipole sources and real EEG signals for depression. In order to study the spatial resolution for cortical potential distribution, the main focus has been on studying the effects of noise sources and estimating LE of inverse solutions. More accurate and robust localization results show that this methodology is very promising for EEG source localization of depression signals. Conclusion: It can be said that proposed algorithm efficiently suppresses the influence of noise in the EEG inverse problem using simulated EEG activity and EEG database for depression. Such a system may offer an effective solution for clinicians as a crucial stage of EEG pre-processing in automated depression detection systems and may prevent delay in diagnosis.

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Barja, Ameen Omar. "Alternative Form of Ordinary Differential Equation of Electroencephalography Signals During an Epileptic Seizure." Malaysian Journal of Fundamental and Applied Sciences 17, no. 2 (April 29, 2021): 109–13. http://dx.doi.org/10.11113/mjfas.v17n2.1976.

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One of the most important fields in clinical neurophysiology is an electroencephalogram (EEG). It is a test used to detect problems related to the brain electrical activity, and it can track and records patterns of brain waves. EEG continues to play an essential role in diagnosis and management of patients with epileptic seizure disorders. Nevertheless, the outcome of EEG as a tool for evaluating epileptic seizure is often interpreted as a noise rather than an ordered pattern. The mathematical modelling of EEG signals provides valuable data to neurologists, and is heavily utilized in the diagnosis and treatment of epilepsy. EEG signals during the seizure can be modeled as ordinary differential equation (ODE). In this study we will present an alternative form of ODE of EEG signals through the seizure.

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Haumann, Niels Trusbak, Lauri Parkkonen, Marina Kliuchko, Peter Vuust, and Elvira Brattico. "Comparing the Performance of Popular MEG/EEG Artifact Correction Methods in an Evoked-Response Study." Computational Intelligence and Neuroscience 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/7489108.

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We here compared results achieved by applying popular methods for reducing artifacts in magnetoencephalography (MEG) and electroencephalography (EEG) recordings of the auditory evoked Mismatch Negativity (MMN) responses in healthy adult subjects. We compared the Signal Space Separation (SSS) and temporal SSS (tSSS) methods for reducing noise from external and nearby sources. Our results showed that tSSS reduces the interference level more reliably than plain SSS, particularly for MEG gradiometers, also for healthy subjects not wearing strongly interfering magnetic material. Therefore, tSSS is recommended over SSS. Furthermore, we found that better artifact correction is achieved by applying Independent Component Analysis (ICA) in comparison to Signal Space Projection (SSP). Although SSP reduces the baseline noise level more than ICA, SSP also significantly reduces the signal—slightly more than it reduces the artifacts interfering with the signal. However, ICA also adds noise, or correction errors, to the waveform when the signal-to-noise ratio (SNR) in the original data is relatively low—in particular to EEG and to MEG magnetometer data. In conclusion, ICA is recommended over SSP, but one should be careful when applying ICA to reduce artifacts on neurophysiological data with relatively low SNR.

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Chen, Zhongye, Yijun Wang, and Zhongyan Song. "Classification of Motor Imagery Electroencephalography Signals Based on Image Processing Method." Sensors 21, no. 14 (July 7, 2021): 4646. http://dx.doi.org/10.3390/s21144646.

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In recent years, more and more frameworks have been applied to brain-computer interface technology, and electroencephalogram-based motor imagery (MI-EEG) is developing rapidly. However, it is still a challenge to improve the accuracy of MI-EEG classification. A deep learning framework termed IS-CBAM-convolutional neural network (CNN) is proposed to address the non-stationary nature, the temporal localization of excitation occurrence, and the frequency band distribution characteristics of the MI-EEG signal in this paper. First, according to the logically symmetrical relationship between the C3 and C4 channels, the result of the time-frequency image subtraction (IS) for the MI-EEG signal is used as the input of the classifier. It both reduces the redundancy and increases the feature differences of the input data. Second, the attention module is added to the classifier. A convolutional neural network is built as the base classifier, and information on the temporal location and frequency distribution of MI-EEG signal occurrences are adaptively extracted by introducing the Convolutional Block Attention Module (CBAM). This approach reduces irrelevant noise interference while increasing the robustness of the pattern. The performance of the framework was evaluated on BCI competition IV dataset 2b, where the mean accuracy reached 79.6%, and the average kappa value reached 0.592. The experimental results validate the feasibility of the framework and show the performance improvement of MI-EEG signal classification.

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Manikandan, N., S. Muruganand, and K. Karuppasamy. "Design and Implement of High Gain and Low Noise Neural Amplifier Using Compensation Techniques." International Journal of Reconfigurable and Embedded Systems (IJRES) 8, no. 2 (July 1, 2019): 124. http://dx.doi.org/10.11591/ijres.v8.i2.pp124-129.

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<em>Electroencephalography is refer to record the electrical signal with respect to brain activity and its reliable EEG information, using this to diagnosis disorder and tumors. However the signal is very difficult to capture and processing due to so many parameter. Mainly this signal is very low range that from 0.1 to 100μv in and its bandwidth range from 1Hz to 100 Hz. So the signal has amplified by using linear and accurate digital program amplifier(PGA).This amplifier has been designed by using First stage amplifier with gain of 120dB with low output noise. The PGA is consists of OPAMPs the PGA change from 10db to 120dB.Inorde to optimized the linear and gain accuracy a new structure resister array is proposed high gain PGA. Hence the simulated result has shown it is promising to exhibit an amplifier with high performance biomedical application.</em>

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Akiyama, Akiyoshi, Jeng-Dau Tsai, Emily W. Y. Tam, Daphne Kamino, Cecil Hahn, Cristina Y. Go, Vann Chau, et al. "The Effect of Music and White Noise on Electroencephalographic (EEG) Functional Connectivity in Neonates in the Neonatal Intensive Care Unit." Journal of Child Neurology 36, no. 1 (August 24, 2020): 38–47. http://dx.doi.org/10.1177/0883073820947894.

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The purpose of this study is to investigate whether listening to music and white noise affects functional connectivity on scalp electroencephalography (EEG) in neonates in the neonatal intensive care unit. Nine neonates of ≥34 weeks’ gestational age, who were already undergoing clinical continuous EEG monitoring in the neonatal intensive care unit, listened to lullaby-like music and white noise for 1 hour each separated by a 2-hour interval of no intervention. EEG segments during periods of music, white noise, and no intervention were band-pass filtered as delta (0.5-4 Hz), theta (4-8 Hz), lower alpha (8-10 Hz), upper alpha (10-13 Hz), beta (13-30 Hz), and gamma (30-45 Hz). Synchronization likelihood was used as a measure of connectivity between any 2 electrodes. In theta, lower alpha, and upper alpha frequency bands, the synchronization likelihood values yielded statistical significance with sound (music, white noise and no intervention) and with edge (between any 2 electrodes) factors. In theta, lower alpha, and upper alpha frequency bands, statistical significance was obtained between music and white noise ( t = 3.12, 3.32, and 3.68, respectively; P < .017), and between white noise and no intervention ( t = 4.51, 3.09, and 2.95, respectively, P < .017). However, there was no difference between music and no intervention. Although limited by a small sample size and the 1-time only auditory intervention, these preliminary results demonstrate the feasibility of EEG connectivity analyses even at bedside in neonates on continuous EEG monitoring in the neonatal intensive care unit. They also point to the possibility of detecting significant changes in functional connectivity related to the theta and alpha bands using auditory interventions.

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Ferdous, Jannatul, Sujan Ali, Ekramul Hamid, and Khademul Islam Molla. "Sub-band selection approach to artifact suppression from electroencephalography signal using hybrid wavelet transform." International Journal of Advanced Robotic Systems 18, no. 1 (January 1, 2021): 172988142199226. http://dx.doi.org/10.1177/1729881421992269.

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This article presents a hybrid wavelet-based algorithm to suppress the ocular artifacts from electroencephalography (EEG) signals. The hybrid wavelet transform (HWT) method is designed by the combination of discrete wavelet decomposition and wavelet packet transform. The artifact suppression is performed by the selection of sub-bands obtained by HWT. Fractional Gaussian noise (fGn) is used as the reference signal to select the sub-bands containing the artifacts. The multichannel EEG signal is decomposed HWT into a finite set of sub-bands. The energies of the sub-bands are compared to that of the fGn to the desired sub-band signals. The EEG signal is reconstructed by the selected sub-bands consisting of EEG. The experiments are conducted for both simulated and real EEG signals to study the performance of the proposed algorithm. The results are compared with recently developed algorithms of artifact suppression. It is found that the proposed method performs better than the methods compared in terms of performance metrics and computational cost.

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Abdullah, Ahmed Kareem, Chao Zhu Zhang, and Si Yao Lian. "Separation of EOG Artifact and Power Line Noise-50Hz from EEG by Efficient Stone's BSS Algorithm." Applied Mechanics and Materials 543-547 (March 2014): 2687–91. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2687.

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An enhanced blind source separation algorithm based on Stone's BSS approach is proposed, to reject the Electrooculogram (EOG) artifact and power line noise (50Hz) from simulated and real human Electroencephalography (EEG) signals without the notch filter, in order not to lose any useful EEG data around the 50-Hz. The proposed algorithm which called efficient Stones BSS (ESBSS) has been compared with four well-known BSS algorithms over super-Gaussian, sub-Gaussian artifacts and EEG signals with a linear mixture. In Original Stones BSS, the half-life values taken as a constant, typically (hlong≥100 hShort), but in the proposed work, an optimization procedure is used to change these values until the maximum temporal predictability is found. The real EEG data are taken from Imperial College London using a computerized EEG device with eight electrodes placed according to the 10-20 system.

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Abbasi, Ali Mohammad, Majid Motamedzade, Mohsen Aliabadi, Rostam Golmohammadi, and Leila Tapak. "Study of the physiological and mental health effects caused by exposure to low-frequency noise in a simulated control room." Building Acoustics 25, no. 3 (June 8, 2018): 233–48. http://dx.doi.org/10.1177/1351010x18779518.

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The purpose of this study was to investigate the physiological and mental health effects caused by exposure to low-frequency noise in typical control rooms and office-like areas. The participants were 35 male students who were exposed to noise at levels of 55, 65, 70, and 75 dBA. The N-back test was used at three cognitive performance loads (low workload ( n = 1), medium workload ( n = 2), and high workload ( n = 3) to evaluate working memory simultaneously in an air conditioning chamber in four sessions with a constant level. The electroencephalography, electrocardiogram, and electrooculography were measured using Nexus 4 by Bio traces software (Mind Media Co.). For evaluation of mental fatigue, fatigue visual analog scale, and psycho-physiological indices were also used. The results showed that the losses of physiological and mental health were rapidly increased with exposure to noise levels of 65–75 dBA. The results showed that mental fatigue significantly affected heart rate, low- to high-frequency ratios, and electroencephalogram indices such as theta, alpha, as well as eye activities and working memory. The findings confirmed that the mental fatigue caused by low-frequency noise significantly impacted the employees’ psycho-physiological and working memory responses. Implementation of the effective interventions to overcome employees’ mental fatigue in typical control rooms and office-like areas can improve the health and acoustic comfort and, consequently, the cognitive performance.

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Nguyen, Hai Thanh, Toi Van Vo, and Trung Van Nguyen. "Control of electric wheelchair by eye activities using eeg technique." Science and Technology Development Journal 16, no. 4 (September 30, 2013): 18–28. http://dx.doi.org/10.32508/stdj.v16i4.1608.

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This paper presents a study control of an electric wheelchair based on ElectroEncephaloGraphy (EEG). The directions of wheelchair are controlled by eye activities. A mean threshold algorithm is proposed to detect eye activities using EEG technique. The activities of eyes such as blinking two eyes, glanced left and glanced right related to the delta area of human brain are investigated. Before analyzing the EEG data, original data are filtered to reduce noise or artifacts by a band-pass filter. The proposed threshold method is applied to distinguish the phenomenon of eye activities. This study is useful for creating a BCI system such as wheelchair control. Experimental results show that the proposed threshold approach is the effectiveness.

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Rashid, Usman, Imran Niazi, Nada Signal, and Denise Taylor. "An EEG Experimental Study Evaluating the Performance of Texas Instruments ADS1299." Sensors 18, no. 11 (November 1, 2018): 3721. http://dx.doi.org/10.3390/s18113721.

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Texas Instruments ADS1299 is an attractive choice for low cost electroencephalography (EEG) devices owing to its low power consumption and low input referred noise. To date, there have been no rigorous evaluations of its performance. In this EEG experimental study we evaluated the performance of the ADS1299 against a high quality laboratory-based system. Two self-paced lower limb motor tasks were performed by 22 healthy participants. Recorded power across delta, theta, alpha, and beta EEG bands, the power ratio across the motor tasks, pre-movement noise, and signal-to-noise ratio were obtained for evaluation. The amplitude and time of the negative peak in the movement-related cortical potentials (MRCPs) extracted from the EEG data were also obtained. Using linear mixed models, no statistically significant differences (p > 0.05) were found in any of these measures across the two systems. These findings were further supported by evaluation of cosine similarity, waveform differences, and topographic maps. There were statistically significant differences in MRCPs across the motor tasks in both systems. We conclude that the performance of the ADS1299 in combination with wet Ag/AgCl electrodes is analogous to that of a laboratory-based system in a low frequency (<40 Hz) EEG recording.

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Kyong, Jeong-Sug, Chanbeom Kwak, Woojae Han, Myung-Whan Suh, and Jinsook Kim. "Effect of Speech Degradation and Listening Effort in Reverberating and Noisy Environments Given N400 Responses." Journal of Audiology and Otology 24, no. 3 (July 10, 2020): 119–26. http://dx.doi.org/10.7874/jao.2019.00514.

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Background and Objectives: In distracting listening conditions, individuals need to pay extra attention to selectively listen to the target sounds. To investigate the amount of listening effort required in reverberating and noisy backgrounds, a semantic mismatch was examined.Subjects and Methods: Electroencephalography was performed in 18 voluntary healthy participants using a 64-channel system to obtain N400 latencies. They were asked to listen to sounds and see letters in 2 reverberated×2 noisy paradigms (i.e., Q-0 ms, Q-2000 ms, 3 dB-0 ms, and 3 dB-2000 ms). With auditory-visual pairings, the participants were required to answer whether the auditory primes and letter targets did or did not match. Results: Q-0 ms revealed the shortest N400 latency, whereas the latency was significantly increased at 3 dB-2000 ms. Further, Q-2000 ms showed approximately a 47 ms delayed latency compared to 3 dB-0 ms. Interestingly, the presence of reverberation significantly increased N400 latencies. Under the distracting conditions, both noise and reverberation involved stronger frontal activation. Conclusions: The current distracting listening conditions could interrupt the semantic mismatch processing in the brain. The presence of reverberation, specifically a 2000 ms delay, necessitates additional mental effort, as evidenced in the delayed N400 latency and the involvement of the frontal sources in this study.

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Tseghai, Granch Berhe, Benny Malengier, Kinde Anlay Fante, and Lieva Van Langenhove. "A Long-Lasting Textile-Based Anatomically Realistic Head Phantom for Validation of EEG Electrodes." Sensors 21, no. 14 (July 7, 2021): 4658. http://dx.doi.org/10.3390/s21144658.

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During the development of new electroencephalography electrodes, it is important to surpass the validation process. However, maintaining the human mind in a constant state is impossible which in turn makes the validation process very difficult. Besides, it is also extremely difficult to identify noise and signals as the input signals are not known. For that reason, many researchers have developed head phantoms predominantly from ballistic gelatin. Gelatin-based material can be used in phantom applications, but unfortunately, this type of phantom has a short lifespan and is relatively heavyweight. Therefore, this article explores a long-lasting and lightweight (−91.17%) textile-based anatomically realistic head phantom that provides comparable functional performance to a gelatin-based head phantom. The result proved that the textile-based head phantom can accurately mimic body-electrode frequency responses which make it suitable for the controlled validation of new electrodes. The signal-to-noise ratio (SNR) of the textile-based head phantom was found to be significantly better than the ballistic gelatin-based head providing a 15.95 dB ± 1.666 (±10.45%) SNR at a 95% confidence interval.

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Choi, Jong-Ho, Min-Hyuk Kim, Luan Feng, Chany Lee, and Hyun-Kyo Jung. "A New Weighted Correlation Coefficient Method to Evaluate Reconstructed Brain Electrical Sources." Journal of Applied Mathematics 2012 (2012): 1–11. http://dx.doi.org/10.1155/2012/251295.

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Various inverse algorithms have been proposed to estimate brain electrical activities with magnetoencephalography (MEG) and electroencephalography (EEG). To validate and compare the performances of inverse algorithms, many researchers have used artificially constructed EEG and MEG datasets. When the artificial sources are reconstructed on the cortical surface, accuracy of the source estimates has been difficult to evaluate. In this paper, we suggest a new measure to evaluate the reconstructed EEG/MEG cortical sources more accurately. To validate the usefulness of the proposed method, comparison between conventional and proposed evaluation metrics was conducted using artificial cortical sources simulated under different noise conditions. The simulation results demonstrated that only the proposed method could reflect the source space geometry regardless of the number of source peaks.

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Schembri, Patrick, Maruisz Pelc, and Jixin Ma. "The Effect That Auditory Distractions Have on a Visual P300 Speller While Utilizing Low-Cost Off-the-Shelf Equipment." Computers 9, no. 3 (August 27, 2020): 68. http://dx.doi.org/10.3390/computers9030068.

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This paper investigates the effect that selected auditory distractions have on the signal of a visual P300 Speller in terms of accuracy, amplitude, latency, user preference, signal morphology, and overall signal quality. In addition, it ensues the development of a hierarchical taxonomy aimed at categorizing distractions in the P300b domain and the effect thereof. This work is part of a larger electroencephalography based project and is based on the P300 speller brain–computer interface (oddball) paradigm and the xDAWN algorithm, with eight to ten healthy subjects, using a non-invasive brain–computer interface based on low-fidelity electroencephalographic (EEG) equipment. Our results suggest that the accuracy was best for the lab condition (LC) at 100%, followed by music at 90% (M90) at 98%, trailed by music at 30% (M30) and music at 60% (M60) equally at 96%, and shadowed by ambient noise (AN) at 92.5%, passive talking (PT) at 90%, and finally by active listening (AL) at 87.5%. The subjects’ preference prodigiously shows that the preferred condition was LC as originally expected, followed by M90, M60, AN, M30, AL, and PT. Statistical analysis between all independent variables shows that we accept our null hypothesis for both the amplitude and latency. This work includes data and comparisons from our previous papers. These additional results should give some insight into the practicability of the aforementioned P300 speller methodology and equipment to be used for real-world applications.

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Liao, Hongpeng, Jianwu Xu, and Zhuliang Yu. "Novel Convolutional Neural Network with Variational Information Bottleneck for P300 Detection." Entropy 23, no. 1 (December 29, 2020): 39. http://dx.doi.org/10.3390/e23010039.

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In the area of brain-computer interfaces (BCI), the detection of P300 is a very important technique and has a lot of applications. Although this problem has been studied for decades, it is still a tough problem in electroencephalography (EEG) signal processing owing to its high dimension features and low signal-to-noise ratio (SNR). Recently, neural networks, like conventional neural networks (CNN), has shown excellent performance on many applications. However, standard convolutional neural networks suffer from performance degradation on dealing with noisy data or data with too many redundant information. In this paper, we proposed a novel convolutional neural network with variational information bottleneck for P300 detection. Wiht the CNN architecture and information bottleneck, the proposed network termed P300-VIB-Net could remove the redundant information in data effectively. The experimental results on BCI competition data sets show that P300-VIB-Net achieves cutting-edge character recognition performance. Furthermore, the proposed model is capable of restricting the flow of irrelevant information adaptively in the network from perspective of information theory. The experimental results show that P300-VIB-Net is a promising tool for P300 detection.

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Rieiro, Héctor, Carolina Diaz-Piedra, José Miguel Morales, Andrés Catena, Samuel Romero, Joaquin Roca-Gonzalez, Luis J. Fuentes, and Leandro L. Di Stasi. "Validation of Electroencephalographic Recordings Obtained with a Consumer-Grade, Single Dry Electrode, Low-Cost Device: A Comparative Study." Sensors 19, no. 12 (June 23, 2019): 2808. http://dx.doi.org/10.3390/s19122808.

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The functional validity of the signal obtained with low-cost electroencephalography (EEG) devices is still under debate. Here, we have conducted an in-depth comparison of the EEG-recordings obtained with a medical-grade golden-cup electrodes ambulatory device, the SOMNOwatch + EEG-6, vs those obtained with a consumer-grade, single dry electrode low-cost device, the NeuroSky MindWave, one of the most affordable devices currently available. We recorded EEG signals at Fp1 using the two different devices simultaneously on 21 participants who underwent two experimental phases: a 12-minute resting state task (alternating two cycles of closed/open eyes periods), followed by 60-minute virtual-driving task. We evaluated the EEG recording quality by comparing the similarity between the temporal data series, their spectra, their signal-to-noise ratio, the reliability of EEG measurements (comparing the closed eyes periods), as well as their blink detection rate. We found substantial agreement between signals: whereas, qualitatively, the NeuroSky MindWave presented higher levels of noise and a biphasic shape of blinks, the similarity metric indicated that signals from both recording devices were significantly correlated. While the NeuroSky MindWave was less reliable, both devices had a similar blink detection rate. Overall, the NeuroSky MindWave is noise-limited, but provides stable recordings even through long periods of time. Furthermore, its data would be of adequate quality compared to that of conventional wet electrode EEG devices, except for a potential calibration error and spectral differences at low frequencies.

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Tuckute, Greta, Sofie Therese Hansen, Nicolai Pedersen, Dea Steenstrup, and Lars Kai Hansen. "Single-Trial Decoding of Scalp EEG under Natural Conditions." Computational Intelligence and Neuroscience 2019 (April 17, 2019): 1–11. http://dx.doi.org/10.1155/2019/9210785.

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There is significant current interest in decoding mental states from electroencephalography (EEG) recordings. EEG signals are subject-specific, are sensitive to disturbances, and have a low signal-to-noise ratio, which has been mitigated by the use of laboratory-grade EEG acquisition equipment under highly controlled conditions. In the present study, we investigate single-trial decoding of natural, complex stimuli based on scalp EEG acquired with a portable, 32 dry-electrode sensor system in a typical office setting. We probe generalizability by a leave-one-subject-out cross-validation approach. We demonstrate that support vector machine (SVM) classifiers trained on a relatively small set of denoised (averaged) pseudotrials perform on par with classifiers trained on a large set of noisy single-trial samples. We propose a novel method for computing sensitivity maps of EEG-based SVM classifiers for visualization of EEG signatures exploited by the SVM classifiers. Moreover, we apply an NPAIRS resampling framework for estimation of map uncertainty, and thus show that effect sizes of sensitivity maps for classifiers trained on small samples of denoised data and large samples of noisy data are similar. Finally, we demonstrate that the average pseudotrial classifier can successfully predict the class of single trials from withheld subjects, which allows for fast classifier training, parameter optimization, and unbiased performance evaluation in machine learning approaches for brain decoding.

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Anitha, T., N. Shanthi, R. Sathiyasheelan, G. Emayavaramban, and T. Rajendran. "Brain-Computer Interface for Persons with Motor Disabilities - A Review." Open Biomedical Engineering Journal 13, no. 1 (December 17, 2019): 127–33. http://dx.doi.org/10.2174/1874120701913010127.

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Aim /Objective: A Brain-Computer Interface (BCI) is a communication medium, which restructures brain signals into respective commands for an external device. Methodology: A BCI allows its target users like persons with motor disabilities to act on their environment using brain signals without using peripheral nerves or muscles. In this review article, we have presented a view on different BCIs for humans with motor disabilities. Results & Conclusion: From the study, it is clear that the P300 based Electroencephalography (EEG)BCIs with Steady-State Visually Evoked Potential (SSVEP) non-parametric feature extraction techniques work with high efficiency in the major parameters like Information Bit Transfer Rate (ITR), Mutual Information (MI) rate and Low Signal to Noise Ratio (SNR) and achieve a maximum classification accuracy using Self Organized Fuzzy Neural Network (SOFNN).

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Pedrosa, Paulo, Patrique Fiedler, Vanessa Pestana, Beatriz Vasconcelos, Hugo Gaspar, Maria H. Amaral, Diamantino Freitas, Jens Haueisen, João M. Nóbrega, and Carlos Fonseca. "In-service characterization of a polymer wick-based quasi-dry electrode for rapid pasteless electroencephalography." Biomedical Engineering / Biomedizinische Technik 63, no. 4 (July 26, 2018): 349–59. http://dx.doi.org/10.1515/bmt-2016-0193.

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Abstract A novel quasi-dry electrode prototype, based on a polymer wick structure filled with a specially designed hydrating solution is proposed for electroencephalography (EEG) applications. The new electrode does not require the use of a conventional electrolyte paste to achieve a wet, low-impedance scalp contact. When compared to standard commercial Ag/AgCl sensors, the proposed wick electrodes exhibit similar electrochemical noise and potential drift values. Lower impedances are observed when tested in human volunteers due to more effective electrode/skin contact. Furthermore, the electrodes exhibit an excellent autonomy, displaying an average interfacial impedance of 37±11 kΩ cm2 for 7 h of skin contact. After performing bipolar EEG trials in human volunteers, no substantial differences are evident in terms of shape, amplitude and spectral characteristics between signals of wick and commercial wet electrodes. Thus, the wick electrodes can be considered suitable to be used for rapid EEG applications (electrodes can be prepared without the presence of the patient) without the traditional electrolyte paste. The main advantages of these novel electrodes over the Ag/AgCl system are their low and stable impedance (obtained without conventional paste), long autonomy, comfort, lack of dirtying or damaging of the hair and because only a minimal cleaning procedure is required after the exam.

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She, Qingshan, Haitao Gan, Yuliang Ma, Zhizeng Luo, Tom Potter, and Yingchun Zhang. "Scale-Dependent Signal Identification in Low-Dimensional Subspace: Motor Imagery Task Classification." Neural Plasticity 2016 (2016): 1–15. http://dx.doi.org/10.1155/2016/7431012.

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Motor imagery electroencephalography (EEG) has been successfully used in locomotor rehabilitation programs. While the noise-assisted multivariate empirical mode decomposition (NA-MEMD) algorithm has been utilized to extract task-specific frequency bands from all channels in the same scale as the intrinsic mode functions (IMFs), identifying and extracting the specific IMFs that contain significant information remain difficult. In this paper, a novel method has been developed to identify the information-bearing components in a low-dimensional subspace without prior knowledge. Our method trains a Gaussian mixture model (GMM) of the composite data, which is comprised of the IMFs from both the original signal and noise, by employing kernel spectral regression to reduce the dimension of the composite data. The informative IMFs are then discriminated using a GMM clustering algorithm, the common spatial pattern (CSP) approach is exploited to extract the task-related features from the reconstructed signals, and a support vector machine (SVM) is applied to the extracted features to recognize the classes of EEG signals during different motor imagery tasks. The effectiveness of the proposed method has been verified by both computer simulations and motor imagery EEG datasets.

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Cowley, Benjamin U., Jussi Korpela, and Jari Torniainen. "Computational testing for automated preprocessing: a Matlab toolbox to enable large scale electroencephalography data processing." PeerJ Computer Science 3 (March 6, 2017): e108. http://dx.doi.org/10.7717/peerj-cs.108.

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Electroencephalography (EEG) is a rich source of information regarding brain function. However, the preprocessing of EEG data can be quite complicated, due to several factors. For example, the distinction between true neural sources and noise is indeterminate; EEG data can also be very large. The various factors create a large number of subjective decisions with consequent risk of compound error. Existing tools present the experimenter with a large choice of analysis methods. Yet it remains a challenge for the researcher to integrate methods for batch-processing of the average large datasets, and compare methods to choose an optimal approach across the many possible parameter configurations. Additionally, many tools still require a high degree of manual decision making for, e.g. the classification of artefacts in channels, epochs or segments. This introduces extra subjectivity, is slow and is not reproducible. Batching and well-designed automation can help to regularise EEG preprocessing, and thus reduce human effort, subjectivity and consequent error. We present the computational testing for automated preprocessing (CTAP) toolbox, to facilitate: (i) batch-processing that is easy for experts and novices alike; (ii) testing and manual comparison of preprocessing methods. CTAP extends the existing data structure and functions from the well-known EEGLAB toolbox, based on Matlab and produces extensive quality control outputs. CTAP is available under MIT licence fromhttps://github.com/bwrc/ctap.

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Winneke, Axel H., Michael Schulte, Matthias Vormann, and Matthias Latzel. "Effect of Directional Microphone Technology in Hearing Aids on Neural Correlates of Listening and Memory Effort: An Electroencephalographic Study." Trends in Hearing 24 (January 2020): 233121652094841. http://dx.doi.org/10.1177/2331216520948410.

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The aim of the study was to compare the effect of different spatial noise-processing algorithms in hearing aids on listening effort and memory effort on a subjective, behavioral, and neurophysiological level using electroencephalography (EEG). Two types of directional microphone (DM) technologies for spatial noise processing were chosen: one with a wide directionality (wide DM) and another with a narrower directionality (narrow DM) to accentuate the speech source. Participants with a severe hearing loss were fitted with hearing aids and participated in two EEG experiments. In the first one, participants listened to sentences in cafeteria noise and were asked to rate the experienced listening effort. The second EEG experiment was a listening span task during which participants had to repeat sentence material and then recall the final words of the last four sentences. Subjective listening effort was lower with narrow than wide DM and EEG alpha power was reduced for the narrow DM. The results of the listening span task indicated a reduction in experienced memory effort and better memory performance. During the memory retention phase, EEG alpha level for the narrow relative to the wide DM was reduced. This effect was more pronounced during linguistically difficult sentences. This study extends previous findings, as it reveals a benefit for narrow DM in terms of cognitive performance and memory effort also on a neural level, and when speech intelligibility is almost 100%. Together, this indicates that a narrow and focused DM allows for a more efficient neurocognitive processing than a wide DM.

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Paul, Brandon T., Joseph Chen, Trung Le, Vincent Lin, and Andrew Dimitrijevic. "Cortical alpha oscillations in cochlear implant users reflect subjective listening effort during speech-in-noise perception." PLOS ONE 16, no. 7 (July 9, 2021): e0254162. http://dx.doi.org/10.1371/journal.pone.0254162.

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Listening to speech in noise is effortful for individuals with hearing loss, even if they have received a hearing prosthesis such as a hearing aid or cochlear implant (CI). At present, little is known about the neural functions that support listening effort. One form of neural activity that has been suggested to reflect listening effort is the power of 8–12 Hz (alpha) oscillations measured by electroencephalography (EEG). Alpha power in two cortical regions has been associated with effortful listening—left inferior frontal gyrus (IFG), and parietal cortex—but these relationships have not been examined in the same listeners. Further, there are few studies available investigating neural correlates of effort in the individuals with cochlear implants. Here we tested 16 CI users in a novel effort-focused speech-in-noise listening paradigm, and confirm a relationship between alpha power and self-reported effort ratings in parietal regions, but not left IFG. The parietal relationship was not linear but quadratic, with alpha power comparatively lower when effort ratings were at the top and bottom of the effort scale, and higher when effort ratings were in the middle of the scale. Results are discussed in terms of cognitive systems that are engaged in difficult listening situations, and the implication for clinical translation.

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Chiarelli, Antonio Maria, David Perpetuini, Pierpaolo Croce, Giuseppe Greco, Leonardo Mistretta, Raimondo Rizzo, Vincenzo Vinciguerra, et al. "Fiberless, Multi-Channel fNIRS-EEG System Based on Silicon Photomultipliers: Towards Sensitive and Ecological Mapping of Brain Activity and Neurovascular Coupling." Sensors 20, no. 10 (May 16, 2020): 2831. http://dx.doi.org/10.3390/s20102831.

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Portable neuroimaging technologies can be employed for long-term monitoring of neurophysiological and neuropathological states. Functional Near-Infrared Spectroscopy (fNIRS) and Electroencephalography (EEG) are highly suited for such a purpose. Their multimodal integration allows the evaluation of hemodynamic and electrical brain activity together with neurovascular coupling. An innovative fNIRS-EEG system is here presented. The system integrated a novel continuous-wave fNIRS component and a modified commercial EEG device. fNIRS probing relied on fiberless technology based on light emitting diodes and silicon photomultipliers (SiPMs). SiPMs are sensitive semiconductor detectors, whose large detection area maximizes photon harvesting from the scalp and overcomes limitations of fiberless technology. To optimize the signal-to-noise ratio and avoid fNIRS-EEG interference, a digital lock-in was implemented for fNIRS signal acquisition. A benchtop characterization of the fNIRS component showed its high performances with a noise equivalent power below 1 pW. Moreover, the fNIRS-EEG device was tested in vivo during tasks stimulating visual, motor and pre-frontal cortices. Finally, the capabilities to perform ecological recordings were assessed in clinical settings on one Alzheimer’s Disease patient during long-lasting cognitive tests. The system can pave the way to portable technologies for accurate evaluation of multimodal brain activity, allowing their extensive employment in ecological environments and clinical practice.

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Wang, Li, Xiong Zhang, Xue Fei Zhong, and Zhao Wen Fan. "Selecting Filter Range of Hybrid Brain-Computer Interfaces by Mutual Information." Advanced Materials Research 981 (July 2014): 171–74. http://dx.doi.org/10.4028/www.scientific.net/amr.981.171.

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The hybrid brain-computer interface (BCI) based on electroencephalography (EEG) become more and more popular. Motor imagery, steady state visual evoked potentials (SSVEPs) and P300 are main training Paradigms. In our previous research, BCI systems based on motor imagery can be extended by speech imagery. However, noise and artifact may be produced by different mental tasks and EEG signals are also different among users, so the classification accuracy can be improved by selecting optimum frequency range for each user. Mutual information (MI) is usually used to choose optimal features. After extracted the features from each narrow frequency range of EEG by common spatial patterns (CSP), the features are assessed by MI. Then, the optimum frequency range can be acquired. The final classification results are calculated by support vector machine (SVM). The average result of optimum frequency range from seven subjects is better than the result of a fixed frequency range.

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Ortiz-Echeverri, César J., Sebastián Salazar-Colores, Juvenal Rodríguez-Reséndiz, and Roberto A. Gómez-Loenzo. "A New Approach for Motor Imagery Classification Based on Sorted Blind Source Separation, Continuous Wavelet Transform, and Convolutional Neural Network." Sensors 19, no. 20 (October 18, 2019): 4541. http://dx.doi.org/10.3390/s19204541.

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Brain-Computer Interfaces (BCI) are systems that allow the interaction of people and devices on the grounds of brain activity. The noninvasive and most viable way to obtain such information is by using electroencephalography (EEG). However, these signals have a low signal-to-noise ratio, as well as a low spatial resolution. This work proposes a new method built from the combination of a Blind Source Separation (BSS) to obtain estimated independent components, a 2D representation of these component signals using the Continuous Wavelet Transform (CWT), and a classification stage using a Convolutional Neural Network (CNN) approach. A criterion based on the spectral correlation with a Movement Related Independent Component (MRIC) is used to sort the estimated sources by BSS, thus reducing the spatial variance. The experimental results of 94.66% using a k-fold cross validation are competitive with techniques recently reported in the state-of-the-art.

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Nicolae, Irina E., and Mihai Ivanovici. "Preparatory Experiments Regarding Human Brain Perception and Reasoning of Image Complexity for Synthetic Color Fractal and Natural Texture Images via EEG." Applied Sciences 11, no. 1 (December 26, 2020): 164. http://dx.doi.org/10.3390/app11010164.

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Texture plays an important role in computer vision in expressing the characteristics of a surface. Texture complexity evaluation is important for relying not only on the mathematical properties of the digital image, but also on human perception. Human subjective perception verbally expressed is relative in time, since it can be influenced by a variety of internal or external factors, such as: Mood, tiredness, stress, noise surroundings, and so on, while closely capturing the thought processes would be more straightforward to human reasoning and perception. With the long-term goal of designing more reliable measures of perception which relate to the internal human neural processes taking place when an image is perceived, we firstly performed an electroencephalography experiment with eight healthy participants during color textural perception of natural and fractal images followed by reasoning on their complexity degree, against single color reference images. Aiming at more practical applications for easy use, we tested this entire setting with a WiFi 6 channels electroencephalography (EEG) system. The EEG responses are investigated in the temporal, spectral and spatial domains in order to assess human texture complexity perception, in comparison with both textural types. As an objective reference, the properties of the color textural images are expressed by two common image complexity metrics: Color entropy and color fractal dimension. We observed in the temporal domain, higher Event Related Potentials (ERPs) for fractal image perception, followed by the natural and one color images perception. We report good discriminations between perceptions in the parietal area over time and differences in the temporal area regarding the frequency domain, having good classification performance.

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Scherer, Reinhold, Stavros P. Zanos, Kai J. Miller, Rajesh P. N. Rao, and Jeffrey G. Ojemann. "Classification of contralateral and ipsilateral finger movements for electrocorticographic brain-computer interfaces." Neurosurgical Focus 27, no. 1 (July 2009): E12. http://dx.doi.org/10.3171/2009.4.focus0981.

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Electrocorticography (ECoG) offers a powerful and versatile platform for developing brain-computer interfaces; it avoids the risks of brain-invasive methods such as intracortical implants while providing significantly higher signal-to-noise ratio than noninvasive techniques such as electroencephalography. The authors demonstrate that both contra- and ipsilateral finger movements can be discriminated from ECoG signals recorded from a single brain hemisphere. The ECoG activation patterns over sensorimotor areas for contra- and ipsilateral movements were found to overlap to a large degree in the recorded hemisphere. Ipsilateral movements, however, produced less pronounced activity compared with contralateral movements. The authors also found that single-trial classification of movements could be improved by selecting patient-specific frequency components in high-frequency bands (> 50 Hz). Their discovery that ipsilateral hand movements can be discriminated from ECoG signals from a single hemisphere has important implications for neurorehabilitation, suggesting in particular the possibility of regaining ipsilateral movement control using signals from an intact hemisphere after damage to the other hemisphere.

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Journal articles on the topic 'Noise Electroencephalography'

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