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Journal articles on the topic 'Brain signal acquisition'

Author: Grafiati

Published: 6 September 2023

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1

Shelishiyah, R., M. Bharani Dharan, T. Kishore Kumar, R. Musaraf, and Thiyam Deepa Beeta. "Signal Processing for Hybrid BCI Signals." Journal of Physics: Conference Series 2318, no. 1 (2022): 012007. http://dx.doi.org/10.1088/1742-6596/2318/1/012007.

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Abstract The brain signals can be converted to a command to control some external device using a brain-computer interface system. The unimodal BCI system has limitations like the compensation of the accuracy with the increase in the number of classes. In addition to this many of the acquisition systems are not robust for real-time application because of poor spatial or temporal resolution. To overcome this, a hybrid BCI technology that combines two acquisition systems has been introduced. In this work, we have discussed a preprocessing pipeline for enhancing brain signals acquired from fNIRS (

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Wang, Jiu Hui, and Qiang Ji. "Research on Signal Acquisition Based on Wireless Sensor for Foot Compressive Characteristics on Basketball Movement." Applied Mechanics and Materials 483 (December 2013): 401–4. http://dx.doi.org/10.4028/www.scientific.net/amm.483.401.

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The signal acquisition system (SAS) operated by battery is designed in this paper. SAS includes signal acquisition and statistics function based on movement joints of basketball player. SAS is a recording of the electrical activity of the brain and pulse from the scalp and the recorded waveforms provide insights into the dynamic aspects of brain activity. The amplified SAS signals are digitized by an A/D converter. The digitized signal is transmitted to PC by a wireless serial port or stored in secure digital memory card. Experimental result shows that the system could implement the acquisitio

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Yuan, Lixue, Yinyan Fan, Quanxi Gan, and Huibin Feng. "Clinical Diagnosis of Psychiatry Based on Electroencephalography." Journal of Medical Imaging and Health Informatics 11, no. 3 (2021): 955–63. http://dx.doi.org/10.1166/jmihi.2021.3338.

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At present, neurophysiological signals used for neuro feedback are EEG (Electroencephalogram), functional magnetic resonance imaging. Among them, the acquisition of EEG signals has the advantages of non-invasive way with low cost. It has been widely used in brain-machine interface technology in recent years. Important progress has been made in rehabilitation and environmental control. However, neural feedback and brainmachine interface technology are completely similar in signal acquisition, signal feature extraction, and pattern classification. Therefore, the related research results of brain

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Edison, Rizki Edmi, Rohmadi Rohmadi, Sra Harke Pratama, Muhammad Fathul Ihsan, Almusfi Saputra, and Warsito Purwo Taruno. "Design of Brain Activity Measurement for Brain ECVT Data Acquisition System." International Journal of Innovative Research in Medical Science 6, no. 10 (2021): 630–34. http://dx.doi.org/10.23958/ijirms/vol06-i10/1223.

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Brain Electrical Capacitance Volume Tomography (ECVT) has been developing as an alternative non-invasive brain imaging method. In this study, brain ECVT consisting of two channels, namely a capacitance sensor, is investigated. As a comparison, EEG sensor is used to measure brain activity simultaneously with the brain ECVT. Brain activity measurements were carried out at the pre-frontal lobe of Fp1 and Fp2 locations. The resulting signal was processed by filtering method and Power Spectral Density (PSD). The result of signal analysis shows that the measurement between EEG and ECVT shows the sam

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Wang, Shinmin, Ovid J. L. Tzeng, and Richard N. Aslin. "Predictive brain signals mediate association between shared reading and expressive vocabulary in infants." PLOS ONE 17, no. 8 (2022): e0272438. http://dx.doi.org/10.1371/journal.pone.0272438.

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The ability to predict upcoming information is crucial for efficient language processing and enables more rapid language learning. The present study explored how shared reading experience influenced predictive brain signals and expressive vocabulary of 12-month-old infants. The predictive brain signals were measured by fNIRS responses in the occipital lobe with an unexpected visual-omission task. The amount of shared reading experience was correlated with the strength of this predictive brain signal and with infants’ expressive vocabulary. Importantly, the predictive brain signal explained uni

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Lin, Jzau Sgeng, and Sun Ming Huang. "An FPGA-Based Brain-Computer Interface for Wireless Electric Wheelchairs." Applied Mechanics and Materials 284-287 (January 2013): 1616–21. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.1616.

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A wireless EEG-based brain-computer interface (BCI) and an FPGA-based system to control electric wheelchairs through a Bluetooth interface was proposed in this paper for paralyzed patients. Paralytic patients can not move freely and only use wheelchairs in their daily life. Especially, people getting motor neuron disease (MND) can only use their eyes and brain to exercise their willpower. Therefore, real-time EEG and winking signals can help these patients effectively. However, current BCI systems are usually complex and have to send the brain waves to a personal computer or a single-chip micr

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Ranjandish, Reza, and Alexandre Schmid. "A Review of Microelectronic Systems and Circuit Techniques for Electrical Neural Recording Aimed at Closed-Loop Epilepsy Control." Sensors 20, no. 19 (2020): 5716. http://dx.doi.org/10.3390/s20195716.

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Closed-loop implantable electronics offer a new trend in therapeutic systems aimed at controlling some neurological diseases such as epilepsy. Seizures are detected and electrical stimulation applied to the brain or groups of nerves. To this aim, the signal recording chain must be very carefully designed so as to operate in low-power and low-latency, while enhancing the probability of correct event detection. This paper reviews the electrical characteristics of the target brain signals pertaining to epilepsy detection. Commercial systems are presented and discussed. Finally, the major blocks o

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Perman, William H., Mokhtar H. Gado, Kenneth B. Larson, and Joel S. Perlmutter. "Simultaneous MR Acquisition of Arterial and Brain Signal-Time Curves." Magnetic Resonance in Medicine 28, no. 1 (1992): 74–83. http://dx.doi.org/10.1002/mrm.1910280108.

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Chenane, Kathia, Youcef Touati, Larbi Boubchir, and Boubaker Daachi. "Neural Net-Based Approach to EEG Signal Acquisition and Classification in BCI Applications." Computers 8, no. 4 (2019): 87. http://dx.doi.org/10.3390/computers8040087.

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The following contribution describes a neural net-based, noninvasive methodology for electroencephalographic (EEG) signal classification. The application concerns a brain–computer interface (BCI) allowing disabled people to interact with their environment using only brain activity. It consists of classifying user’s thoughts in order to translate them into commands, such as controlling wheelchairs, cursor movement, or spelling. The proposed method follows a functional model, as is the case for any BCI, and can be achieved through three main phases: data acquisition and preprocessing, feature ex

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Vajravelu, Ashok, Muhammad Mahadi Bin Abdul Jamil, Mohd Helmy Bin Abd Wahab, et al. "Nanocomposite-Based Electrode Structures for EEG Signal Acquisition." Crystals 12, no. 11 (2022): 1526. http://dx.doi.org/10.3390/cryst12111526.

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Objective: To fabricate a lightweight, breathable, comfortable, and able to contour to the curvilinear body shape, electrodes built on a flexible substrate are a significant growth in wearable health monitoring. This research aims to create a GNP/FE electrode-based EEG signal acquisition system that is both efficient and inexpensive. Methodology: Three distinct electrode concentrations were developed for EEG signal acquisition, three distinct electrode concentrations (1.5:1.5, 2:1, and 3:0). The high strength-to-weight ratio to form the tribofilm in the fabrication of the electrode will provid

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Ferrari, Rosana, Aldo Ivan Cespedes Arce, Mariza Pires de Melo, and Ernane Jose Xavier Costa. "Noninvasive method to assess the electrical brain activity from rats." Ciência Rural 43, no. 10 (2013): 1838–42. http://dx.doi.org/10.1590/s0103-84782013005000117.

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This research presents a noninvasive method for the acquisition of brain electrical signal in rat. Was used an electroencephalography (EEG) system developed for bovine and adapted to rats. The bipolar electrode system (needle electrodes) was glued on the surface of the head of the animal without surgical procedures and the other electrode was glued to the tail, as ground. The EEG activity was sampled at 120Hz for an hour. The accuracy and precision of the EEG measurement was performed using Fourier analysis and signal energy. For this, the digital signal was divided into sections successive of

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Aach, T., H. Witte, and T. M. Lehmann. "Sensor, Signal and Image Informatics." Yearbook of Medical Informatics 15, no. 01 (2006): 57–67. http://dx.doi.org/10.1055/s-0038-1638479.

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SummaryThe number of articles published annually in the fields of biomedical signal and image acquisition and processing is increasing. Based on selected examples, this survey aims at comprehensively demonstrating the recent trends and developments.Four articles are selected for biomedical data acquisition covering topics such as dose saving in CT, C-arm X-ray imaging systems for volume imaging, and the replacement of dose-intensive CTbased diagnostic with harmonic ultrasound imaging. Regarding biomedical signal analysis (BSA), the four selected articles discuss the equivalence of different ti

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Orban, Mostafa, Mahmoud Elsamanty, Kai Guo, Senhao Zhang, and Hongbo Yang. "A Review of Brain Activity and EEG-Based Brain–Computer Interfaces for Rehabilitation Application." Bioengineering 9, no. 12 (2022): 768. http://dx.doi.org/10.3390/bioengineering9120768.

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Patients with severe CNS injuries struggle primarily with their sensorimotor function and communication with the outside world. There is an urgent need for advanced neural rehabilitation and intelligent interaction technology to provide help for patients with nerve injuries. Recent studies have established the brain-computer interface (BCI) in order to provide patients with appropriate interaction methods or more intelligent rehabilitation training. This paper reviews the most recent research on brain-computer-interface-based non-invasive rehabilitation systems. Various endogenous and exogenou

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Abdulwahab, Samaa S., Hussain K. Khleaf, and Manal H. Jassim. "A Survey in Implementation and Applications of Electroencephalograph (EEG)-Based Brain-Computer Interface." Engineering and Technology Journal 39, no. 7 (2021): 1117–32. http://dx.doi.org/10.30684/etj.v39i7.1854.

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A Brain-Computer Interface (BCI) is an external system that controls activities and processes in the physical world based on brain signals. In Passive BCI, artificial signals are automatically generated by a computer program without any input from nerves in the body. This is useful for individuals with mobility issues. Traditional BCI has been dependent only on recording brain signals with Electroencephalograph (EEG) and has used a rule-based translation algorithm to generate control commands. These systems have developed very accurate translation systems. This paper is about the different met

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Gopalakrishnaiah, Shubratha Koralagundi, Kevin Joseph, and Ulrich G. Hofmann. "Microfluidic drive for flexible brain implants." Current Directions in Biomedical Engineering 3, no. 2 (2017): 675–78. http://dx.doi.org/10.1515/cdbme-2017-0142.

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AbstractFlexible polyimide probes, used for neuronal signal acquisition, are thought to reduce signal deteriorating gliosis, improving the quality of recordings in brain machine interfacing applications. These probes suffer from the disadvantage that they cannot penetrate brain tissue on their own, owing to their limited stiffness and low buckling forces. A microfluidic device as an external micro-drive which aids in the insertion of flexible polyimide neural probes in 0.6% Agarose gel is presented here.

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Yarmish, Gail, and Michael L. Lipton. "Functional Magnetic Resonance Imaging: From Acquisition to Application." Einstein Journal of Biology and Medicine 20, no. 1 (2016): 2. http://dx.doi.org/10.23861/ejbm200320103.

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Functional magnetic resonance imaging (fMRI) is a technique that exploits magnetic resonance imaging (MRI) to detect regional brain activity through measurement of the hemodynamic response that is coupled to electrical neuronal activity. The most common fMRI method detects blood oxygen level dependent (BOLD) contrast. The BOLD effect represents alteration in the ratio of deoxygenated to oxygenated hemoglobin within brain tissue following neuronal activity. Alterations in this hemoglobin ratio result from changes in cerebral oxygen extraction, cerebral blood flow, and cerebral blood volume that

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Qiao, Xiao Yan, and Jia Hui Peng. "P300 Feature Extraction of Visual and Auditory Evoked EEG Signal." Applied Mechanics and Materials 490-491 (January 2014): 1374–77. http://dx.doi.org/10.4028/www.scientific.net/amm.490-491.1374.

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It is a significant issue to accurately and quickly extract brain evoked potentials under strong noise in the research of brain-computer interface technology. Considering the non-stationary and nonlinearity of the electroencephalogram (EEG) signal, the method of wavelet transform is adopted to extract P300 feature from visual, auditory and visual-auditory evoked EEG signal. Firstly, the imperative pretreatment to EEG acquisition signals was performed. Secondly, respectivly obtained approximate and detail coefficients of each layer, by decomposing the pretreated signals for five layers using wa

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Tong, Peiwen, Hui Xu, Yi Sun, Yongzhou Wang, Wei Wang, and Jiwei Li. "Electroencephalogram signal analysis with 1T1R arrays toward high-efficiency brain computer interface." AIP Advances 12, no. 12 (2022): 125108. http://dx.doi.org/10.1063/5.0117159.

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Brain computer interface (BCI) is a promising way for automatic driving and exploring brain functions. As the number of electrodes for electroencephalogram (EEG) acquisition continues to grow, the signal processing capabilities of BCI are facing challenges. Considering the bottlenecks of the Von Neumann architecture, it is increasingly difficult for the traditional digital computing pattern to meet the requirements of the EEG signal processing in terms of power consumption and efficiency. Here, we propose a 1T1R array-based EEG signal analysis system in which the biological likelihood of the m

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Jurgielewicz, Paweł, Tomasz Fiutowski, Ewa Kublik, et al. "Modular Data Acquisition System for Recording Activity and Electrical Stimulation of Brain Tissue Using Dedicated Electronics." Sensors 21, no. 13 (2021): 4423. http://dx.doi.org/10.3390/s21134423.

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In this paper, we present a modular Data Acquisition (DAQ) system for simultaneous electrical stimulation and recording of brain activity. The DAQ system is designed to work with custom-designed Application Specific Integrated Circuit (ASIC) called Neurostim-3 and a variety of commercially available Multi-Electrode Arrays (MEAs). The system can control simultaneously up to 512 independent bidirectional i.e., input-output channels. We present in-depth insight into both hardware and software architectures and discuss relationships between cooperating parts of that system. The particular focus of

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Bahr, Andreas, Lait Abu Saleh, Dietmar Schroeder, and Wolfgang H. Krautschneider. "High speed digital interfacing for a neural data acquisition system." Current Directions in Biomedical Engineering 2, no. 1 (2016): 87–90. http://dx.doi.org/10.1515/cdbme-2016-0022.

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AbstractDiseases like schizophrenia and genetic epilepsy are supposed to be caused by disorders in the early development of the brain. For the further investigation of these relationships a custom designed application specific integrated circuit (ASIC) was developed that is optimized for the recording from neonatal mice [Bahr A, Abu-Saleh L, Schroeder D, Krautschneider W. 16 Channel Neural Recording Integrated Circuit with SPI Interface and Error Correction Coding. Proc. 9th BIOSTEC 2016. Biodevices: Rome, Italy, 2016; 1: 263; Bahr A, Abu-Saleh L, Schroeder D, Krautschneider W. Development of

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Karimi-Bidhendi, Alireza, Omid Malekzadeh-Arasteh, Mao-Cheng Lee, et al. "CMOS Ultralow Power Brain Signal Acquisition Front-Ends: Design and Human Testing." IEEE Transactions on Biomedical Circuits and Systems 11, no. 5 (2017): 1111–22. http://dx.doi.org/10.1109/tbcas.2017.2723607.

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Kasper, Lars, Maria Engel, Christoph Barmet, et al. "Rapid anatomical brain imaging using spiral acquisition and an expanded signal model." NeuroImage 168 (March 2018): 88–100. http://dx.doi.org/10.1016/j.neuroimage.2017.07.062.

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Englert, Robert, Fabienne Rupp, Frank Kirchhoff, Klaus Peter Koch, and Michael Schweigmann. "Technical characterization of an 8 or 16 channel recording system to acquire electrocorticograms of mice." Current Directions in Biomedical Engineering 3, no. 2 (2017): 595–98. http://dx.doi.org/10.1515/cdbme-2017-0124.

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AbstractWhen performing electrocorticography, reliable recordings of bioelectrical signals are essential for signal processing and analysis. The acquisition of cellular electrical activity from the brain surface of mice requires a system that is able to record small signals within a low frequency range. This work presents a recording system with self-developed software and shows the result of a technical characterization in combination with self-developed electrode arrays to measure electrocorticograms of mice.

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Tong, Yunjie, Kimberly P. Lindsey, and Blaise deB Frederick. "Partitioning of Physiological Noise Signals in the Brain with Concurrent Near-Infrared Spectroscopy and fMRI." Journal of Cerebral Blood Flow & Metabolism 31, no. 12 (2011): 2352–62. http://dx.doi.org/10.1038/jcbfm.2011.100.

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The blood–oxygen level dependent (BOLD) signals measured by functional magnetic resonance imaging (fMRI) are contaminated with noise from various physiological processes, such as spontaneous low-frequency oscillations (LFOs), respiration, and cardiac pulsation. These processes are coupled to the BOLD signal by different mechanisms, and represent variations with very different frequency content; however, because of the low sampling rate of fMRI, these signals are generally not separable by frequency, as the cardiac and respiratory waveforms alias into the LFO band. In this study, we investigate

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Stevenazzi, Lorenzo, Andrea Baschirotto, Giorgio Zanotto, Elia Arturo Vallicelli, and Marcello De Matteis. "Noise Power Minimization in CMOS Brain-Chip Interfaces." Bioengineering 9, no. 2 (2022): 42. http://dx.doi.org/10.3390/bioengineering9020042.

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This paper presents specific noise minimization strategies to be adopted in silicon–cell interfaces. For this objective, a complete and general model for the analog processing of the signal coming from cell–silicon junctions is presented. This model will then be described at the level of the single stages and of the fundamental parameters that characterize them (bandwidth, gain and noise). Thanks to a few design equations, it will therefore be possible to simulate the behavior of a time-division multiplexed acquisition channel, including the most relevant parameters for signal processing, such

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Re, Rebecca, Ileana Pirovano, Davide Contini, et al. "Reliable Fast (20 Hz) Acquisition Rate by a TD fNIRS Device: Brain Resting-State Oscillation Studies." Sensors 23, no. 1 (2022): 196. http://dx.doi.org/10.3390/s23010196.

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A high power setup for multichannel time-domain (TD) functional near infrared spectroscopy (fNIRS) measurements with high efficiency detection system was developed. It was fully characterized based on international performance assessment protocols for diffuse optics instruments, showing an improvement of the signal-to-noise ratio (SNR) with respect to previous analogue devices, and allowing acquisition of signals with sampling rate up to 20 Hz and source-detector distance up to 5 cm. A resting-state measurement on the motor cortex of a healthy volunteer was performed with an acquisition rate o

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Lee, Do-Wan, Chul-Woong Woo, Dong-Cheol Woo, Jeong Kon Kim, Kyung Won Kim, and Dong-Hoon Lee. "Regional Mapping of Brain Glutamate Distributions Using Glutamate-Weighted Chemical Exchange Saturation Transfer Imaging." Diagnostics 10, no. 8 (2020): 571. http://dx.doi.org/10.3390/diagnostics10080571.

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Purpose: To investigate glutamate signal distributions in multiple brain regions of a healthy rat brain using glutamate-weighted chemical exchange saturation transfer (GluCEST) imaging. Method: The GluCEST data were obtained using a 7.0 T magnetic resonance imaging (MRI) scanner, and all data were analyzed using conventional magnetization transfer ratio asymmetry in eight brain regions (cortex, hippocampus, corpus callosum, and rest of midbrain in each hemisphere). GluCEST data acquisition was performed again one month later in five randomly selected rats to evaluate the stability of the GluCE

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Xu, Bao Lei, Yun Fa Fu, Gang Shi, et al. "Comparison of Optical and Concentration Feature Used for fNIRS-Based BCI System Using HMM." Applied Mechanics and Materials 385-386 (August 2013): 1443–48. http://dx.doi.org/10.4028/www.scientific.net/amm.385-386.1443.

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Brain-Computer Interface (BCI) is very useful for people who lose limb control such as amyotrophic lateral sclerosis (ALS) patients, stroke patients and patients with prosthetic limbs. Among all the brain signal acquisition devices, functional near-infrared spectroscopy (fNIRS) is an efficient approach to detect hemodynamic responses correlated with brain activities using optical method, and its spatial resolution is much higher than EEG. In this paper, we investigate the classification performance of both optical signal and hemodynic signal that both used in fNIRS-based BCI system using Hidde

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Chang, Yuwei. "Enhancement of Human Feeling via AI-based BCI: A Survey." Highlights in Science, Engineering and Technology 36 (March 21, 2023): 633–37. http://dx.doi.org/10.54097/hset.v36i.5748.

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Technology developments related with brain-computer interface (BCI) promote study and research in emotion recognition. In study recognizes, classifies human emotional states, electroencephalograph (EEG) signal acquired by BCI devices will go through several process include data analysis in computational research. This article performs a survey in recent study use EEG as signal acquisition equipment, compare research targets, and provide summary of both research-grade EEG, consumer-grade EEG devices used in recent research. A comprehensive view of emotion recognition research process is given.

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Liu, Huawei, Adam W. Autry, Peder E. Z. Larson, Duan Xu, and Yan Li. "Atlas-Based Adaptive Hadamard-Encoded MR Spectroscopic Imaging at 3T." Tomography 9, no. 5 (2023): 1592–602. http://dx.doi.org/10.3390/tomography9050127.

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Background: This study aimed to develop a time-efficient method of acquiring simultaneous, dual-slice MR spectroscopic imaging (MRSI) for the evaluation of brain metabolism. Methods: Adaptive Hadamard-encoded pulses were developed and integrated with atlas-based automatic prescription. The excitation profiles were evaluated via simulation, phantom and volunteer experiments. The feasibility of γ-aminobutyric acid (GABA)-edited dual-slice MRSI was also assessed. Results: The signal between slices in the dual-band MRSI was less than 1% of the slice profiles. Data from a homemade phantom containin

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Chaddad, Ahmad, Yihang Wu, Reem Kateb, and Ahmed Bouridane. "Electroencephalography Signal Processing: A Comprehensive Review and Analysis of Methods and Techniques." Sensors 23, no. 14 (2023): 6434. http://dx.doi.org/10.3390/s23146434.

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The electroencephalography (EEG) signal is a noninvasive and complex signal that has numerous applications in biomedical fields, including sleep and the brain–computer interface. Given its complexity, researchers have proposed several advanced preprocessing and feature extraction methods to analyze EEG signals. In this study, we analyze a comprehensive review of numerous articles related to EEG signal processing. We searched the major scientific and engineering databases and summarized the results of our findings. Our survey encompassed the entire process of EEG signal processing, from acquisi

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Chanu, Oinam Robita, R. Kalpana, B. Soorya, R. Santhosh, and V. Karthik Raj. "Development of a Hardware Circuit for Real-Time Acquisition of Brain Activity Using NI myDAQ." Journal of Circuits, Systems and Computers 29, no. 10 (2020): 2050170. http://dx.doi.org/10.1142/s0218126620501704.

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Electroencephalography (EEG) is the recording of electrical activity of the brain. The 10–20 system is the standard electrode location method used to acquire EEG data, which uses 21 electrodes to record the electrical activity of the brain. Patient preparation and correct electrode placement are important to obtain reliable outputs. The current 10–20 system consumes greater time for patient preparation and also causes discomfort due to a higher number of electrodes being used or wearing an uncomfortable cap. This paper focuses on reducing the number of electrodes, thus reducing patient discomf

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Martínez-Villaseñor, Lourdes, and Hiram Ponce. "A concise review on sensor signal acquisition and transformation applied to human activity recognition and human–robot interaction." International Journal of Distributed Sensor Networks 15, no. 6 (2019): 155014771985398. http://dx.doi.org/10.1177/1550147719853987.

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Human activitiy recognition deals with the integration of sensing and reasoning aiming to understand better people’s actions. Moreover, it plays an important role in human interaction, human–robot interaction, and brain–computer interaction. When these approaches have to be developed, different efforts from signal processing and artificial intelligence are considered. In that sense, this article aims to present a concise review of signal processing in human activitiy recognition systems and describe two examples and applications both in human activity recognition and robotics: human–robot inte

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Gao, Xiang, Gesangzeren Fnu, and Xianshu Wan. "Development of the Electroencephalograph-based Brain Computer Interface System." Journal of Physics: Conference Series 2078, no. 1 (2021): 012079. http://dx.doi.org/10.1088/1742-6596/2078/1/012079.

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Abstract A practical BCI-based application design contains a variety of design stages are needed to be considered. The design challenges are majorly present in 3 major stages: brain signal acquisition, signal processing unit, and signal classification. Combinations of different approaches have to be employed to achieve the functional and accurate performance of the overall design. Those design choices, algorithms, and methodologies that are meant to solve design challenges presented in the previously mentioned three stages have become a hot subject of a number of studies. This paper aims at pr

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Qing, Zengyu, Zongxing Lu, Yingjie Cai, and Jing Wang. "Elements Influencing sEMG-Based Gesture Decoding: Muscle Fatigue, Forearm Angle and Acquisition Time." Sensors 21, no. 22 (2021): 7713. http://dx.doi.org/10.3390/s21227713.

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The surface Electromyography (sEMG) signal contains information about movement intention generated by the human brain, and it is the most intuitive and common solution to control robots, orthotics, prosthetics and rehabilitation equipment. In recent years, gesture decoding based on sEMG signals has received a lot of research attention. In this paper, the effects of muscle fatigue, forearm angle and acquisition time on the accuracy of gesture decoding were researched. Taking 11 static gestures as samples, four specific muscles (i.e., superficial flexor digitorum (SFD), flexor carpi ulnaris (FCU

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PARK, HYUNG-MIN, JONG-HWAN LEE, TAESU KIM, et al. "MODELING AUDITORY PATHWAY FOR INTELLIGENT INFORMATION ACQUISITION." International Journal of Information Acquisition 01, no. 04 (2004): 345–56. http://dx.doi.org/10.1142/s0219878904000367.

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An auditory model has been developed for an intelligent speech information acquisition system in real-world noisy environment. The developed mathematical model of the human auditory pathway consists of three components, i.e. the nonlinear feature extraction from cochlea to auditory cortex, the binaural processing at superior olivery complex, and the top-down attention from higher brain to the cochlea. The feature extraction is based on information-theoretic sparse coding throughout the auditory pathway. Also, the time-frequency masking is incorporated as a model of the lateral inhibition in bo

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Moreno Escobar, Jesús Jaime, Oswaldo Morales Matamoros, Ricardo Tejeida Padilla, et al. "Biomedical Signal Acquisition Using Sensors under the Paradigm of Parallel Computing." Sensors 20, no. 23 (2020): 6991. http://dx.doi.org/10.3390/s20236991.

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There are several pathologies attacking the central nervous system and diverse therapies for each specific disease. These therapies seek as far as possible to minimize or offset the consequences caused by these types of pathologies and disorders in the patient. Therefore, comprehensive neurological care has been performed by neurorehabilitation therapies, to improve the patients’ life quality and facilitating their performance in society. One way to know how the neurorehabilitation therapies contribute to help patients is by measuring changes in their brain activity by means of electroencephal

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Mascia, Antonello, Riccardo Collu, Andrea Spanu, Matteo Fraschini, Massimo Barbaro, and Piero Cosseddu. "Wearable System Based on Ultra-Thin Parylene C Tattoo Electrodes for EEG Recording." Sensors 23, no. 2 (2023): 766. http://dx.doi.org/10.3390/s23020766.

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In an increasingly interconnected world, where electronic devices permeate every aspect of our lives, wearable systems aimed at monitoring physiological signals are rapidly taking over the sport and fitness domain, as well as biomedical fields such as rehabilitation and prosthetics. With the intent of providing a novel approach to the field, in this paper we discuss the development of a wearable system for the acquisition of EEG signals based on a portable, low-power custom PCB specifically designed to be used in combination with non-conventional ultra-conformable and imperceptible Parylene-C

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Zhang, Yu Xi, Wen Gui Fan, and Jin Ping Sun. "Compressed Sensing Based Neural Signal Processing and Performance Analysis." Applied Mechanics and Materials 513-517 (February 2014): 1595–99. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.1595.

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Measurement of neural signal provides important value for study of brain function and the pathogenesis of neurological. With emerging extensive research of electrical activity, more and more neural signal need to be collected, transmitted and stored, making the compression processing of neural signal become important part of digital signal processing. In recent years, ASIC-based wireless neural signal acquisition system has been developed rapidly, encountered strict restrictions on power consumption which is dominant determined by the data rate and complexity of algorithm. In order to reduce p

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Browarska, Natalia, Aleksandra Kawala-Sterniuk, Jaroslaw Zygarlicki, et al. "Comparison of Smoothing Filters’ Influence on Quality of Data Recorded with the Emotiv EPOC Flex Brain–Computer Interface Headset during Audio Stimulation." Brain Sciences 11, no. 1 (2021): 98. http://dx.doi.org/10.3390/brainsci11010098.

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Off-the-shelf, consumer-grade EEG equipment is nowadays becoming the first-choice equipment for many scientists when it comes to recording brain waves for research purposes. On one hand, this is perfectly understandable due to its availability and relatively low cost (especially in comparison to some clinical-level EEG devices), but, on the other hand, quality of the recorded signals is gradually increasing and reaching levels that were offered just a few years ago by much more expensive devices used in medicine for diagnostic purposes. In many cases, a well-designed filter and/or a well-thoug

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Vidaurre, Carmen, Tilmann H. Sander, and Alois Schlögl. "BioSig: The Free and Open Source Software Library for Biomedical Signal Processing." Computational Intelligence and Neuroscience 2011 (2011): 1–12. http://dx.doi.org/10.1155/2011/935364.

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BioSig is an open source software library for biomedical signal processing. The aim of the BioSig project is to foster research in biomedical signal processing by providing free and open source software tools for many different application areas. Some of the areas where BioSig can be employed are neuroinformatics, brain-computer interfaces, neurophysiology, psychology, cardiovascular systems, and sleep research. Moreover, the analysis of biosignals such as the electroencephalogram (EEG), electrocorticogram (ECoG), electrocardiogram (ECG), electrooculogram (EOG), electromyogram (EMG), or respir

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Rama Raju, Venkateshwarla, Kavitha Rani Balmuri, Konda Srinivas, and G. Madhukar. "MER Signal Acquisition of STN-DBS Biomarkers in Parkinson`s: A machine learning auto regression approach." IP Indian Journal of Neurosciences 7, no. 3 (2021): 224–30. http://dx.doi.org/10.18231/j.ijn.2021.040.

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Microelectrode recording (MER) or microelectrode signals recording of local field potentials by means of subthalamic-nuclei deep brain stimulation is highly successful for construing or deducing Parkinson disease (PD) signal analysis acquiescent to elucidation are fetching ever more germane. These signals are supposed to emulate STN neurons action-potential movement and, these potential frequency modulations are coupled to spiking-events. The method uses auto regression stochastic (random nature) model machine learning approach and, other standard techniques as of system identification field.

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Ma, Tengfei, Wentian Chen, Xin Li, Yuting Xia, Xinhua Zhu, and Sailing He. "fNIRS Signal Classification Based on Deep Learning in Rock-Paper-Scissors Imagery Task." Applied Sciences 11, no. 11 (2021): 4922. http://dx.doi.org/10.3390/app11114922.

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To explore whether the brain contains pattern differences in the rock–paper–scissors (RPS) imagery task, this paper attempts to classify this task using fNIRS and deep learning. In this study, we designed an RPS task with a total duration of 25 min and 40 s, and recruited 22 volunteers for the experiment. We used the fNIRS acquisition device (FOIRE-3000) to record the cerebral neural activities of these participants in the RPS task. The time series classification (TSC) algorithm was introduced into the time-domain fNIRS signal classification. Experiments show that CNN-based TSC methods can ach

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Bhagawati, Amlan Jyoti, and Riku Chutia. "Design of Single Channel Portable EEG Signal Acquisition System for Brain Computer Interface Application." International journal of Biomedical Engineering and Science 3, no. 1 (2016): 37–44. http://dx.doi.org/10.5121/ijbes.2016.3103.

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Nallet, Caroline, and Judit Gervain. "Neurodevelopmental Preparedness for Language in the Neonatal Brain." Annual Review of Developmental Psychology 3, no. 1 (2021): 41–58. http://dx.doi.org/10.1146/annurev-devpsych-050620-025732.

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Neonates show broad-based, universal speech perception abilities, allowing them to acquire any language. Moreover, an increasing body of research shows that prenatal experience with speech, which is a low-pass signal mainly preserving prosody, already shapes those abilities. In this review, we first provide a summary of the empirical evidence available today on newborns’ universal and experience-modulated speech perception abilities. We then interpret these findings in a new framework, focusing on the role of the prenatal prosodic experience in speech perception development. We argue that the

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Zhi, Chunxiang. "A Brain-Myoelectric Signal-Based Approach to Hand Rehabilitation in Stroke." Scholars Journal of Engineering and Technology 11, no. 06 (2023): 139–46. http://dx.doi.org/10.36347/sjet.2023.v11i06.003.

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The existing hand function rehabilitation training model for stroke patients has problems such as single mode, low patient participation, poor rehabilitation effect and long rehabilitation period. In this paper, we propose an active stroke hand rehabilitation training method based on brain EMG signals, including the use of EEG signals to help stroke patients achieve brain neural remodelling and EMG signals to achieve real-time hand function rehabilitation training to assist patients to complete hand rehabilitation. Firstly, a multimodal guided motor imagery experimental paradigm with a mixture

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Dimitrov, Georgi P., Galina Panayotova, Boyan Jekov, et al. "Algorithms for Classification of Signals Derived From Human Brain." International Journal of Circuits, Systems and Signal Processing 15 (September 20, 2021): 1521–26. http://dx.doi.org/10.46300/9106.2021.15.164.

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Comparison of the Accuracy of different off-line methods for classification Electroencephalograph (EEG) signals, obtained from Brain-Computer Interface (BCI) devices are investigated in this paper. BCI is a technology that allows people to interact directly or indirectly with their environment only by using brain activity. But, the method of signal acquisition is non-invasive, resulting in significant data loss. In addition, the received signals do not contain only useful information. All this requires careful selection of the method for the classification of the received signals. The main pur

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Jin, Zhaoyang, Ling Xia, Minming Zhang, and Yiping P. Du. "Background-Suppressed MR Venography of the Brain Using Magnitude Data: A High-Pass Filtering Approach." Computational and Mathematical Methods in Medicine 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/812785.

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Conventional susceptibility-weighted imaging (SWI) uses both phase and magnitude data for the enhancement of venous vasculature and, thus, is subject to signal loss in regions with severe field inhomogeneity and in the peripheral regions of the brain in the minimum-intensity projection. The purpose of this study is to enhance the visibility of the venous vasculature and reduce the artifacts in the venography by suppressing the background signal in postprocessing. A high-pass filter with an inverted Hamming window or an inverted Fermi window was applied to the Fourier domain of the magnitude im

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Sudha Kumari, Lekshmy, and Abbas Z. Kouzani. "A Miniaturized Closed-Loop Optogenetic Brain Stimulation Device." Electronics 11, no. 10 (2022): 1591. http://dx.doi.org/10.3390/electronics11101591.

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This paper presents a tetherless and miniaturized closed-loop optogenetic brain stimulation device, designed as a back mountable device for laboratory mice. The device has the ability to sense the biomarkers corresponding to major depressive disorder (MDD) from local field potential (LFP), and produces a feedback signal to control the closed-loop operation after on-device processing of the sensed signals. MDD is a chronic neurological disorder and there are still many unanswered questions about the underlying neurological mechanisms behind its occurrence. Along with other brain stimulation par

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SAFAIE, J., R. GREBE, H. ABRISHAMI MOGHADDAM, and F. WALLOIS. "WIRELESS DISTRIBUTED ACQUISITION SYSTEM FOR NEAR INFRARED SPECTROSCOPY – WDA-NIRS." Journal of Innovative Optical Health Sciences 06, no. 03 (2013): 1350019. http://dx.doi.org/10.1142/s1793545813500193.

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The wireless distributed acquisition system for near infrared spectroscopy (WDA-NIRS) is a portable, ultra-compact, continuous wave (CW) NIRS system. Its main advantage is that it allows continuous synchronized multi-site hemodynamic monitoring. The WDA-NIRS system calculates online changes in hemoglobin concentration based on modified Beer–Lambert law and the tissue oxygenation index based on the spatial-resolved spectroscopy method. It consists of up to seven signal acquisition units, sufficiently small to be easily attached to any part of the body. These units are remotely synchronized by a

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