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Journal articles on the topic 'Bande EEG'

Author: Grafiati

Published: 9 March 2023

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1

Micoulaud Franchi, J. A. "Le neurofeedback comme outil de compréhension et de régulation de l’attention." European Psychiatry 28, S2 (2013): 13. http://dx.doi.org/10.1016/j.eurpsy.2013.09.030.

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Le « neurofeedback » est une technique de biofeedback, appelée également « EEG biofeedback », utilisant l’enregistrement électroencéphalographique (EEG). Cette technique existe depuis près de 30 ans. Deux grands types de protocoles de neurofeedback en fonction du type de traitement en temps réel réalisé sur le signal EEG sont retrouvés. Dans le premier, la puissance spectrale d’une bande fréquentielle EEG en regard d’une région cérébrale est calculée. Il peut être par exemple demandé au sujet d’augmenter la puissance spectrale de la bande bêta ou de diminuer celle de la bande thêta enregistrée

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Espa, F., B. Ondzé, M. Billiard, and A. Besset. "Analyse de la puissance spectrale de la bande delta-EEG et des modifications de l'activité respiratoire au cours du sommeil chez le sujet atteint de somnambulisme ou de terreurs nocturnes." Neurophysiologie Clinique/Clinical Neurophysiology 26, no. 6 (1996): 430–31. http://dx.doi.org/10.1016/s0987-7053(97)89173-3.

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Talebi, Shawhin, John Waczak, Bharana A. Fernando, Arjun Sridhar, and David J. Lary. "Data-Driven EEG Band Discovery with Decision Trees." Sensors 22, no. 8 (2022): 3048. http://dx.doi.org/10.3390/s22083048.

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Electroencephalography (EEG) is a brain imaging technique in which electrodes are placed on the scalp. EEG signals are commonly decomposed into frequency bands called delta, theta, alpha, and beta. While these bands have been shown to be useful for characterizing various brain states, their utility as a one-size-fits-all analysis tool remains unclear. The goal of this work is to outline an objective strategy for discovering optimal EEG bands based on signal power spectra. A two-step data-driven methodology is presented for objectively determining the best EEG bands for a given dataset. First,

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Martin-Loeches, Manuel, Pedro Gil, and Francisco José Rubia. "Two-Hz wide EEG bands in Alzheimer's disease." Biological Psychiatry 33, no. 3 (1993): 153–59. http://dx.doi.org/10.1016/0006-3223(93)90134-y.

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Jatupaiboon, Noppadon, Setha Pan-ngum, and Pasin Israsena. "Real-Time EEG-Based Happiness Detection System." Scientific World Journal 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/618649.

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We propose to use real-time EEG signal to classify happy and unhappy emotions elicited by pictures and classical music. We use PSD as a feature and SVM as a classifier. The average accuracies of subject-dependent model and subject-independent model are approximately 75.62% and 65.12%, respectively. Considering each pair of channels, temporal pair of channels (T7 and T8) gives a better result than the other area. Considering different frequency bands, high-frequency bands (Beta and Gamma) give a better result than low-frequency bands. Considering different time durations for emotion elicitation

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Mendoza-Sánchez, Sandra, Alvaro Murillo-Garcia, Juan Luis Leon-Llamas, Jesús Sánchez-Gómez, Narcis Gusi, and Santos Villafaina. "Neurophysiological Response of Adults with Cerebral Palsy during Inclusive Dance with Wheelchair." Biology 11, no. 11 (2022): 1546. http://dx.doi.org/10.3390/biology11111546.

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A total of 16 adults with cerebral palsy (age = 37.50 (7.78)) participated in this cross-sectional study. The electroencephalographic (EEG) data were recorded under three conditions: (1) baseline; (2) while listening to music; (3) while performing inclusive dance choreography with wheelchair. EEG data was banded into theta (4–7 Hz), alpha (8–12 Hz), and beta (13–30 Hz). Significantly higher values of theta, alpha, and beta bands were found in dance conditions than in the baseline. Significant differences between baseline and listening to music conditions were not found in any of the power spec

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Huang, Hui, Jianhai Zhang, Li Zhu, et al. "EEG-Based Sleep Staging Analysis with Functional Connectivity." Sensors 21, no. 6 (2021): 1988. http://dx.doi.org/10.3390/s21061988.

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Sleep staging is important in sleep research since it is the basis for sleep evaluation and disease diagnosis. Related works have acquired many desirable outcomes. However, most of current studies focus on time-domain or frequency-domain measures as classification features using single or very few channels, which only obtain the local features but ignore the global information exchanging between different brain regions. Meanwhile, brain functional connectivity is considered to be closely related to brain activity and can be used to study the interaction relationship between brain areas. To exp

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Curio, Gabriel. "Ain’t No Rhythm Fast Enough: EEG Bands Beyond Beta." Journal of Clinical Neurophysiology 17, no. 4 (2000): 339–40. http://dx.doi.org/10.1097/00004691-200007000-00001.

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Marecek, R., M. Lamos, M. Mikl, et al. "What can be found in scalp EEG spectrum beyond common frequency bands. EEG–fMRI study." Journal of Neural Engineering 13, no. 4 (2016): 046026. http://dx.doi.org/10.1088/1741-2560/13/4/046026.

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10

Cruz-Rodríguez, Andrés M., and Hernán Sánchez-Machet. "Prótesis de mano controlada con señales EEG." MOMENTO, no. 63 (July 9, 2021): 34–51. http://dx.doi.org/10.15446/mo.n63.96407.

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En este trabajo, se describe cómo las ondas cerebrales producidas en el lóbulo lateral izquierdo del cerebro humano y detectadas con un auricular NeuroSky MindFlex fueron usadas para controlar voluntariamente una prótesis de mano. En primera instancia, las señales EEG (electroencefalográficas) son detectadas por una BCI (Interfaz de control cerebral) y tras ser analizado con la ayuda de un microcontrolador ARDUINO su espectro de frecuencias es dividido en rangos Alfa, Beta, Theta y Gamma. Como resultado del análisis EEG, se encuentra que el parpadeo voluntario afecta principalmente los v

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Harada, Hirofumi, Kimio Shiraishi, Toshihiko Kato, and Toyoji Soda. "Coherence analysis of EEG changes during odour stimulation in humans." Journal of Laryngology & Otology 110, no. 7 (1996): 652–56. http://dx.doi.org/10.1017/s0022215100134528.

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AbstractIn a pilot study, EEG changes during odour administration were evaluated by coherence analysis. Ten normal adults were studied. Simultaneous recordings of 16 EEG channels with, and without, odour administration were stored on magnetic tape for further processing. EEG signals were analysed using a signal analyser. Coherence spectra were calculated between all possible channel pairs on the scalp. The amount of data was reduced by extracting broad band coherence values for five frequency bands: delta (2–3.9 Hz), theta (4–7.9 Hz), alpha 1 (8–9.9 Hz), alpha 2 (10–12.9 Hz), and beta 1 (13–17

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Ma, Yue, Qing Liu, and Liu Yang. "Exploring Seafarers’ Workload Recognition Model with EEG, ECG and Task Scenarios’ Complexity: A Bridge Simulation Study." Journal of Marine Science and Engineering 10, no. 10 (2022): 1438. http://dx.doi.org/10.3390/jmse10101438.

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Seafarers are prone to reduce behavioral reliability under high workloads, resulting in human errors and accidents. To explore the changes in seafarers’ workload and physiological activities under complex task conditions, a bridge simulator experiment was conducted to collect the EEG and ECG data of 23 seafarers. The power in different EEG sub-bands was extracted from a one-channel EEG acquisition headset employed by Welch’s method and ratio processing. The features such as root mean square of RR interval difference (RMSSD) were extracted from ECG. Then, an improved seafarer workload recogniti

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Bellegdi, Sameh A., and Samer M. A. Arafat. "Automatic Detection of Epilepsy Using EEG Energy and Frequency Bands." International Journal of Applied Mathematics, Electronics and Computers 1, SpecialIssue (2017): 36–41. http://dx.doi.org/10.18100/ijamec.2017specialissue30468.

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14

Elgandelwar, Sachin M., and Vinayak K. Bairagi. "Power analysis of EEG bands for diagnosis of Alzheimer disease." International Journal of Medical Engineering and Informatics 13, no. 5 (2021): 376. http://dx.doi.org/10.1504/ijmei.2021.117728.

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Bairagi, Vinayak K., and Sachin M. Elgandelwar. "Power analysis of EEG bands for diagnosis of Alzheimer disease." International Journal of Medical Engineering and Informatics 13, no. 5 (2021): 376. http://dx.doi.org/10.1504/ijmei.2021.10041112.

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Mann, K., P. Bäcker, and J. Röschke. "Dynamical properties of the sleep eeg in different frequency bands." International Journal of Neuroscience 73, no. 3-4 (1993): 161–69. http://dx.doi.org/10.3109/00207459308986666.

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MARTIS, ROSHAN JOY, JEN HONG TAN, CHUA KUANG CHUA, TOO CHEAH LOON, SHARON WAN JIE YEO, and LOUIS TONG. "EPILEPTIC EEG CLASSIFICATION USING NONLINEAR PARAMETERS ON DIFFERENT FREQUENCY BANDS." Journal of Mechanics in Medicine and Biology 15, no. 03 (2015): 1550040. http://dx.doi.org/10.1142/s0219519415500402.

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Epilepsy is a chronic neurological disorder with considerable incidence and affects the population everywhere in the world. It occurs due to recurrent unprovoked seizures which can be noninvasively diagnosed using electroencephalograms (EEGs) which are the neuronal electrical activity recorded on the scalp. The EEG signal is highly random, nonlinear, nonstationary and non-Gaussian in nature. The nonlinear features characterize the EEG more accurately than linear models. EEG comprsises of different activities like delta, theta, lower alpha, upper alpha, lower beta, upper beta and lower gamma wh

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Khurana, Vaishali, Pradeep Kumar, Rajkumar Saini, and Partha Pratim Roy. "EEG based word familiarity using features and frequency bands combination." Cognitive Systems Research 49 (June 2018): 33–48. http://dx.doi.org/10.1016/j.cogsys.2017.11.003.

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19

Wilson, Glenn F., and Thomas Hankins. "Eeg and Subjective Measures of Private Pilot Workload." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 38, no. 19 (1994): 1322–25. http://dx.doi.org/10.1177/154193129403801916.

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Complex systems can place high levels of mental demand on human operators and methods of assessing these demands are needed. Subjective and performance metrics are typically employed while psychophysiological assessment has been used to a more limited extent. In this study, civilian pilots flew a single engine propeller aircraft on a flight profile designed to produce several levels of cognitive workload using VFR and IFR conditions. Subjective and brain wave (EEG) measures were used to assess mental workload. EEG theta band activity was sensitive to a wider range of workload levels and was mo

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Shen, Fangyao, Yong Peng, Wanzeng Kong, and Guojun Dai. "Multi-Scale Frequency Bands Ensemble Learning for EEG-Based Emotion Recognition." Sensors 21, no. 4 (2021): 1262. http://dx.doi.org/10.3390/s21041262.

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Emotion recognition has a wide range of potential applications in the real world. Among the emotion recognition data sources, electroencephalography (EEG) signals can record the neural activities across the human brain, providing us a reliable way to recognize the emotional states. Most of existing EEG-based emotion recognition studies directly concatenated features extracted from all EEG frequency bands for emotion classification. This way assumes that all frequency bands share the same importance by default; however, it cannot always obtain the optimal performance. In this paper, we present

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Aspiotis, Vasileios, Andreas Miltiadous, Konstantinos Kalafatakis, et al. "Assessing Electroencephalography as a Stress Indicator: A VR High-Altitude Scenario Monitored through EEG and ECG." Sensors 22, no. 15 (2022): 5792. http://dx.doi.org/10.3390/s22155792.

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Over the last decade, virtual reality (VR) has become an increasingly accessible commodity. Head-mounted display (HMD) immersive technologies allow researchers to simulate experimental scenarios that would be unfeasible or risky in real life. An example is extreme heights exposure simulations, which can be utilized in research on stress system mobilization. Until recently, electroencephalography (EEG)-related research was focused on mental stress prompted by social or mathematical challenges, with only a few studies employing HMD VR techniques to induce stress. In this study, we combine a stat

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Barriga-Paulino, Catarina I., Angelica B. Flores, and Carlos M. Gómez. "Developmental Changes in the EEG Rhythms of Children and Young Adults." Journal of Psychophysiology 25, no. 3 (2011): 143–58. http://dx.doi.org/10.1027/0269-8803/a000052.

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This study analyzed the developmental trends of brain rhythms in a group of children and a group of young adults. Principal component analysis (PCA), ANOVA, as well as correlational and topographical analyses were applied to the power spectral density of spontaneous electroencephalography (EEG). Absolute and relative power data were analyzed. The PCA analysis allowed to define three sources of variability related to the classical EEG rhythms. The absolute power results showed that children have higher spectral power than young adults in all frequency bands. Relative power demonstrated that chi

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Miladinović, Aleksandar, Miloš Ajčević, Pierpaolo Busan, et al. "EEG changes and motor deficits in Parkinson’s disease patients: Correlation of motor scales and EEG power bands." Procedia Computer Science 192 (2021): 2616–23. http://dx.doi.org/10.1016/j.procs.2021.09.031.

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24

You, Shingchern D. "Classification of Relaxation and Concentration Mental States with EEG." Information 12, no. 5 (2021): 187. http://dx.doi.org/10.3390/info12050187.

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In this paper, we study the use of EEG (Electroencephalography) to classify between concentrated and relaxed mental states. In the literature, most EEG recording systems are expensive, medical-graded devices. The expensive devices limit the availability in a consumer market. The EEG signals are obtained from a toy-grade EEG device with one channel of output data. The experiments are conducted in two runs, with 7 and 10 subjects, respectively. Each subject is asked to silently recite a five-digit number backwards given by the tester. The recorded EEG signals are converted to time-frequency repr

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Pastor, Jesús, Lorena Vega-Zelaya, and Elena Martín Abad. "Specific EEG Encephalopathy Pattern in SARS-CoV-2 Patients." Journal of Clinical Medicine 9, no. 5 (2020): 1545. http://dx.doi.org/10.3390/jcm9051545.

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We used quantified electroencephalography (qEEG) to define the features of encephalopathy in patients released from the intensive care unit after severe illness from COVID-19. Artifact-free 120–300 s epoch lengths were visually identified and divided into 1 s windows with 10% overlap. Differential channels were grouped by frontal, parieto-occipital, and temporal lobes. For every channel and window, the power spectrum was calculated and used to compute the area for delta (0–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), and beta (13–30 Hz) bands. Furthermore, Shannon’s spectral entropy (SSE) and synch

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Wang, W. W., J. C. Li, and X. Wu. "Quantitative EEG Effects of Topiramate." Clinical Electroencephalography 34, no. 2 (2003): 87–92. http://dx.doi.org/10.1177/155005940303400208.

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Objective: The study is to invesigate the effect of topiramate (TPM) on EEG by means of quantitative pharmacoelectroencephalography (QPEEG). Methods: One dose of TPM was administrated to epileptics and healthy adults. The EEG samples were obtained prior to and at regular intervals within the 24 hours following the administration of TPM. The EEG activity was processed with power spectral analysis. Results: The power of slow wave, alpha 1 bands and total power increased after the administration of TPM, the power or slow wave in both occipital areas, and the total power of all scalp areas also in

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Reeves, Roy R., Frederick A. Struve, and Gloria Patrick. "The Effects of Donepezil on Quantitative EEG in Patients with Alzheimer's Disease." Clinical Electroencephalography 33, no. 2 (2002): 93–96. http://dx.doi.org/10.1177/155005940203300209.

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Donepezil is a cholinesterase inhibitor which has been previously shown to affect the cognitive evoked potentials (EPs) of patients with Alzheimer's Disease (AD) during treatment with the drug. The purpose of this study was to determine the effect of treatment with donepezil 5 mg daily for 1 month on quantitative EEG (QEEG) in patients with AD. Treatment was associated with no significant differences between the pre- and post-treatment QEEGs for (1) absolute power (all four frequency bands), (2) percent relative power (all four frequency bands), (3) total mean frequency, (4) mean frequency for

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Bhole, Leena, and Maya Ingle. "Estimating Range and Relationship of EEG Frequency Bands for Emotion Recognition." International Journal of Computer Applications 178, no. 13 (2019): 16–21. http://dx.doi.org/10.5120/ijca2019918896.

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Abdulhameed, M. K., M. S. Mohamad Isa, Z. Zakaria, et al. "Novel design of triple bands EBG." TELKOMNIKA (Telecommunication Computing Electronics and Control) 17, no. 4 (2019): 1683. http://dx.doi.org/10.12928/telkomnika.v17i4.12616.

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Tian, Geliang, and Yue Liu. "Simple Convolutional Neural Network for Left-Right Hands Motor Imagery EEG Signals Classification." International Journal of Cognitive Informatics and Natural Intelligence 13, no. 3 (2019): 36–49. http://dx.doi.org/10.4018/ijcini.2019070103.

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This article proposes a classification method of two-class motor imagery electroencephalogram (EEG) signals based on convolutional neural network (CNN), in which EEG signals from C3, C4 and Cz electrodes of publicly available BCI competition IV dataset 2b were used to test the performance of the CNN. The authors investigate two similar CNNs: a single-input CNN with a form of 2-dimensional input from short time Fourier transform (STFT) combining time, frequency and location information, and a multiple-input CNN with 3-dimensional input which processes the electrodes as an independent dimension.

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Ayodele, Kayode P., Wisdom O. Ikezogwo, and Anthony A. Osuntuyi. "Empirical Characterization of the Temporal Dynamics of EEG Spectral Components." International Journal of Online and Biomedical Engineering (iJOE) 16, no. 15 (2020): 80. http://dx.doi.org/10.3991/ijoe.v16i15.16663.

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The properties of time-domain electroencephalographic data have been studied extensively. There has however been no attempt to characterize the temporal evolution of resulting spectral components when successive segments of electroencephalographic data are decomposed. We analysed resting-state scalp electroencephalographic data from 23 subjects, acquired at 256 Hz, and transformed using 64-point Fast Fourier Transform with a Hamming window. KPSS and Nason tests were administered to study the trend- and wide sense stationarity respectively of the spectral components. Their complexities were est

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Rampp, Stefan, Martin Kaltenhäuser, Nadia Müller-Voggel, et al. "MEG Node Degree for Focus Localization: Comparison with Invasive EEG." Biomedicines 11, no. 2 (2023): 438. http://dx.doi.org/10.3390/biomedicines11020438.

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Epilepsy surgery is a viable therapy option for patients with pharmacoresistant focal epilepsies. A prerequisite for postoperative seizure freedom is the localization of the epileptogenic zone, e.g., using electro- and magnetoencephalography (EEG/MEG). Evidence shows that resting state MEG contains subtle alterations, which may add information to the workup of epilepsy surgery. Here, we investigate node degree (ND), a graph-theoretical parameter of functional connectivity, in relation to the seizure onset zone (SOZ) determined by invasive EEG (iEEG) in a consecutive series of 50 adult patients

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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 (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 t

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Galbraith, Gary C., and Eugene H. Wong. "Moment Analysis of Eeg Amplitude Histograms and Spectral Analysis: Relative Classification of Several Behavioral Tasks." Perceptual and Motor Skills 76, no. 3 (1993): 859–66. http://dx.doi.org/10.2466/pms.1993.76.3.859.

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Previous studies indicate that EEG amplitude probability density functions are Gaussian (normal) during rest and non-Gaussian during performance of mental tasks. In the present study we compared measures of normality, including higher central moments (e.g., skewness, kurtosis) and relative spectral power, to classify data sampled from several different behavioral tasks (resting eyes closed and mental arithmetic). Analysis shows significant classification in 22 of 25 subjects, based upon a total of 46 EEG variables. However, only two of these variables involved Gaussian properties of the amplit

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Laport, Francisco, Adriana Dapena, Paula M. Castro, Francisco J. Vazquez-Araujo, and Daniel Iglesia. "A Prototype of EEG System for IoT." International Journal of Neural Systems 30, no. 07 (2020): 2050018. http://dx.doi.org/10.1142/s0129065720500185.

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In this work, we develop open source hardware and software for eye state classification and integrate it with a protocol for the Internet of Things (IoT). We design and build the hardware using a reduced number of components and with a very low-cost. Moreover, we propose a method for the detection of open eyes (oE) and closed eyes (cE) states based on computing a power ratio between different frequency bands of the acquired signal. We compare several real- and complex-valued transformations combined with two decision strategies: a threshold-based method and a linear discriminant analysis. Simu

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Fernández-Bouzas, Antonio, Thalía Harmony, Thalía Fernández, et al. "Sources of Abnormal EEG Activity in Spontaneous Intracerebral Hemorrhage." Clinical Electroencephalography 33, no. 2 (2002): 70–76. http://dx.doi.org/10.1177/155005940203300205.

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This report describes the results obtained with EEG source analysis in the frequency domain (FD-VARETA), in 14 patients with brain hemorrhages; 6 hemorrhages were located in the putaminal region, 1 was mesencephalic and 7 were lobar cerebral hemorrhages. Our goal was to evaluate FD-VARETA accuracy for the localization of fast growth expansive brain lesions. FD-VARETA produces brain electromagnetic tomography images of EEG sources in every frequency. The location of the most abnormal or the maximum Z value across all frequencies was compared with the location of spontaneous hemorrhages in compu

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Silva, Joana, A. Martins Da Silva, and Luís Coelho. "Quantification of EEG Brain Activity in the Self-Paced Regime." U.Porto Journal of Engineering 4, no. 1 (2018): 1–8. http://dx.doi.org/10.24840/2183-6493_004.001_0001.

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The processing of motor, sensory and cognitive information by the brain can result in changes of the electroencephalogram (EEG) by Event Related Desynchronization (ERD) or Event Related Synchronization (ERS). The first one concerns a decrease in the amplitude of a rhythmic activity while the second corresponds to its increase. The analysis of these two phenomena in specific frequency bands - alpha (8-13 Hz) and beta (14-30 Hz) - allows the understanding of the cerebral activity. This study focuses on the quantification of cerebral activity by determining the ERD and ERS on the referred band, i

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Cui, Dong, Jing Liu, Zhijie Bian, Qiuli Li, Lei Wang, and Xiaoli Li. "Cortical Source Multivariate EEG Synchronization Analysis on Amnestic Mild Cognitive Impairment in Type 2 Diabetes." Scientific World Journal 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/523216.

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Is synchronization altered in amnestic mild cognitive impairment (aMCI) and normal cognitive functions subjects in type 2 diabetes mellitus (T2DM)? Resting eye-closed EEG data were recorded in 8 aMCI subjects and 11 age-matched controls in T2DM. Three multivariate synchronization algorithms (S-estimator (S), synchronization index (SI), and global synchronization index (GSI)) were used to measure the synchronization in five ROIs of sLORETA sources for seven bands. Results showed that aMCI group had lower synchronization values than control groups in parietal delta and beta2 bands, temporal delt

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Moradi, Narges, Pierre LeVan, Burak Akin, Bradley G. Goodyear, and Roberto C. Sotero. "Holo-Hilbert spectral-based noise removal method for EEG high-frequency bands." Journal of Neuroscience Methods 368 (February 2022): 109470. http://dx.doi.org/10.1016/j.jneumeth.2021.109470.

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Choong, W. Y., W. Khairunizam, W. A. Mustafa, et al. "Correlation Analysis of Emotional EEG In Alpha, Beta and Gamma Frequency Bands." Journal of Physics: Conference Series 1997, no. 1 (2021): 012029. http://dx.doi.org/10.1088/1742-6596/1997/1/012029.

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Qu, Hongquan, Zhanli Fan, Shuqin Cao, Liping Pang, Hao Wang, and Jie Zhang. "A Study on Sensitive Bands of EEG Data under Different Mental Workloads." Algorithms 12, no. 7 (2019): 145. http://dx.doi.org/10.3390/a12070145.

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Electroencephalogram (EEG) signals contain a lot of human body performance information. With the development of the brain–computer interface (BCI) technology, many researchers have used the feature extraction and classification algorithms in various fields to study the feature extraction and classification of EEG signals. In this paper, the sensitive bands of EEG data under different mental workloads are studied. By selecting the characteristics of EEG signals, the bands with the highest sensitivity to mental loads are selected. In this paper, EEG signals are measured in different load flight

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Mann, K., and J. Röschke. "Different Phase Relationships Between EEG Frequency Bands During NREM and REM Sleep." Sleep 20, no. 9 (1997): 753–56. http://dx.doi.org/10.1093/sleep/20.9.753.

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Podlipsky, Ilana, Eti Ben-Simon, Talma Hendler, and Nathan Intrator. "Robust modeling based on optimized EEG bands for functional brain state inference." Journal of Neuroscience Methods 203, no. 2 (2012): 377–85. http://dx.doi.org/10.1016/j.jneumeth.2011.10.015.

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Spironelli, Chiara, Jessica Busenello, and Alessandro Angrilli. "Supine posture inhibits cortical activity: Evidence from Delta and Alpha EEG bands." Neuropsychologia 89 (August 2016): 125–31. http://dx.doi.org/10.1016/j.neuropsychologia.2016.06.015.

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Fabio, Rosa Angela, Lucia Billeci, Giulia Crifaci, Emilia Troise, Gaetano Tortorella, and Giovanni Pioggia. "Cognitive training modifies frequency EEG bands and neuropsychological measures in Rett syndrome." Research in Developmental Disabilities 53-54 (June 2016): 73–85. http://dx.doi.org/10.1016/j.ridd.2016.01.009.

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Cozac, V. V., J. G. Bogaarts, M. Chaturvedi, et al. "P 128 Olfactory deficits and the EEG-frequency bands in Parkinson’s disease." Clinical Neurophysiology 128, no. 10 (2017): e391. http://dx.doi.org/10.1016/j.clinph.2017.06.201.

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Demir, Sait, and İlker Türker. "Arithmetic success and gender-based characterization of brain connectivity across EEG bands." Biomedical Signal Processing and Control 64 (February 2021): 102222. http://dx.doi.org/10.1016/j.bspc.2020.102222.

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LI, Chunchun, Jingyao GAO, Xiaoqin WANG, Gongwu WANG, and Jun CAO. "Effects of Different Extinction for Morphine-CPP on Hippocampal EEG Power Spectrum in Mice." Wuhan University Journal of Natural Sciences 27, no. 3 (2022): 265–72. http://dx.doi.org/10.1051/wujns/2022273265.

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The relationship between hippocampal electroencephalogram (EEG) power spectrum and the extinction of addiction memory was investigated. Forty KM mice (Kunming mice) that had successfully established morphine (MOR)-conditioned place preference (CPP) were divided into four groups: saline-training extinction (SAL-TE), SAL-natural extinction (SAL-NE), MOR-TE, MOR-NE, for extinction treatment and EEG recording in the dorsal and ventral hippocampus (DH/VH). Results show that the CPP score of MOR-TE significantly decreased and the total, β and γ bands power spectrum of MOR-TE was suppressed. Notably,

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Liu, Ye, Qibin Zhao, and Liqing Zhang. "Uncorrelated Multiway Discriminant Analysis for Motor Imagery EEG Classification." International Journal of Neural Systems 25, no. 04 (2015): 1550013. http://dx.doi.org/10.1142/s0129065715500136.

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Motor imagery-based brain–computer interfaces (BCIs) training has been proved to be an effective communication system between human brain and external devices. A practical problem in BCI-based systems is how to correctly and efficiently identify and extract subject-specific features from the blurred scalp electroencephalography (EEG) and translate those features into device commands in order to control external devices. In real BCI-based applications, we usually define frequency bands and channels configuration that related to brain activities beforehand. However, a steady configuration usuall

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Theodoropoulou, A., S. Tei, D. Lehmann, P. L. Faber, F. Schlegel, and P. Milz. "EEG frequency band sloreta sources during mental arithmetic compared to resting." European Psychiatry 26, S2 (2011): 945. http://dx.doi.org/10.1016/s0924-9338(11)72650-5.

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IntroductionArithmetic reportedly involves left parietal areas.ObjectivesTo test this in independent groups of healthy persons.AimsWhich brain regions are activated / inhibited during mental arithmetic compared to task-free resting?MethodsWe examined four independent groups of healthy adults (N = 15, 14, 14, 23, respectively) during simple arithmetic (continuous subtraction of 7) and task-free resting before and after arithmetic, all with closed eyes. Multichannel head surface EEG (19–58 channels) was continually recorded, then recomputed (using sLORETA functional tomography) into current dens

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