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Relevant bibliographies by topics / Bande EEG

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Journal articles

Academic literature on the topic 'Bande EEG'

Author: Grafiati

Published: 9 March 2023

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Journal articles on the topic "Bande EEG"

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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2

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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3

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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4

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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6

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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8

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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9

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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