BioPyC, an Open-Source Python Toolbox for Offline Electroencephalographic and Physiological Signals Classification

Research on brain–computer interfaces (BCIs) has become more democratic in recent decades, and experiments using electroencephalography (EEG)-based BCIs has dramatically increased. The variety of protocol designs and the growing interest in physiological computing require parallel improvements in pr...

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Bibliographic Details
Published in:	Sensors Vol. 21; no. 17; p. 5740
Main Authors:	Appriou, Aurélien, Pillette, Léa, Trocellier, David, Dutartre, Dan, Cichocki, Andrzej, Lotte, Fabien
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 26.08.2021 MDPI
Subjects:	[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI] [INFO.INFO-BT] Computer Science [cs]/Biotechnology [INFO.INFO-NE] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE] [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing Algorithms Animals Artificial Intelligence Biotechnology Boidae Brain Brain research Brain-Computer Interfaces brain–computer interfaces (BCI) Chemical technology Classification Computer Science Datasets Electroencephalography electroencephalography (EEG) Machine learning Neural and Evolutionary Computing physiological signals Physiology Programming languages Python platform Respiration Signal and Image Processing Signal processing Signal Processing, Computer-Assisted TP1-1185 User interface User training Visualization Wheelchairs Workloads electroencephalography (EEG) physiological signals Python platform machine learning signal processing
ISSN:	1424-8220, 1424-8220
Online Access:	Get full text
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