BioPyC, an Open-Source Python Toolbox for Offline Electroencephalographic and Physiological Signals Classification
| Autor: | Fabien Lotte, Aurélien Appriou, Dan Dutartre, Léa Pillette, Andrzej Cichocki, David Trocellier |
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| Přispěvatelé: | Popular interaction with 3d content (Potioc), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), RIKEN Center for Brain Science [Wako] (RIKEN CBS), RIKEN - Institute of Physical and Chemical Research [Japon] (RIKEN), Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria), Skolkovo Institute of Science and Technology [Moscow] (Skoltech), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
OpenVibe
Computer science 0206 medical engineering TP1-1185 02 engineering and technology [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE] Electroencephalography Machine learning computer.software_genre Biochemistry Python platform Article physiological signals [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] Analytical Chemistry 03 medical and health sciences 0302 clinical medicine [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing brain–computer interfaces (BCI) medicine Animals Biosignal electroencephalography (EEG) [INFO.INFO-BT]Computer Science [cs]/Biotechnology Electrical and Electronic Engineering signal processing Instrumentation Protocol (object-oriented programming) Graphical user interface computer.programming_language Brain–computer interface Signal processing medicine.diagnostic_test business.industry Chemical technology Brain Signal Processing Computer-Assisted Python (programming language) 020601 biomedical engineering Atomic and Molecular Physics and Optics Boidae machine learning Brain-Computer Interfaces Artificial intelligence business computer Algorithms 030217 neurology & neurosurgery |
| Zdroj: | Sensors Volume 21 Issue 17 MDPI Sensors Sensors (Basel, Switzerland) Sensors, Vol 21, Iss 5740, p 5740 (2021) Sensors, 2021, 21, ⟨10.3390/s21175740⟩ Sensors, MDPI, 2021, 21, ⟨10.3390/s21175740⟩ |
| ISSN: | 1424-8220 |
| DOI: | 10.3390/s21175740 |
| Popis: | International audience; 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 processing and classification of both EEG signals and bio signals, such as electrodermal activity (EDA), heart rate (HR) or breathing. If some EEG-based analysis tools are already available for online BCIs with a number of online BCI platforms (e.g., BCI2000 or OpenViBE), it remains crucial to perform offline analyses in order to design, select, tune, validate and test algorithmsbefore using them online. Moreover, studying and comparing those algorithms usually requires expertise in programming, signal processing and machine learning, whereas numerous BCI researchers come from other backgrounds with limited or no training in such skills. Finally, existing BCI toolboxes are focused on EEG and other brain signals but usually do not include processing tools for other bio signals. Therefore, in this paper, we describe BioPyC, a free, open-source and easy-to-use Python platform for offline EEG and biosignal processing and classification. Based on an intuitive and well-guided graphical interface, four main modules allow the user to follow the standard steps of the BCI process without any programming skills: (1) reading different neurophysiological signal data formats, (2) filtering and representing EEG and bio signals, (3) classifying them, and (4) visualizing and performing statistical tests on the results. We illustrate BioPyC use on four studies, namely classifying mental tasks, the cognitive workload, emotions and attention states from EEG signals. |
| Databáze: | OpenAIRE |
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