Impact of the baseline temporal selection on the ERD/ERS analysis for Motor Imagery-based BCI.

Motor Imagery-based Brain-Computer Interfaces (MI-BCIs) are neurotechnologies that exploit the modulation of sensorimotor rhythms over the motor cortices, respectively known as Event-Related Desynchronization (ERD) and Synchronization (ERS). The interpretation of ERD/ERS is directly related to the selection of the baseline used to estimate them, and might result in a misleading ERD/ERS visualization. In fact, in BCI paradigms, if two trials are separated by a few seconds, taking a baseline close to the end of the previous trial could result in an over-estimation of the ERD, while taking a baseline too close to the upcoming trial could result in an under-estimation of the ERD.

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 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.