Open AccessTowards ideal time window for classifying motor imagery in brain-computer interfaces
Author(s) -
Vitor Vilas-Boas,
Vitor Da Silva Jorge,
Cleison Daniel Silva
Publication year - 2020
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/kdmile.2020.11961
Subject(s) - motor imagery , computer science , electroencephalography , brain–computer interface , context (archaeology) , artificial intelligence , synchronization (alternating current) , pattern recognition (psychology) , window (computing) , signal (programming language) , sliding window protocol , cognition , selection (genetic algorithm) , speech recognition , psychology , neuroscience , paleontology , computer network , channel (broadcasting) , biology , programming language , operating system
Brain-Computer Interfaces (ICM) allow the control of devices by modulating brain activity. Commonly, when based on motor imagery (IM) these systems use the energy (de)synchronization in the electroencephalogram signal (EEG), voluntarily caused by the individual, to identify and classify their motor intention. Therefore, the EEG segment used in the training of the learning algorithms plays a fundamental role in the description of the characteristics and, consequently, in the recognition of patterns in the signal. In this context, the objective of this work is to demonstrate the correlation between the temporal properties of the input EEG segment and the classification performance of a ICM-IM system. An auxiliary sliding window was used in order to obtain the variation of performance in function of the variation in the time and to support the decision making about the appropriate window. Simulations based on public EEG data point to significant variability in the location and width of the ideal window and suggest the need for individualized selection according to the cognitive patterns of each subject.