Towards ideal time window for classifying motor imagery in brain-computer interfaces

  • Vitor Mendes Vilas-Boas Universidade Federal do Pará
  • Vitor da Silva Jorge Universidade Federal do Pará
  • Cleison Daniel Silva Universidade Federal do Pará


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.

Palavras-chave: brain-computer interfaces, ideal time window, machine learning, motor imagery


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VILAS-BOAS, Vitor Mendes; JORGE, Vitor da Silva ; SILVA, Cleison Daniel. Towards ideal time window for classifying motor imagery in brain-computer interfaces. In: SYMPOSIUM ON KNOWLEDGE DISCOVERY, MINING AND LEARNING (KDMILE), 8. , 2020, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 73-80. ISSN 2763-8944. DOI: