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In a self-paced motor-imagery brain-computer interface (MI-BCI), the onsetsof the MI commands presented in a continuous electroencephalogram (EEG) signalare unknown. To detect these onsets, most self-paced approaches apply a windowfunction on the continuous EEG signal and split it into long segments forfurther analysis. As a result, the system has a high latency. To reduce thesystem latency, we propose an algorithm based on the time series predictionconcept and use the data of the previously received time samples to predict theupcoming time samples. Our predictor is an encoder-decoder (ED) network builtwith long short-term memory (LSTM) units. The onsets of the MI commands aredetected shortly by comparing the incoming signal with the predicted signal.The proposed method is validated on dataset IVc from BCI competition III. Thesimulation results show that the proposed algorithm improves the averageF1-score achieved by the winner of the competition by 26.7% for latenciesshorter than one second.

A self-paced BCI system with low latency for motor imagery onset detection based on time series prediction paradigm