Real-Time Training of Voted Perceptron for Classification of EEG Data
Brain-computer interface (BCI) is a communication system that translates brain activity into commands for a computer or other digital device. BCI applications have strict time constraints for signal processing. Therefore, BCI systems and parts thereof must be considered as real-time systems subject to the requirements for correctness and guaranteed response. The classifier, which maps electroencephalography (EEG) signal features to device control instructions, is the most computationally expensive part of the BCI systems. Most of the modern classification methods produce good results on the benchmark EEG datasets, however their training and classification times are unacceptable for real-time BCI applications. In this paper, we analyse dataset transformations to reduce classifier training time and present a modified version of the Voted Perceptron algorithm subject to the real-time constraint.
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