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On-chip implementation of ECoG signal data decoding in brain-computer interface

Brain-computer interface (BCI) has been extensively studied as a means of restoring sensorimotor functions. The BCI enables individuals to use their own brain electrical activity signals recorded by electrodes to control an external device through decoding, translating, and actuating. While most state of the art decoding techniques rely on offline analysis, in this work, we showed that brain signal decoding can be performed efficiently using on-chip implementation, which serves as a pathway to development of a real-time BCI system capable of predicting volitional movement intentions. The main building blocks of our decoding architecture are a hardware friendly version of principle component analysis (PCA) and a multilayer perceptron (MLP). Experimental results using an FPGA and publicly available electrocorticography (ECoG) data show that our proposed architecture can predict single finger movements with more than 80% accuracy.