Automated electroencephalogram artifacts removal and cognitive task classification methods for edge devices implementation

Abstract

Finally, the performance evaluation of the newlineproposed on-device implementations on two public EEG databases and real-time recorded newlinedatabase indicates that high performance is achieved with low power consumption and newlinelatency in output prediction. Hence, the proposed on-device implementations can be used newlinein BCI and other resource-constrained environments. newline