A prototype of SSVEP-based BCI for home appliances control

In this research, a prototype of home appliances control system based on steady-state visually evoked potential (SSVEP) is designed. The system is designed using two SSVEP datasets with different characteristics: the first dataset consists eight frequencies within 6-12 Hz, while the second consists frequencies of 8, 14, and 28 Hz. The EEG signal from the datasets is processed using three components: windowed-sinc digital filter for pre-processing, FFT for feature extraction, and SVM for feature classification. Then, the signal processing result is used for controlling three LEDs, which represent the home appliances to be controlled. Based on the test conducted on both datasets, using RBF kernel for SVM results in higher classification accuracy (83.26% and 71.67%) compared to using linear kernel (36.84% and 65%). In addition, the result shows the designed system works best when SSVEP frequencies within low range (i.e. 14 Hz and below) is used.