Steady-state visual evoked potential-based real-time BCI for smart appliance control

Brain–Computer Interface (BCI) provides an alternative way for humans to communicate with the external environment. BCI systems can be of great help to people with severe motor disabilities who cannot perform normal daily activities. In this paper, we introduce a novel steady-state visual evoked potential (SSVEP)-based brain–computer interface system that control home appliances like electric fan, tube light, etc. Designed system aim is to extract the SSVEP signal and then classify them using PCA. We confirmed the generation of SSVEP frequencies in the online analysis using Fast Fourier Transform. The classification of SSVEP signals is done using Principal Component Analysis.

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