SSVEP-based BCI Robotic Car Control System with MATLAB

Collected EEG signals from occipital regions using gold cup electrodes and a Cyton board (250 Hz).

Applied Butterworth filters for noise reduction and extracted key EEG components (4–35 Hz).

Implemented and compared CCA and FFT methods to decode SSVEP frequencies with high accuracy.

Transmitted motion commands via Bluetooth to demonstrate reliable and precise human-machine interaction.

Reference: Li, M.; He, D.; Li, C.; Qi, S. Brain–Computer Interface Speller Based on Steady-State Visual Evoked Potential: A Review Focusing on the Stimulus Paradigm and Performance. Brain Sci. 2021, 11, 450. https://doi.org/10.3390/brainsci11040450