Feature extraction of EEG spectrum for Steady-State Visual Evoked Potentials detection

Recent years, a Steady-State Visual Evoked Potential (SSVEP) is adopted as a basis for Brain Computer Interface (BCI)[1]. Various feature extractions and classification techniques are proposed to achieve BCI based on SSVEP. The feature extraction of SSVEP is developed in the frequency domain regardless of the limitation in hardware architecture, i.e. a low power and simple calculation. We introduce here the feature extraction using a spectrum intensity ratio. Results show that the detection ratio reaches 90% by using a spectrum intensity ratio with unsupervised classification. It also indicates the SSVEP is enhanced by proposed feature extraction with second harmonic.