Research on Unsupervised Classification Algorithm Based on SSVEP

Abstract

Filter Bank Canonical Correlation Analysis (FBCCA) is used to classify electroencephalography (EEG) signals to overcome insufficient training data for EEG signal classification. This approach is not constrained by the training data or time and also performs unsupervised Steady-State Visual Evoked Potential (SSVEP) classification in a short time, which is easy to extend and optimize. By examining the data set from the Brain–Computer Interface (BCI) contest and comparing it to Canonical Correlation Analysis (CCA) using various parameter settings, the results show that FBCCA carries better classification performance than CCA. When the number of harmonics is 4 and the number of subbands is 5, the identification rate of 40 targets with the frequency difference of 0.2 Hz achieves 88.9%, and the maximum information transfer rate (ITR) achieves 88.64 bits/min, which shows superior compatibility and practicability.