  In Brain Computer Interface (BCI), data generated from Electroencephalogram
(EEG) is non-stationary with low signal to noise ratio and contaminated with
artifacts. Common Spatial Pattern (CSP) algorithm has been proved to be
effective in BCI for extracting features in motor imagery tasks, but it is
prone to overfitting. Many algorithms have been devised to regularize CSP for
two class problem, however they have not been effective when applied to
multiclass CSP. Outliers present in data affect extracted CSP features and
reduces performance of the system. In addition to this non-stationarity present
in the features extracted from the CSP present a challenge in classification.
We propose a method to identify and remove artifact present in the data during
pre-processing stage, this helps in calculating eigenvectors which in turn
generates better CSP features. To handle the non-stationarity, Self-Regulated
Interval Type-2 Neuro-Fuzzy Inference System (SRIT2NFIS) was proposed in the
literature for two class EEG classification problem. This paper extends the
SRIT2NFIS to multiclass using Joint Approximate Diagonalization (JAD). The
results on standard data set from BCI competition IV shows significant increase
in the accuracies from the current state of the art methods for multiclass
classification.
