A subject-specific frequency band selection for efficient BCI- an interval type-2 fuzzy inference system approach

The Common Spatial Pattern (CSP) is an effective algorithm used in EEG based Brain Computer Interface (BCI) to extract discriminative features, however, its effectiveness depends upon the subject-specific frequency bands. Also, the generated features using CSP are non-stationary in nature. In this paper, we propose a Meta-cognitive Interval type-2 Neuro-Fuzzy Inference System to handle non-stationarity in CSP features with recursive band elimination to find subject-specific frequency bands, together known as (McIT2NFIS-RBE). McIT2NFIS uses the non-stationary features generated by CSP as its input and models it as uncertainty using Interval type-2 fuzzy sets in the antecedent of fuzzy rules. The recursive band elimination (RBE) employs the McIT2NFIS training algorithm to recursively eliminate all the features of a band, one at a time. It aims to improve the performance by removing features of a band one at a time, whose elimination will not have any effect on the training performance. The performance of McIT2NFIS-RBE is evaluated using the publicly available dataset-IIa from BCI competition dataset IV [26]. The results highlight the performance of McIT2NFIS-RBE over other algorithms.