Optimal Selection of EEG Electrodes Using Interval Type-2 Fuzzy-Logic-Based Semiseparating Signaling Game.

This article addresses the noise contamination in spatial filtering of brain responses using a novel signaling game-based approach to the optimal selection of EEG electrodes. The proposed method takes the standard common spatial pattern (CSP) filter as an input and produces an optimal electrode set as output for effective classification of different cognitive tasks. The standard CSP algorithms are highly prone to the inclusion of noise in the EEG data and may select noisy electrodes/signal sources that are redundant for a specific cognitive task which, in turn, may lead to a lower classification accuracy. A lot of literature exists in this area of research, most of which deals with adding the regularization term in the standard CSP algorithm. However, all of these methods lack capturing the uncertainty present in the EEG responses due to intrasession and intersession variations of subjective brain response. The novelty of this article lies in designing the fuzzy signaling game-based approach for optimal electrode selection using an interval type-2 fuzzy set, which can capture both the intrasession and intersession variability of EEG responses acquired from a subject's scalp. Experiments are undertaken over a wide variety of possible cognitive task classification problems which reveal that the proposed method yields superior results in electrode selection with respect to classification accuracy. Statistical tests undertaken using the Friedman test also confirm the superiority of the proposed method over its competitors.

Designing Phase-Sensitive Common Spatial Pattern Filter to Improve Brain-Computer Interfacing.

This paper addresses an interesting problem to model common spatial pattern (CSP) using an objective function employed to segregate EEG signals for a given cognitive task into two classes. The novelty of the present research is to include phase information of the EEG signal along with the amplitude for differentiating class boundaries. Two modified CSP algorithms are proposed in this paper. The first one introduces the composite effect of amplitude and phase angle of the EEG signal in CSP formulation and is solved using Lagrange's multiplier method taking phase information of EEG into account. In the second approach, a novel CSP algorithm is proposed in this paper which has the efficacy of handling the non-linearities hidden in the brain signal, here EEG. Experiments undertaken confirm that the proposed phase-sensitive CSP yields the best performance than their non-phase sensitive counterparts by a large margin with respect to classification accuracy.