Classification of EEG Signal based on non-Gaussian Neutral Vector

TL;DR

This paper introduces a novel EEG signal classification method using non-Gaussian neutral vector properties of mDWT coefficients, improving accuracy over traditional classifiers in brain-computer interface applications.

Contribution

It proposes a new non-linear transformation of mDWT coefficients based on neutrality assumptions, along with a feature selection strategy, enhancing EEG classification performance.

Findings

Improved classification accuracy with the proposed method.

Transformation into independent scalar coefficients.

Effective feature selection strategy enhances results.

Abstract

In the design of brain-computer interface systems, classification of Electroencephalogram (EEG) signals is the essential part and a challenging task. Recently, as the marginalized discrete wavelet transform (mDWT) representations can reveal features related to the transient nature of the EEG signals, the mDWT coefficients have been frequently used in EEG signal classification. In our previous work, we have proposed a super-Dirichlet distribution-based classifier, which utilized the nonnegative and sum-to-one properties of the mDWT coefficients. The proposed classifier performed better than the state-of-the-art support vector machine-based classifier. In this paper, we further study the neutrality of the mDWT coefficients. Assuming the mDWT vector coefficients to be a neutral vector, we transform them non-linearly into a set of independent scalar coefficients. Feature selection strategy is proposed on the transformed feature domain. Experimental results show that the feature selection strategy helps improving the classification accuracy.