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Classification of EEG-Motor imagery based BCI scheme for lower limb using Power Spectrum Density and Linear Discriminant Analysis

human-computer-interaction
algorithms-and-analysis-of-algorithms
brain-computer-interface
computer-vision
data-mining-and-machine-learning

Abstract

Referring to the significant advances in Brain-Computer Interface (BCI) and the significance of its applicability in bio-origin rehabilitation, this paper proposes a classification method for motor imagery-electroencephalogram (EEG) based BCI to generate better classification accuracy for foot movement to improve the reliability and safety of a BCI system for rehabilitation. The EEG data set was classified into idling or foot movement mental states using Linear Discriminant Analysis (LDA) based on the extracted features, which were derived from spectral information using Power Spectrum Density (PSD) as a frequency domain analysis method. The LDA classifier is designed to maximize the distance between the means of the two classes and minimize the inter-class variance. In contrast, PSD is obtained to present the frequency range at which variations are substantial. The BCI Competition IV 2a dataset is used to evaluate the proposed method, and a 10-fold cross-validation method was implemented to prove the classifier's performance. This method achieves a classification accuracy of 97.7%, which confirms the feasibility and effectiveness of the technique.
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