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Home > Archives > Volume 20, No 8 (2022) > Article

DOI: 10.14704/nq.2022.20.8.NQ44704

BRAIN COMPUTER INTERFACE FOR THE CLASSIFICATION OF PARALYSIS VIA MOTOR IMAGERY

CHRISTOPHER PAUL A, KAVITHA M S, BHANU D

Abstract

The Motor Imagery (MI) based Brain Computer Interface (BCI) establishes a direct line of communication between the subject head and an external device. This allows the subject to control the external equipment with their thoughts. The majority of BCIs get their input from the EEG characteristics of a single channel. However, in order for the features that are being provided to have any real value at all, the interdependencies between EEG channels need to be taken into consideration. These interdependencies are proven by an in-depth investigation of the connections in the brain. In this paper, we study various discriminative features from the EEG signals and model a Linear Discriminant Analysis (LDA) classifier for discriminating the paralysis in arms of a human effectively. The simulation is conducted on various datasets of EEG and the results show that the proposed method has higher range of classification accuracy than the other methods.

Keywords

Motor Imagery, discriminative feature, Linear Discriminant Analysis, classification accuracy.

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