Analysis of Relation between Motor Activity and Imaginary EEG Records

TL;DR

This paper investigates the relationship between actual motor activities and their imagined counterparts using EEG signals, employing high-performance algorithms for feature extraction, selection, and classification to analyze data from 109 subjects.

Contribution

It introduces a comprehensive analysis of motor and imagined EEG signals, utilizing advanced algorithms to improve understanding and classification of motor imagery.

Findings

High classification accuracy achieved for motor and imagery EEG signals

Significant correlation found between actual movements and imagined movements

Effective feature extraction methods enhance EEG analysis

Abstract

Electroencephalography (EEG) signals signals are often used to learn about brain structure and to learn what thinking. EEG signals can be easily affected by external factors. For this reason, they should be applied various pre-process during their analysis. In this study, it is used the EEG signals received from 109 subjects when opening and closing their right or left fists and performing hand and foot movements and imagining the same movements. The relationship between motor activities and imaginary of that motor activities were investigated. Algorithms with high performance rates have been used for feature extraction , selection and classification using the nearest neighbour algorithm.