Analysis of Relation between Motor Activity and Imaginary EEG Records
This work addresses the challenge of analyzing EEG signals affected by external factors for brain-computer interface applications, but it is incremental as it applies existing methods to new data.
The study investigated the relationship between actual motor activities and their imagined counterparts using EEG signals from 109 subjects, achieving high performance rates in feature extraction, selection, and classification with the nearest neighbor algorithm.
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.