An overview of the proposed framework.

<div><p>Electroencephalogram (EEG) signals play a critical role in advancing brain-computer interface (BCI) systems, particularly for detecting motor imagery (MI) movements. However, analysing large volume of EEG datasets faces some challenges due to redundant information, and performanc...

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Bibliographic Details
Main Author:	Taslima Khanam (9739867) (author)
Other Authors:	Siuly Siuly (11027558) (author), Kabir Ahmad (9739864) (author), Hua Wang (13530) (author)
Published:	2025
Subjects:	Biotechnology Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified xlink "> electroencephalogram supporting patient rehabilitation outperformed existing approaches individual subject compared analysing large volume slows system performance algorithms superior performance motor imagery tasks detecting motor imagery framework employs dlrcsp enable rapid identification mi task performance highest accuracy score eeg datasets faces based bci datasets performance degradation highest accuracy applied datasets reduces accuracy accuracy gains time eeg three real third dataset study produces study aims signals play results demonstrate redundant information redundant channels proposed technique new perspective neural network method achieved first dataset feature extraction every subjects critical role correlation coefficients computer interface challenges due bonferroni correction bci applications
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An overview of the proposed framework.

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