Hand Movement Classification Base on EEG Signals using Deep Learning and Dimensional Reduction Technique

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dc.contributor.authorBoonme, Phattraporn
dc.contributor.authorThongserm, Petchanon
dc.contributor.authorArunsuriyasak, Peerachai
dc.contributor.authorPhasukkit, Pattarapong
dc.date.accessioned2026-08-06T10:26:12Z
dc.date.available2026-08-06T10:26:12Z
dc.date.issued2019-11-01
dc.description.abstractThis research is presented the bio-signal activities of arm movements by using deep learning for classification between right-arm and left-arm. It's well-known that Electroencephalography (EEG) shows neural oscillation behaviors in electrical voltage form. Also, Brain-Computer Interface (BCI) is direct communication between neural oscillation and computer to control machines without physical movements. So, this paper aims to present the classification method of EEG signals data to develop a BCI in the future. By using deep learning to classification data is classified into raise the right arm, raise the left arm. And decrease EEG signal data by using Principal Component Analysis (PCA). PCA can reduce the data size of EEG signal from 1000x28 to 28x28. Experimental result of classification has accuracy 90.86% and 94.71%
dc.identifier.citationBmeicon 2019 12th Biomedical Engineering International Conference, 2019
dc.identifier.doi10.1109/BMEiCON47515.2019.8990192
dc.identifier.other2-s2.0-85080048938
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/10315
dc.sourceBmeicon 2019 12th Biomedical Engineering International Conference
dc.subjectBCI
dc.subjectClassification
dc.subjectDCNNs
dc.subjectDeep Learning
dc.subjectEEG
dc.subjectPCA
dc.titleHand Movement Classification Base on EEG Signals using Deep Learning and Dimensional Reduction Technique
dc.typeConference Paper

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