Subject-Independent Detection of Yes/No Decisions Using EEG Recordings During Motor Imagery Tasks: A Novel Machine-Learning Approach with Fine-Graded EEG Spectrum

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dc.contributor.authorPenava, Pascal
dc.contributor.authorBrozat, Marie-Louise
dc.contributor.authorZimmermann, Yara
dc.contributor.authorBreitenbach, Johannes
dc.contributor.authorUlrich, Patrick
dc.contributor.authorBuettner, Ricardo
dc.date.accessioned2022-12-27T19:11:28Z
dc.date.available2022-12-27T19:11:28Z
dc.date.issued2023-01-03
dc.description.abstractThe classification of sensorimotor rhythms in electroencephalography signals can enable paralyzed individuals, for example, to make yes/no decisions. In practice, these approaches are hard to implement due to the variability of electroencephalography signals between and within subjects. Therefore, we report a novel and fast machine learning model, meeting the need for efficiency and reliability as well as low calibration and training time. Our model extracts finely graded frequency bands from motor imagery electroencephalography data by using power spectral density and training a random forest algorithm for classification. The goal was to create a non-invasive generalizable method by training the algorithm with subject-independent EEG data. We evaluate our approach using one of the currently largest publicly available electroencephalography datasets. With a balanced accuracy of 73.94%, our novel algorithm outperforms other state-of-the-art non-subject-dependent algorithms.
dc.format.extent10
dc.identifier.doi10.24251/HICSS.2023.496
dc.identifier.isbn978-0-9981331-6-4
dc.identifier.urihttps://hdl.handle.net/10125/103127
dc.language.isoeng
dc.relation.ispartofProceedings of the 56th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectHuman-Computer Interaction in the Digital Economy
dc.subjectbinary decision decoding
dc.subjectbrain–computer interface
dc.subjectelectroencephalogram
dc.subjecthuman-computer interaction
dc.subjectmachine learning
dc.titleSubject-Independent Detection of Yes/No Decisions Using EEG Recordings During Motor Imagery Tasks: A Novel Machine-Learning Approach with Fine-Graded EEG Spectrum
dc.type.dcmitext
prism.startingpage4057

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