The classification of EEG-based winking signals: A transfer learning and random forest pipeline

The classification of EEG-based winking signals: A transfer learning and random forest pipeline

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Brain Computer-Interface (BCI) technology plays a considerable role in the control of rehabilitation or peripheral devices for stroke patients. This is particularly due to their inability to control such devices from their inherent physical limitations after such an attack. More often than not, the...

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Bibliographic Details
Main Authors:	Abdul Majeed, A.P.P (Author), Jailani, R. (Author), Mahendra Kumar, J.L (Author), Mohd Razman, M.A (Author), Musa, R.M (Author), Rashid, M. (Author), Sulaiman, N. (Author)
Format:	Article
Language:	English
Published:	PeerJ Inc. 2021
Series:	PeerJ
Subjects:	accuracy adult Article artificial neural network behavior classification accuracy comparative effectiveness continuous wavelet transform Continuous wavelet transform convolutional neural network cross validation cumulative scale data classification data mining data processing diagnostic accuracy EEG electroencephalogram electromyography female gray matter human human computer interaction learning algorithm machine learning male mathematical model predictive value pyramidal nerve cell quality of life random forest Random forest receiver operating characteristic sensitivity and specificity signal noise ratio stroke patient training Transfer learning transfer of learning validation study Winking
Online Access:	View Fulltext in Publisher View in Scopus

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