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
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