Paper Info
Title
Cross Subject Mental Work Load Classification From Electroencephalographic Signals With Automatic Artifact Rejection And Muscle Pruning
Abstract
Purpose of this study was to understand the effect of automatic muscle pruning of electroencephalograph on cognitive work load prediction. Pruning was achieved using an automatic Independent Component Analysis (ICA) based component classification. Initially, raw data from EEG recording was used for prediction, this result was then compared with mental work load prediction results from muscle-pruned EEG data. This study used Support Vector Machine (SVM) with Linear Kernel for cognitive work load prediction from EEG data. Initial part of the study was to learn a classification model from the whole data, whereas the second part was to learn the model from a set of subjects and predict the mental work load for an unseen subject by the model. The experimental results show that an accuracy of nearly 100% is possible with ICA and automatic pruning based pre-processing. Cross subject prediction significantly improved from a mean accuracy of 54% to 69% for an unseen subject with the pre-processing.
Year
DOI
Venue
2016
10.1007/978-3-319-47103-7_29
BRAIN INFORMATICS AND HEALTH
DocType
Volume
ISSN
Conference
9919
0302-9743
Citations 
PageRank 
References 
0
0.34
6
Authors
7
Name
Order
Citations
PageRank
Sajeev Kunjan100.34
Trent W. Lewis29810.94
T. S. Grummett312.04
David M. W. Powers450067.39
Kenneth J. Pope592.78
Sean P. Fitzgibbon614811.34
John O. Willoughby781.75