Version 1
: Received: 19 June 2021 / Approved: 21 June 2021 / Online: 21 June 2021 (11:39:31 CEST)
Version 2
: Received: 29 June 2021 / Approved: 6 July 2021 / Online: 6 July 2021 (12:42:59 CEST)
Prakash, B.; Baboo, G.K.; Baths, V. A Novel Approach to Learning Models on EEG Data Using Graph Theory Features—A Comparative Study. Big Data and Cognitive Computing 2021, 5, 39, doi:10.3390/bdcc5030039.
Prakash, B.; Baboo, G.K.; Baths, V. A Novel Approach to Learning Models on EEG Data Using Graph Theory Features—A Comparative Study. Big Data and Cognitive Computing 2021, 5, 39, doi:10.3390/bdcc5030039.
Prakash, B.; Baboo, G.K.; Baths, V. A Novel Approach to Learning Models on EEG Data Using Graph Theory Features—A Comparative Study. Big Data and Cognitive Computing 2021, 5, 39, doi:10.3390/bdcc5030039.
Prakash, B.; Baboo, G.K.; Baths, V. A Novel Approach to Learning Models on EEG Data Using Graph Theory Features—A Comparative Study. Big Data and Cognitive Computing 2021, 5, 39, doi:10.3390/bdcc5030039.
Abstract
Functional Connectivity analysis using Electroencephalography signals is a common 2 practice. The EEG signals are converted to networks by transforming the signals into a correlation 3 matrix and analyzing the resulting networks. Here, four learning models, namely, Logistic Regres 4 sion, Random Forest, Support Vector Machine, and Recurrent Neural Networks, are implemented 5 on the correlation matrix data to classify them either on their psychometric assessment or the 6 effect of therapy. The classifications based on RNN provided higher accuracy( 74-88%) compared 7 to the other three models( 50-78%). The use of a correlation matrix, instead of using individual 8 graph features provides an initial test of the data. When compared with the time-resolved correlation 9 matrix it provided 4-5% higher accuracy.
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
