TY - GEN
T1 - Decoding mental states in bistable perception by using source based wavelet features
AU - Sen, Susmita
AU - Watanabe, Katsumi
AU - Bhattacharya, Joydeep
AU - Saha, Goutam
N1 - Funding Information:
ACKNOWLEDGMENT This work is supported by the MHRD, Department of Higher Education, New Delhi, Govt. of India.
Publisher Copyright:
© 2017 IEEE.
PY - 2018/2/2
Y1 - 2018/2/2
N2 - This work presents an efficient application of machine learning and signal processing algorithms in decoding the mental states from brain signals. We decoded two internal mental states, transition and maintenance, representing the process of switching and maintaining a perception in bistable perception, respectively. The underlying sources were reconstructed from the Magnetoencephalogram (MEG) signals recorded from human participants while they were presented with six different conditions of bistable stimuli. We extracted features using complex Morlet wavelet transform that captured the temporal dynamics of large scale brain oscillations in the source domain. The mental states were predicted on a trial-by-Trial basis using Support Vector Machine (SVM) and Artificial Neural Network (ANN) classifiers along with Fisher's ratio based feature selection technique. We achieved accuracies of 75.65% and 74.58% using SVM and ANN classifier, respectively. The analysis also exhibits the involvement of the sources of parietal, temporal and cerebellum areas in characterizing the two mental processes.
AB - This work presents an efficient application of machine learning and signal processing algorithms in decoding the mental states from brain signals. We decoded two internal mental states, transition and maintenance, representing the process of switching and maintaining a perception in bistable perception, respectively. The underlying sources were reconstructed from the Magnetoencephalogram (MEG) signals recorded from human participants while they were presented with six different conditions of bistable stimuli. We extracted features using complex Morlet wavelet transform that captured the temporal dynamics of large scale brain oscillations in the source domain. The mental states were predicted on a trial-by-Trial basis using Support Vector Machine (SVM) and Artificial Neural Network (ANN) classifiers along with Fisher's ratio based feature selection technique. We achieved accuracies of 75.65% and 74.58% using SVM and ANN classifier, respectively. The analysis also exhibits the involvement of the sources of parietal, temporal and cerebellum areas in characterizing the two mental processes.
KW - ANN
KW - Bistable Perception
KW - Decoding
KW - MEG
KW - SVM
KW - Single-Trial Classification
KW - Source Reconstruction
UR - http://www.scopus.com/inward/record.url?scp=85047392618&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85047392618&partnerID=8YFLogxK
U2 - 10.1109/CALCON.2017.8280713
DO - 10.1109/CALCON.2017.8280713
M3 - Conference contribution
AN - SCOPUS:85047392618
T3 - 2017 IEEE Calcutta Conference, CALCON 2017 - Proceedings
SP - 144
EP - 149
BT - 2017 IEEE Calcutta Conference, CALCON 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Calcutta Conference, CALCON 2017
Y2 - 2 December 2017 through 3 December 2017
ER -