Decoding mental states in bistable perception by using source based wavelet features

Susmita Sen, Katsumi Watanabe, Joydeep Bhattacharya, Goutam Saha

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトル2017 IEEE Calcutta Conference, CALCON 2017 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ144-149
ページ数6
ISBN(電子版)9781538637456
DOI
出版ステータスPublished - 2018 2 2
イベント2017 IEEE Calcutta Conference, CALCON 2017 - Kolkata, India
継続期間: 2017 12 22017 12 3

出版物シリーズ

名前2017 IEEE Calcutta Conference, CALCON 2017 - Proceedings
2018-January

Other

Other2017 IEEE Calcutta Conference, CALCON 2017
CountryIndia
CityKolkata
Period17/12/217/12/3

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Control and Optimization

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