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

Susmita Sen, Katsumi Watanabe, Joydeep Bhattacharya, Goutam Saha

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    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.

    Original languageEnglish
    Title of host publication2017 IEEE Calcutta Conference, CALCON 2017 - Proceedings
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages144-149
    Number of pages6
    Volume2018-January
    ISBN (Electronic)9781538637456
    DOIs
    Publication statusPublished - 2018 Feb 2
    Event2017 IEEE Calcutta Conference, CALCON 2017 - Kolkata, India
    Duration: 2017 Dec 22017 Dec 3

    Other

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

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    Keywords

    • ANN
    • Bistable Perception
    • Decoding
    • MEG
    • Single-Trial Classification
    • Source Reconstruction
    • SVM

    ASJC Scopus subject areas

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

    Cite this

    Sen, S., Watanabe, K., Bhattacharya, J., & Saha, G. (2018). Decoding mental states in bistable perception by using source based wavelet features. In 2017 IEEE Calcutta Conference, CALCON 2017 - Proceedings (Vol. 2018-January, pp. 144-149). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CALCON.2017.8280713