A Machine Learning Approach to Decode Mental States in Bistable Perception

Susmita Sen, Syed Naser Daimi, Katsumi Watanabe, Joydeep Bhattacharya, Goutam Saha

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

1 Citation (Scopus)

Abstract

This work demonstrates the usefulness of machine learning framework in decoding mental states from recorded brain signals. Magnetoencephalogram (MEG) signals were recorded from human participants while they were presented with six different conditions of bistable stimuli. Two internal mental states, transition and maintenance, which are related to switching or maintaining a perception in bistable perception respectively, were decoded. We extracted two types of features using complex Morlet wavelet transform that capture the spatio-temporal dynamics of large scale brain oscillations at global and local scale. Principal component analysis (PCA) was employed to reduce the dimension of the feature vector as well to minimize the redundancy among the features. Support vector machine (SVM) and artificial neural network (ANN) based classifiers were used to predict the mental states on a trial-by-trial basis. We were able to decode the two mental states from pooled data of all six conditions with accuracies of 79.52% and 79.56% using SVM and ANN classifier, respectively from local features which performed better than global features. The results show the effectiveness of signal processing and machine learning based approaches to identify internal mental states.

Original languageEnglish
Title of host publicationProceedings - 2017 International Conference on Information Technology, ICIT 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Print)9781538629246
DOIs
Publication statusPublished - 2018 Jul 31
Event16th International Conference on Information Technology, ICIT 2017 - Bhubaneswar, Odisha, India
Duration: 2017 Dec 212017 Dec 23

Publication series

NameProceedings - 2017 International Conference on Information Technology, ICIT 2017

Other

Other16th International Conference on Information Technology, ICIT 2017
CountryIndia
CityBhubaneswar, Odisha
Period17/12/2117/12/23

Keywords

  • ANN
  • Bistable Perception
  • Decoding
  • MEG
  • PCA
  • SVM
  • Single-trial classification

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Computer Networks and Communications
  • Computer Science Applications
  • Software
  • Information Systems and Management

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  • Cite this

    Sen, S., Daimi, S. N., Watanabe, K., Bhattacharya, J., & Saha, G. (2018). A Machine Learning Approach to Decode Mental States in Bistable Perception. In Proceedings - 2017 International Conference on Information Technology, ICIT 2017 (pp. 1-6). [8423873] (Proceedings - 2017 International Conference on Information Technology, ICIT 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICIT.2017.30