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Empirical Mode Decomposition Algorithms for Classification of Single-Channel EEG Manifesting McGurk Effect

  • Arup Kumar Pal
  • Dipanjan Roy
  • G. Vinodh Kumar
  • Bipra Chatterjee
  • L. N. Sharma
  • Arpan Banerjee
  • Cota Navin GuptaEmail author
Conference paper
  • 41 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11886)

Abstract

Brain state classification using electroencephalography (EEG) finds applications in both clinical and non-clinical contexts, such as detecting sleep states or perceiving illusory effects during multisensory McGurk paradigm, respectively. Existing literature mostly considers recordings of EEG electrodes that cover the entire head. However, for real world applications, wearable devices that encompass just one (or a few) channels are desirable, which make the classification of EEG states even more challenging. With this as background, we applied variants of data driven Empirical Mode Decomposition (EMD) on McGurk EEG, which is an illusory perception of speech when the movement of lips does not match with the audio signal, for classifying whether the perception is affected by the visual cue or not. After applying a common pre-processing pipeline, we explored four EMD based frameworks to extract EEG features, which were classified using Random Forest. Among the four alternatives, the most effective framework decomposes the ensemble average of two classes of EEG into their respective intrinsic mode functions forming the basis on which the trials were projected to obtain features, which on classification resulted in accuracies of 63.66% using single electrode and 75.85% using three electrodes. The frequency band which plays vital role during audio-visual integration was also studied using traditional band pass filters. Of all, Gamma band was found to be the most prominent followed by alpha and beta bands which contemplates findings from previous studies.

Keywords

EMD EEG McGurk effect Random Forest 

Notes

Acknowledgement

NBRC authors (DR, GVK and AB) collected the EEG data and performed preprocessing steps. The EMD frameworks were formulated by AKP and CNG. The code for Random Forest was written in MATLAB by BC, AKP and CNG. The paper was written by AKP, BC and CNG. All authors assisted in answering the reviewers comments.

This initial part of this research was funded by NBRC core and the grants Ramalingaswami fellowship (BT/RLF/Re-entry/31/2011) and Innovative Young Bio-technologist Award (IYBA) (BT/07/IYBA/2013) from the Department of Biotechnology (DBT), Ministry of Science Technology, Government of India to Arpan Banerjee. Dipanjan Roy was supported by the Ramalingaswami fellowship (BT/RLF/Re-entry/07/2014) and DST extramural grant (SR/CSRI/21/2016). Neural Engineering Lab, IIT Guwahati is supported by Startup Grant, IIT Guwahati and NECBH grant sponsored by DBT (NECBH/2019-20/177).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Arup Kumar Pal
    • 1
    • 3
  • Dipanjan Roy
    • 2
  • G. Vinodh Kumar
    • 2
  • Bipra Chatterjee
    • 1
  • L. N. Sharma
    • 3
  • Arpan Banerjee
    • 2
  • Cota Navin Gupta
    • 1
    Email author
  1. 1.Neural Engineering Lab, Department of Biosciences and BioengineeringIndian Institute of Technology GuwahatiGuwahatiIndia
  2. 2.Cognitive Brain Dynamics LabNational Brain Research CentreGurgaonIndia
  3. 3.EMST Lab, Department of Electronics and Electrical EngineeringIndian Institute of Technology GuwahatiGuwahatiIndia

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