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An Extensive Analysis on Deep Neural Architecture for Classification of Subject-Independent Cognitive States

Published: 15 January 2020 Publication History

Abstract

Human mental state can be measured by analyzing and understanding EEG (Electroencephalogram) signal in various applications such as neuro-science, brain-computer interfaces, etc. It is an important area of research where machine learning algorithms are being used to develop tools for mental state classification. The modern deep learning algorithms can be used on large EEG data set after applying the data augmentation process on them. In this paper, we apply the Deep Belief Network (DBN) model based on the Restricted Boltzmann Machine (RBM) for unsupervised feature learning of EEG signals to extract salient features for classification. This DBN model provides an unsupervised taxonomy-based system without human intervention. The efficiency of this model is evaluated on the ambulatory EEG signal with other deep learning algorithms. Experimental results demonstrate that DBN with Recurrent Neural Network-Long Short Term Memory (DBN-RNN-LSTM) provides an accuracy of 98.3% which is better than RNN-LSTM and other classical machine learning algorithm.

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  • (2025)Mental Workload Assessment Using Deep Learning Models From EEG Signals: A Systematic ReviewIEEE Transactions on Cognitive and Developmental Systems10.1109/TCDS.2024.346075017:1(40-60)Online publication date: Feb-2025
  • (2024)Learner’s cognitive state recognition based on multimodal physiological signal fusionApplied Intelligence10.1007/s10489-024-05958-155:2Online publication date: 11-Dec-2024
  • (2022)Session Invariant EEG Signatures using Elicitation Protocol Fusion and Convolutional Neural NetworkIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2021.306077519:4(2488-2500)Online publication date: 1-Jul-2022
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  1. An Extensive Analysis on Deep Neural Architecture for Classification of Subject-Independent Cognitive States

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    cover image ACM Other conferences
    CoDS COMAD 2020: Proceedings of the 7th ACM IKDD CoDS and 25th COMAD
    January 2020
    399 pages
    ISBN:9781450377386
    DOI:10.1145/3371158
    © 2020 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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    Publication History

    Published: 15 January 2020

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    Author Tags

    1. Data Augmentation
    2. Deep Belief Network (DBN)
    3. EEG (Electroencephalogram)
    4. Long Short Term Memory (LSTM)
    5. Recurrent Neural Network (RNN)
    6. Restricted Boltzmann Machine (RBM)

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    CoDS COMAD 2020
    CoDS COMAD 2020: 7th ACM IKDD CoDS and 25th COMAD
    January 5 - 7, 2020
    Hyderabad, India

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    CoDS COMAD 2020 Paper Acceptance Rate 78 of 275 submissions, 28%;
    Overall Acceptance Rate 197 of 680 submissions, 29%

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    View all
    • (2025)Mental Workload Assessment Using Deep Learning Models From EEG Signals: A Systematic ReviewIEEE Transactions on Cognitive and Developmental Systems10.1109/TCDS.2024.346075017:1(40-60)Online publication date: Feb-2025
    • (2024)Learner’s cognitive state recognition based on multimodal physiological signal fusionApplied Intelligence10.1007/s10489-024-05958-155:2Online publication date: 11-Dec-2024
    • (2022)Session Invariant EEG Signatures using Elicitation Protocol Fusion and Convolutional Neural NetworkIEEE Transactions on Dependable and Secure Computing10.1109/TDSC.2021.306077519:4(2488-2500)Online publication date: 1-Jul-2022
    • (2020)A Smart Ambulatory Cognitive State Taxonomy System Through EEG Signal Analysis2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT49239.2020.9225660(1-7)Online publication date: Jul-2020

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