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Updated: Sep 14, 2025

Assessment and Communication for People with Disorders of Consciousness
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Multi-class mental Task Classification based Brain-Computer Interface using Improved Remora depthwise convolutional

D Deepika1, G Rekha2

  • 1Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad, Telangana 500075, India; Department of Computer Science and Engineering, Mahatma Gandhi Institute of Technology, Hyderabad, Telangana 500075, India.

Journal of Neuroscience Methods
|July 18, 2025
PubMed
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This study introduces an advanced deep learning model for brain-computer interfaces (BCIs) to accurately classify mental tasks. The novel approach significantly enhances BCI system performance for individuals with motor disabilities.

Area of Science:

  • Neuroscience
  • Computer Science
  • Biomedical Engineering

Background:

  • Brain-computer interfaces (BCIs) offer communication and control for individuals with severe motor disabilities.
  • Challenges in BCI development include noisy EEG signals, limited data, and real-time performance issues.
  • Accurate classification of mental tasks is crucial for effective BCI operation.

Purpose of the Study:

  • To propose an efficient Multi-Class Mental Task Classification based BCI system.
  • To address limitations of existing BCI systems using deep learning.
  • To enhance the accuracy and reliability of BCI for users with motor impairments.

Main Methods:

  • EEG data pre-processing using Finite Linear Haar wavelet-based Filtering (FLHF).
  • Optimal feature extraction via Hybrid dynamic centre binary pattern and multi-threshold-based ternary pattern (H-DCBP-MTTP).
Keywords:
Brain-computer interfaceDepthwise convolutionalDynamic center binary patternElectroencephalogramFinite LinearMulti-threshold-based ternary patternNeuro-fuzzy inference network

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  • Classification using an Improved Remora depthwise convolutional adaptive neuro-fuzzy inference network (IRDCANFIN), optimized by Improved Remora optimization approach (IROA).

    • Main Results:

    • Achieved 99.3% accuracy on the BCI laboratory dataset.
    • Achieved 99.56% accuracy on the EEG Psychiatric Disorders Dataset.
    • Demonstrated superior performance compared to existing models like DQN, GSP-ML, Shallow 1D-CNN, KNN, and SVM.

    Conclusions:

    • The proposed IRDCANFIN model effectively classifies multiple mental tasks.
    • The system shows high accuracy, robustness, and computational efficiency.
    • This advanced BCI system holds significant potential for assistive technology.