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Updated: Sep 14, 2025
Assessment and Communication for People with Disorders of Consciousness
Published on: August 1, 2017
Multi-class mental Task Classification based Brain-Computer Interface using Improved Remora depthwise convolutional
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.
Background:
Brain-computer interfaces (BCIs) offer a promising avenue for individuals with severe motor disabilities to interact with the world. By decoding brain signals, BCIs can enable users to control devices and communicate thoughts. However, challenges such as noise in EEG signals and limited data availability hinder the development of accurate and reliable BCI systems. Nonetheless, problems persist, including limited data availability, noisy EEG signals, real-time performance limitations, and reduced classification accuracy.
New Method:
To overcome this, the present work proposes an efficient Multi-Class Mental Task Classification based BCI using deep learning techniques. Initially, the obtained EEG data is pre-processed with a Finite Linear Haar wavelet-based Filtering (FLHF) technique to remove disturbances in EEG data. Afterwards, optimal feature extraction utilizes a Hybrid dynamic centre binary pattern and multi-threshold-based ternary pattern (H-DCBP-MTTP) technique to extract characteristics from pre-processed EEG data. Finally, the Improved Remora depthwise convolutional adaptive neuro-fuzzy inference network (IRDCANFIN) model is used to classify the mental tasks. To improve classification results, the model's parameters are fine-tuned using an Improved Remora optimization approach (IROA).
Results:
The proposed approach's performance is examined using the BCI laboratory dataset and the EEG Psychiatric Disorders Dataset, which yield accuracy results of 99.3 % and 99.56 %, respectively. Furthermore, evaluation results show that the proposed approach outperforms existing models.
Comparison With Existing Methods:
Compared to existing models, such as DQN with a 1D-CNN-LSTM, GSP-ML, Shallow 1D-CNN, KNN, and SVM, and the proposed approach yields effective results in terms of accuracy, robustness, and computational efficiency.
Conclusion:
The proposed IRDCANFIN classifier is used to classify multiple classes of mental tasks like baseline, counting, multiplication, letter composing, and rotation.
