Motor imagery EEG signal classification using novel deep learning algorithm

Electroencephalography (EEG) signal classification plays a critical role in various biomedical and cognitive research applications, including neurological disorder detection and cognitive state monitoring. However, these technologies face challenges and exhibit reduced performances due to signal noi...

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Bibliographic Details
Published in:Scientific reports Vol. 15; no. 1; pp. 24539 - 24
Main Authors: Mathiyazhagan, Sathish, Devasena, M. S. Geetha
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 08.07.2025
Nature Publishing Group
Nature Portfolio
Subjects:
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692/308
692/700
Accuracy
Adaptive deep belief network
Algorithms
Brain-Computer Interfaces
Brain–computer interface
Classification
Datasets
Deep Learning
Discriminant analysis
EEG
EEG signal processing
Electroencephalography
Electroencephalography - methods
Far and near optimization
Feature selection
Fourier transforms
Geometry
Humanities and Social Sciences
Humans
Hybrid preprocessing model
Imagination - physiology
Mental task performance
Motor imagery
multidisciplinary
Neural networks
Optimization
Real time
Science
Science (multidisciplinary)
Signal Processing, Computer-Assisted
Spatial analysis
Support vector machines
Wavelet Analysis
Wavelet transforms
Motor imagery
Hybrid preprocessing model
Adaptive deep belief network
Far and near optimization
EEG signal processing
Brain–computer interface
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary:Electroencephalography (EEG) signal classification plays a critical role in various biomedical and cognitive research applications, including neurological disorder detection and cognitive state monitoring. However, these technologies face challenges and exhibit reduced performances due to signal noise, inter-subject variability, and real-time processing demands. Thus, to overcome these limitations a novel model is presented in this research work for motor imagery (MI) EEG signal classification. To begin, the preprocessing stage of the proposed approach includes an innovative hybrid approach that combines empirical mode decomposition (EMD) for extracting intrinsic signal modes. In addition to that, continuous wavelet transform (CWT) is used for multi-resolution analysis. For spatial feature enhancement the proposed approach utilizes source power coherence (SPoC) integrated with common spatial patterns (CSP) for robust feature extraction. For final feature classification, an adaptive deep belief network (ADBN) is proposed. To attain enhanced performance the parameters of the classifier network are optimized using the Far and near optimization (FNO) algorithm. This combined approach provides superior classification accuracy and adaptability to diverse conditions in EEG signal analysis. The evaluations of the proposed approach were conducted using benchmark BCI competition IV Dataset 2a and Physionet dataset. On the BCI dataset, the proposed approach achieves 95.7% accuracy, 96.2% recall, 95.9% precision, and 97.5% specificity. In addition, it delivers 94.1% accuracy, 94.0% recall, 93.6% precision, and 95.0% specificity on the PhysioNet dataset. With better results, the proposed model attained superior performance compared to existing methods such as CNN, LSTM, and BiLSTM algorithms.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-00824-7