An Adaptive Convolutional Neural Network With Spatio-Temporal Attention and Dynamic Pathways (ACNN-STADP) for Robust EEG-Based Motor Imagery Classification
Electroencephalogram (EEG)-based Brain-Computer Interfaces (BCIs) have gained substantial attention, particularly for motor imagery (MI) that facilitates direct brain-to-device communication without any muscular movement. However, existing classification models face limitations such as inter-subject...
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2025-01-01
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| author | Aaqib Raza Mohd Zuki Yusoff |
| author_facet | Aaqib Raza Mohd Zuki Yusoff |
| author_sort | Aaqib Raza |
| collection | DOAJ |
| description | Electroencephalogram (EEG)-based Brain-Computer Interfaces (BCIs) have gained substantial attention, particularly for motor imagery (MI) that facilitates direct brain-to-device communication without any muscular movement. However, existing classification models face limitations such as inter-subject variability, lack of generalizability, high computational demands, low signal-to-noise ratios, and inefficient feature extraction, which impede their robustness and accuracy. Moreover, advanced deep learning models often utilize rigid architectures with fixed spatial-temporal filters, restricting their adaptability to dynamic EEG patterns. To address these challenges, this paper proposes an Adaptive Convolutional Neural Network with Spatio-Temporal Attention and Dynamic Pathways (ACNN-STADP), which introduces a novel dynamic pathway mechanism and adaptive attention strategy for robust MI-EEG decoding. The proposed model integrates a Dynamic Pathway Convolution Network (DPCN) for adaptive feature extraction, incorporating a Dynamic Gating Controller (DGC) and Dynamic Adaptive Spatio-Temporal (DAST) blocks to efficiently capture multi-scale spatial and temporal dependencies. Additionally, an Adaptive Attention Fusion (AAF) module employs Dual Multi-Head Self-Attention (DMHSA) and a U-Net-inspired Adaptive Fusion Block (AFB) to enhance feature integration and improve classification performance. Furthermore, the model introduces three key innovations: Dynamic Multi-Scale Convolutional Learning for adaptive kernel selection, Unified Spatio-Temporal Attention (USTA) for efficient feature recalibration, and AFB for multi-scale feature fusion while preserving long-range dependencies. The model is validated on BCI Competition IV Dataset 2a, achieving a peak accuracy of 90.77%, and further evaluated across six additional MI-EEG datasets, demonstrating an overall average accuracy above 78.98%. ACNN-STADP significantly improves generalization, reduces computational complexity, and enhances real-time applicability, establishing a robust multi-dataset adaptive deep learning framework for EEG-based MI classification. |
| format | Article |
| id | doaj-art-ab90d18eec7e4b7e8bc5ee87405d5e8b |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Access |
| spelling | doaj-art-ab90d18eec7e4b7e8bc5ee87405d5e8b2025-08-20T03:29:34ZengIEEEIEEE Access2169-35362025-01-011310638710640510.1109/ACCESS.2025.358014511037490An Adaptive Convolutional Neural Network With Spatio-Temporal Attention and Dynamic Pathways (ACNN-STADP) for Robust EEG-Based Motor Imagery ClassificationAaqib Raza0https://orcid.org/0009-0006-0509-6832Mohd Zuki Yusoff1https://orcid.org/0000-0001-9306-6655Electrical and Electronic Engineering Department, Centre for Intelligent Signal and Imaging Research (CISIR), Universiti Teknologi PETRONAS (UTP), Seri Iskandar, MalaysiaElectrical and Electronic Engineering Department, Centre for Intelligent Signal and Imaging Research (CISIR), Universiti Teknologi PETRONAS (UTP), Seri Iskandar, MalaysiaElectroencephalogram (EEG)-based Brain-Computer Interfaces (BCIs) have gained substantial attention, particularly for motor imagery (MI) that facilitates direct brain-to-device communication without any muscular movement. However, existing classification models face limitations such as inter-subject variability, lack of generalizability, high computational demands, low signal-to-noise ratios, and inefficient feature extraction, which impede their robustness and accuracy. Moreover, advanced deep learning models often utilize rigid architectures with fixed spatial-temporal filters, restricting their adaptability to dynamic EEG patterns. To address these challenges, this paper proposes an Adaptive Convolutional Neural Network with Spatio-Temporal Attention and Dynamic Pathways (ACNN-STADP), which introduces a novel dynamic pathway mechanism and adaptive attention strategy for robust MI-EEG decoding. The proposed model integrates a Dynamic Pathway Convolution Network (DPCN) for adaptive feature extraction, incorporating a Dynamic Gating Controller (DGC) and Dynamic Adaptive Spatio-Temporal (DAST) blocks to efficiently capture multi-scale spatial and temporal dependencies. Additionally, an Adaptive Attention Fusion (AAF) module employs Dual Multi-Head Self-Attention (DMHSA) and a U-Net-inspired Adaptive Fusion Block (AFB) to enhance feature integration and improve classification performance. Furthermore, the model introduces three key innovations: Dynamic Multi-Scale Convolutional Learning for adaptive kernel selection, Unified Spatio-Temporal Attention (USTA) for efficient feature recalibration, and AFB for multi-scale feature fusion while preserving long-range dependencies. The model is validated on BCI Competition IV Dataset 2a, achieving a peak accuracy of 90.77%, and further evaluated across six additional MI-EEG datasets, demonstrating an overall average accuracy above 78.98%. ACNN-STADP significantly improves generalization, reduces computational complexity, and enhances real-time applicability, establishing a robust multi-dataset adaptive deep learning framework for EEG-based MI classification.https://ieeexplore.ieee.org/document/11037490/Brain-computer interface (BCI)electroencephalogram (EEG)motor imagery (MI)classificationadaptive CNNunified spatio-temporal attention |
| spellingShingle | Aaqib Raza Mohd Zuki Yusoff An Adaptive Convolutional Neural Network With Spatio-Temporal Attention and Dynamic Pathways (ACNN-STADP) for Robust EEG-Based Motor Imagery Classification IEEE Access Brain-computer interface (BCI) electroencephalogram (EEG) motor imagery (MI) classification adaptive CNN unified spatio-temporal attention |
| title | An Adaptive Convolutional Neural Network With Spatio-Temporal Attention and Dynamic Pathways (ACNN-STADP) for Robust EEG-Based Motor Imagery Classification |
| title_full | An Adaptive Convolutional Neural Network With Spatio-Temporal Attention and Dynamic Pathways (ACNN-STADP) for Robust EEG-Based Motor Imagery Classification |
| title_fullStr | An Adaptive Convolutional Neural Network With Spatio-Temporal Attention and Dynamic Pathways (ACNN-STADP) for Robust EEG-Based Motor Imagery Classification |
| title_full_unstemmed | An Adaptive Convolutional Neural Network With Spatio-Temporal Attention and Dynamic Pathways (ACNN-STADP) for Robust EEG-Based Motor Imagery Classification |
| title_short | An Adaptive Convolutional Neural Network With Spatio-Temporal Attention and Dynamic Pathways (ACNN-STADP) for Robust EEG-Based Motor Imagery Classification |
| title_sort | adaptive convolutional neural network with spatio temporal attention and dynamic pathways acnn stadp for robust eeg based motor imagery classification |
| topic | Brain-computer interface (BCI) electroencephalogram (EEG) motor imagery (MI) classification adaptive CNN unified spatio-temporal attention |
| url | https://ieeexplore.ieee.org/document/11037490/ |
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