🤖 AI Summary
To address the low classification accuracy and high computational complexity in motor imagery (MI)-based brain–computer interfaces (BCIs) using multi-channel electroencephalography (EEG), this paper proposes a novel channel selection and multi-domain feature fusion framework grounded in functional brain regional characteristics. First, electrodes are grouped according to anatomically and functionally defined brain regions, and key channels are selected based on inter-regional correlation analysis. Subsequently, discriminative features are extracted from three complementary domains: spatial features via common spatial patterns (CSP), clustering-based features using fuzzy C-means, and manifold features derived from tangent space mapping on Riemannian manifolds. This fusion strategy enhances feature discriminability while mitigating redundancy. Finally, a support vector machine (SVM) classifier is employed. Evaluated on BCI Competition IV datasets 2a and 2b, the method achieves average classification accuracies of 90.77% and 84.50%, respectively—outperforming state-of-the-art approaches.
📝 Abstract
A Brain Computer Interface (BCI) connects the human brain to the outside world, providing a direct communication channel. Electroencephalography (EEG) signals are commonly used in BCIs to reflect cognitive patterns related to motor function activities. However, due to the multichannel nature of EEG signals, explicit information processing is crucial to lessen computational complexity in BCI systems. This study proposes an innovative method based on brain region-specific channel selection and multi-domain feature fusion to improve classification accuracy. The novelty of the proposed approach lies in region-based channel selection, where EEG channels are grouped according to their functional relevance to distinct brain regions. By selecting channels based on specific regions involved in motor imagery (MI) tasks, this technique eliminates irrelevant channels, reducing data dimensionality and improving computational efficiency. This also ensures that the extracted features are more reflective of the brain actual activity related to motor tasks. Three distinct feature extraction methods Common Spatial Pattern (CSP), Fuzzy C-means clustering, and Tangent Space Mapping (TSM), are applied to each group of channels based on their brain region. Each method targets different characteristics of the EEG signal: CSP focuses on spatial patterns, Fuzzy C means identifies clusters within the data, and TSM captures non-linear patterns in the signal. The combined feature vector is used to classify motor imagery tasks (left hand, right hand, and right foot) using Support Vector Machine (SVM). The proposed method was validated on publicly available benchmark EEG datasets (IVA and I) from the BCI competition III and IV. The results show that the approach outperforms existing methods, achieving classification accuracies of 90.77% and 84.50% for datasets IVA and I, respectively.