Imagined Speech Classification Using EEG and CBAM-CNN Model

This work introduces a novel Convolutional Block Attention Module (CBAM) along with Convolutional Neural Network (CNN) architecture for the recognition of imagined speech electroencephalography (EEG) data. The real-time data is recorded in Biomedical Signal Processing Laboratory, IIT Roorkee. The Pearson correlation (P) method is utilized to gain understanding of neural activity during speech imagination. The proposed CBAM model leverages both spatial and channel attention, allowing the model to selectively focus on the most distinguishing regions. The incorporation of the CBAM mechanism enhances feature representation by adaptively emphasizing the most informative spatial and channel-wise EEG components, thereby improving both model performance and interpretability. This model also utilizes binary and multiclass classification of imagined speech from correlation-based EEG feature images. The subject independent and subject-dependent classification accuracies obtained from P feature images range from 52.72±7.1% to 68.20±5.3% and 67.47±5.8% to 88.09±4.2% respectively. The results suggest that correlation-based feature representation effectively captures the underlying neural dynamics associated with imagined speech. Comparative analysis with existing state-of-the-art methods indicates that the proposed model achieves improved classification accuracy and generalization, validating its effectiveness for EEG-based imagined speech decoding. These findings indicate the potential of the proposed approach for reliable and scalable brain–computer interface (BCI) applications in real-world scenarios.