Wavelet Augmented Phase Coherence Features for EEG-Based Imagined Speech Classification
Anand Mohan, R. S Anand · IEEE Sensors Letters · 2025
Brain-computer interfaces (BCIs) provide direct communication between the brain and external devices. Using electroencephalogram (EEG) sensors, BCIs are applied in assistive technologies and neuroprosthetics. Among various BCI paradigms, imagined speech-based BCI aims to decode internal speech representations from EEG signals, enabling silent communication. Decoding imagined speech is challenging due to the non-stationarity, inter-subject variability of EEG signals and low signal-to-noise ratio (SNR). The proposed method uses a Multi-Layer Perceptron (MLP) integrated with a Convolutional Block Attention Module (CBAM) to enhance feature learning by refining spatial and channel-wise attention. To further improve performance, wavelet-based augmentation enhances data diversity. Phase and coherence-based functional connectivity features capture inter-channel dependencies critical for imagined speech classification. The proposed Wavelet-Augmented Phase Coherence Features with MLP-CBAM (WaveCoh-MLP-CBAM) framework is evaluated on an imagined speech dataset. The WaveCoh-MLP-CBAM shows superior classification accuracy, F1-score, and Cohen's kappa compared to conventional approaches. Results highlight the importance of augmentation, functional connectivity features, and attention in improving EEG-based imagined speech decoding.