Lightweight Diffusion-based Framework for Online Imagined Speech Decoding in Aphasia
This advances brain-computer interfaces for clinical communication support in people with severe expressive language impairment, though it appears incremental.
The researchers tackled real-time imagined speech classification in individuals with aphasia using a lightweight diffusion-based framework with EEG data, achieving 65% top-1 and 70% top-2 accuracy in online trials, outperforming offline evaluation.
A diffusion-based neural decoding framework optimized for real-time imagined speech classification in individuals with aphasia. The system integrates a lightweight conditional diffusion encoder and convolutional classifier trained using subject-specific EEG data acquired from a Korean-language paradigm. A dual-criterion early stopping strategy enabled rapid convergence under limited calibration data, while dropout regularization and grouped temporal convolutions ensured stable generalization. During online operation, continuous EEG streams were processed in two-second sliding windows to generate class probabilities that dynamically modulated visual and auditory feedback according to decoding confidence. Across twenty real-time trials, the framework achieved 65% top-1 and 70% top-2 accuracy, outperforming offline evaluation (50% top-1). These results demonstrate the feasibility of deploying diffusion-based EEG decoding under practical clinical constraints, maintaining reliable performance despite environmental variability and minimal preprocessing. The proposed framework advances the translation of imagined speech brain-computer interfaces toward clinical communication support for individuals with severe expressive language impairment.