Lightweight Diffusion-based Framework for Online Imagined Speech Decoding in Aphasia

TL;DR

This paper introduces a lightweight, diffusion-based neural decoding framework for real-time imagined speech decoding in aphasia, demonstrating promising accuracy with a clinically relevant task design.

Contribution

It presents a novel real-time diffusion-based neural decoding model optimized for online imagined speech decoding in aphasia patients.

Findings

Achieved 65% top-1 accuracy in real-time decoding

Demonstrated feasibility of online imagined speech decoding in aphasia

Optimized model architecture for real-time inference

Abstract

Individuals with aphasia experience severe difficulty in real-time verbal communication, while most imagined speech decoding approaches remain limited to offline analysis or computationally demanding models. To address this limitation, we propose a two-session experimental framework consisting of an offline data acquisition phase and a subsequent online feedback phase for real-time imagined speech decoding. The paradigm employed a four-class Korean-language task, including three imagined speech targets selected according to the participant's daily communicative needs and a resting-state condition, and was evaluated in a single individual with chronic anomic aphasia. Within this framework, we introduce a lightweight diffusion-based neural decoding model explicitly optimized for real-time inference, achieved through architectural simplifications such as dimensionality reduction, temporal kernel optimization, group normalization with regularization, and dual early-stopping criteria. In real-time evaluation, the proposed system achieved 65\% top-1 and 70\% top-2 accuracy, with the Water class reaching 80\% top-1 and 100\% top-2 accuracy. These results demonstrate that real-time-optimized diffusion-based architectures, combined with clinically grounded task design, can support feasible online imagined speech decoding for communication-oriented BCI applications in aphasia.