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 percent top-1 and 70 percent top-2 accuracy, with the Water class reaching 80 percent top-1 and 100 percent 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.

翻译：失语症患者在实时言语交流方面存在严重困难，而大多数想象言语解码方法仍局限于离线分析或计算密集型模型。为应对这一局限，我们提出了一个双阶段实验框架，包含离线数据采集阶段和随后的在线反馈阶段，用于实时想象言语解码。该范式采用四类韩语任务，包括根据参与者日常交流需求选择的三个想象言语目标和一个静息状态条件，并在一位患有慢性命名性失语症的个体中进行了评估。在此框架内，我们引入了一种专为实时推理显式优化的轻量级基于扩散的神经解码模型，通过架构简化实现，包括降维、时序核优化、带正则化的组归一化以及双重早停准则。在实时评估中，所提系统达到了65%的Top-1准确率和70%的Top-2准确率，其中"水"类别的准确率分别达到80%的Top-1和100%的Top-2准确率。这些结果表明，经过实时优化的基于扩散的架构，结合临床导向的任务设计，能够为失语症中面向交流的脑机接口应用提供可行的在线想象言语解码支持。