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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.07920 |
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| _version_ | 1866911376530735104 |
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| author | Ko, Eunyeong Kim, Soowon Jo, Ha-Na |
| author_facet | Ko, Eunyeong Kim, Soowon Jo, Ha-Na |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_07920 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Lightweight Diffusion-based Framework for Online Imagined Speech Decoding in Aphasia Ko, Eunyeong Kim, Soowon Jo, Ha-Na Artificial Intelligence 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. |
| title | Lightweight Diffusion-based Framework for Online Imagined Speech Decoding in Aphasia |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2511.07920 |