Stable Decoding from a Speech BCI Enables Control for an Individual with ALS without Recalibration for 3 Months,Advanced Science

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Stable Decoding from a Speech BCI Enables Control for an Individual with ALS without Recalibration for 3 Months
Advanced Science ( IF 14.3 ) Pub Date : 2023-10-24 , DOI: 10.1002/advs.202304853
Shiyu Luo ₁ , Miguel Angrick ₂ , Christopher Coogan ₂ , Daniel N Candrea ₁ , Kimberley Wyse-Sookoo ₁ , Samyak Shah ₂ , Qinwan Rabbani _{3,

4} , Griffin W Milsap ₅ , Alexander R Weiss ₂ , William S Anderson ₆ , Donna C Tippett _{2,

7,

8} , Nicholas J Maragakis ₂ , Lora L Clawson ₂ , Mariska J Vansteensel ₉ , Brock A Wester ₅ , Francesco V Tenore ₅ , Hynek Hermansky _{3,

4} , Matthew S Fifer ₅ , Nick F Ramsey ₉ , Nathan E Crone ₂

Affiliation

Brain-computer interfaces (BCIs) can be used to control assistive devices by patients with neurological disorders like amyotrophic lateral sclerosis (ALS) that limit speech and movement. For assistive control, it is desirable for BCI systems to be accurate and reliable, preferably with minimal setup time. In this study, a participant with severe dysarthria due to ALS operates computer applications with six intuitive speech commands via a chronic electrocorticographic (ECoG) implant over the ventral sensorimotor cortex. Speech commands are accurately detected and decoded (median accuracy: 90.59%) throughout a 3-month study period without model retraining or recalibration. Use of the BCI does not require exogenous timing cues, enabling the participant to issue self-paced commands at will. These results demonstrate that a chronically implanted ECoG-based speech BCI can reliably control assistive devices over long time periods with only initial model training and calibration, supporting the feasibility of unassisted home use.

中文翻译：

语音 BCI 的稳定解码可实现对 ALS 患者的控制，无需重新校准 3 个月

脑机接口 (BCI) 可用于控制患有限制言语和运动的肌萎缩侧索硬化症 (ALS) 等神经系统疾病的患者的辅助设备。对于辅助控制，BCI 系统需要准确可靠，最好具有最短的设置时间。在这项研究中，一名因 ALS 导致严重构音障碍的参与者通过腹侧感觉运动皮层上的慢性皮层电图 (ECoG) 植入物使用六个直观语音命令来操作计算机应用程序。在 3 个月的研究期间，无需重新训练或重新校准模型，即可准确检测和解码语音命令（中位准确度：90.59%）。使用 BCI 不需要外源时间提示，使参与者能够随意发出自定进度的命令。这些结果表明，长期植入的基于 ECoG 的语音 BCI 可以在长时间内可靠地控制辅助设备，只需初始模型训练和校准，支持无辅助家庭使用的可行性。

更新日期：2023-10-24

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