Year: 2025
Type: article
Abstract: Speech brain-computer interfaces (BCIs) offer a promising means to provide functional communication capacity for patients with anarthria caused by neurological conditions such as amyotrophic lateral sclerosis (ALS) or brainstem stroke. Current speech decoding research has predominantly focused on English using phoneme-driven architectures, whereas real-time decoding of tonal monosyllabic languages such as Mandarin Chinese remains a major challenge. This study demonstrates a real-time Mandarin speech BCI that decodes monosyllabic units directly from neural signals. Using the 256-channel microelectrocorticographic BCI, we achieved robust decoding of a comprehensive set of 394 distinct syllables based purely on neural signals, yielding median syllable identification accuracy of 71.2% in a single-character reading task. Leveraging this high-performing syllable decoder, we further demonstrated real-time sentence decoding. Our findings demonstrate the efficacy of a tonally integrated, direct syllable neural decoding approach for Mandarin Chinese, paving the way for full-coverage systems in tonal monosyllabic languages.
Source: Science Advances
Authors: Youkun Qian, Changjiang Liu, Peixi Yu, Xingchen Ran, Shurui Li, Qinrong Yang, Yang Liu, Lei Xia, Yijie Wang, Jianxuan Qi, Erda Zhou, Junfeng Lu, Yuanning Li, Tiger H. Tao, Zhitao Zhou, Jinsong Wu
Institutions: Shanghai Medical College of Fudan University, Fudan University, Huashan Hospital, Chinese Academy of Sciences, Shanghai Institute of Microsystem and Information Technology, ShanghaiTech University, University of Chinese Academy of Sciences, Frontiers Center for Brain Science of the Ministry of Education, Shanghai Clinical Research Center, Guangdong Institute of Intelligence Science and Technology, State Key Laboratory of Transducer Technology
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $4,500
OpenAlex ID: W4415935273