Decoding Chinese speech across multiple neural conditions via EEG: dataset construction and interpretability driven spatial optimization

作者：Haoming Wang, G L Zhang, Xurong Xie, Ying� Qin, Tian Zheng, Zhou Cb, Jin Huang, Feng Tian, Shikui Wei · 发表于：Frontiers in Psychology · 年份：2026 · DOI：10.3389/fpsyg.2026.1833448 · 研究领域：EEG and Brain-Computer Interfaces、Emotion and Mood Recognition、Functional Brain Connectivity Studies

The integration of artificial intelligence (AI) and brain-computer interfaces (BCIs) technologies shows great potential in assisting patients with speech impairments and improving cognitive-linguistic decline. Electroencephalogram (EEG) based BCIs, characterized by non-invasiveness, low cost, and high temporal resolution, hold significant application value in speech decoding and cognitive rehabilitation. Currently, most mainstream public EEG datasets rely on Western languages. As a tonal language, Chinese Mandarin differs significantly from Western languages in speech production mechanisms, making existing data insufficient to support future BCI research for Mandarin-speaking patients. To address this gap, we establish a systematic Mandarin EEG dataset and conduct effective speech decoding and related analyses. We design four distinct experimental conditions, namely overt, overt-noisy, intend, and imagine, to simulate different types of speech disorders in clinical scenarios. Using typical Mandarin tonal-vowels and common vocabularies as stimuli, we construct an EEG dataset collected from a healthy adult. We evaluate the speech decoding performance using short-time Fourier transform combined with support vector machine (STFT-SVM) and EEG-Conformer models. Furthermore, we design a multi-task architecture based on the EEG-Conformer to perform a unified decoding task for the two stimulus types and a classification task across the four dataset conditions. To interpret the model, ...