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

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, we combine Shapley value computation and decision trees to calculate the importance of different electrodes during classification. Experimental results show that the models achieve effective decoding on our dataset. The EEG-Conformer model performs significantly above chance level across all data, reaching an accuracy of 69.83% in normal speaking conditions and up to 61.46% in conditions simulating speech disorders. In the multi-task setting, the classification accuracy across different conditions exceeds 97%. By utilizing the important electrodes identified through interpretability methods as new feature inputs, the classification performance further improves even with a reduction of over 50% in the channels. These results demonstrate the potential of neural signal decoding technologies in communication assistance, reveal the decodability of Chinese Mandarin EEG datasets, and provide feasible recommendations for the future design of Chinese BCI applications.