Imagined Chinese Speech Decoding Based on Initials and Finals From EEG Activity

Brain‐computer interface (BCI) plays an important role in various fields, such as neuroscience, rehabilitation, and machine learning. The silent BCI, which can reconstruct inner speech from neural activity, holds great promise for aphasia patients. In this paper, we design an imagined Chinese speech experimental paradigm based on initials and finals and collect raw signals from eight healthy participants by using 64‐channel scalp electroencephalograms. Linear predictive coding (LPC) and mel frequency cepstral coefficients (MFCC), which are classical algorithms in the field of speech recognition, are used to extract distinguishing features for speech classification and reconstruction. Besides, the phase‐lock value (PLV) is introduced to enrich the feature information. We choose support vector machine (SVM), linear discriminant analysis (LDA), decision tree (DT), and LogitBoost (LB) for binary classification in several different cases. Two‐channel selection (CS) based on Broca’s area and Wernicke’s area of the brain is also introduced in the paper. The highest imaginary speech decoding accuracy reaches 84.38%, which demonstrates the effectiveness of the feature engineering. In addition, the comparative analysis is conducted with deep learning methods specifically designed for small sample scenarios. This study offers a novel systematic approach for the research of Chinese speech imagination BCI.