The rapid development of medical informatization and continuous innovation of artificial intelligence have made it possible to analyze data and predict prognosis through making full use of data analysis or data mining methods in medical field, which can provide not only more accurate basis of diagnosis and treatment for patients but also important decision-making reference for the government and hospitals to allocate medical resources reasonably. As a classical model for processing time series data in machine learning, long short-term memory network can break through some limitations of statistics to process large and complex medical data. The current applications of long short-term memory networks in medical and biomedical fields can be mainly summarized as seven themes, including natural language processing, biomedical information, signals, motion, clinical medical records, hospital management, and public health and policy.
Speech imagery is an emerging brain-computer interface (BCI) paradigm with potential to provide effective communication for individuals with speech impairments. This study designed a Chinese speech imagery paradigm using three clinically relevant words—“Help me”, “Sit up” and “Turn over”—and collected electroencephalography (EEG) data from 15 healthy subjects. Based on the data, a Channel Attention Multi-Scale Convolutional Neural Network (CAM-Net) decoding algorithm was proposed, which combined multi-scale temporal convolutions with asymmetric spatial convolutions to extract multidimensional EEG features, and incorporated a channel attention mechanism along with a bidirectional long short-term memory network to perform channel weighting and capture temporal dependencies. Experimental results showed that CAM-Net achieved a classification accuracy of 48.54% in the three-class task, outperforming baseline models such as EEGNet and Deep ConvNet, and reached a highest accuracy of 64.17% in the binary classification between “Sit up” and “Turn over.” This work provides a promising approach for future Chinese speech imagery BCI research and applications.