In recent years, research and development of brain-computer interfaces (BCIs) have progressed rapidly, aiming to support communication for patients with quadriplegia resulting from conditions such as amyotrophic lateral sclerosis (ALS) or neurological damage. However, the classification of electroencephalogram (EEG) signals remains a significant challenge, with many studies limited to binary classification and insufficient accuracy for practical application. This study aims to classify Japanese vowels during covert speech imagery using deep learning techniques based on brain activity signals. EEG data were recorded from a healthy adult male subject during imagined articulation of Japanese vowels without vocalization. Support Vector Machine (SVM), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM) models were trained and evaluated on the collected data. The classification accuracies achieved were 65%, 90%, 85%, and 93% for SVM, CNN, LSTM, and BiLSTM, respectively. These results indicate that deep learning approaches can successfully identify Japanese vowels from EEG signals associated with covert speech imagery. Future work will focus on applying natural language processing techniques to the predicted character sequences to enable word- and sentence-level estimation, ultimately contributing to the development of a communication support system for non-vocal environments.
YAMANE et al. (Wed,) studied this question.