Abstract
This study participated in the Silent Speech Decoding Challenge (SSDC) and investigated the application of a lightweight BERT-based architecture for EEG-based silent speech recognition. We pre-trained a foundation model using publicly available EEG data and fine-tuned it on the SSDC dataset. The model was evaluated on six silent speech commands: “right,” “left,” “up,” “down,” “select,” and “cancel.” The average accuracy and F1 score across all eight subjects were 0.165 and 0.137, respectively. Subject 5 achieved the highest discrimination performance, with an accuracy of 0.239 and an F1 score of 0.223. However, the overall classification performance remained below 25%. The confusion matrix analysis revealed frequent misclassifications across multiple classes, highlighting the challenges of EEG-based silent speech recognition. Accuracy varied across subjects, with the highest exceeding 20% and the lowest below 10%. These findings indicate that while pre-training captured meaningful EEG signal representations, the classification accuracy after fine-tuning was limited, emphasizing the difficulty of silent speech recognition using EEG. Despite these challenges, our approach provides insights into EEG-based classification and demonstrates the potential of BERT-based architectures for future research in this domain.
https://ror.org/01bawqf59 
