Abstract
Inner speech recognition using electroencephalography (EEG) signals holds significant potential for advancing brain-computer interface (BCI), particularly for individuals with speech impairments. However, decoding inner speech from EEG data remains a challenging task due to the nonlinear, high-dimensional and temporal dynamic nature of neural signals. In order to address the challenges, this study explores the application of reservoir computing (RC), with a particular focus on bidirectional RC, for the purpose of classifying inner speech from EEG signals. Contrary to unidirectional RC, which processes data in a single time direction, bidirectional RC captures both past and future temporal dependencies. This enhancement of the extraction of meaningful EEG features for classification is a significant contribution of this study. We evaluate the performance of both unidirectional and bidirectional RC architectures across a range of reservoir sizes (400, 500, and 600 units) to identify the most effective configuration for this task. Our results demonstrate that the bidirectional RC consistently outperforms the unidirectional RC in terms of accuracy and F1-score across all reservoir sizes, highlighting its superior ability to extract comprehensive temporal features from EEG data. The optimal performance is achieved by bidirectional RC with 600 reservoir units, yielding an accuracy of 18.94% and an F1-score of 19.02%, which surpasses all other configurations. In contrast, unidirectional RC with 600 units exhibits a lower accuracy of 17.22%. These findings underscore the potential of bidirectional RC in EEG signals classification for inner speech recognition, offering a promising direction for developing efficient BCI with low-cost computation.
https://ror.org/02278tr80 
