Impact of Generation Rate of Speech Imagery on Neural Activity and BCI Decoding Performance: A fNIRS Study

Brain-computer interface (BCI) enables stroke patients to actively modulate neural activity, fostering neuroplasticity and thereby accelerating the recovery process. Due to being portable, non-invasive, and safe, functional near-infrared spectroscopy (fNIRS) has become one of the most widely used neuroimaging techniques. Current BCI research primarily focuses on improving the decoding performance. However, a key aspect of stroke rehabilitation lies in inducing stronger cortical activations in the damaged brain areas, thereby accelerating the recovery of brain functions. This study investigated the regulatory mechanism of the generation rate of speech imagery on neural activity and its impact on BCI decoding performance based on fNIRS. As the generation rate increased from 1 word/4 s to 1 word/2 s, and finally to 1 word/1 s, neural activity in speech-related brain regions steadily enhanced. Correspondingly, the accuracy of detecting speech imagery tasks increased from 83.83% to 85.39%, and ultimately showed a significant improvement, reaching 88.28%. Additionally, the differences in neural activities between the “yes” and “no” speech imagery tasks became more pronounced as the generation rate increased, leading to an improvement in classification performance from 62.81% to 65.78%, and ultimately to 67.50%. This study demonstrates that the neural activity level of most speech-related brain regions during speech imagery enhanced as the generation rate increased. Therefore, accelerating the generation rate of speech imagery induces stronger neural activity and more distinct response patterns between different tasks, which holds the potential to facilitate the development of a BCI feedback system with higher neuroplasticity induction and improved decoding performance.