Rhythmic Motor Imagery Boosts Accuracy and Efficiency in Noninvasive Brain-Computer Interfaces.

Abstract

Decoding motor intentions from noninvasive brain recordings remains a longstanding challenge in neural engineering, particularly in advancing brain-computer interfaces (BCIs) for motor assistance and rehabilitation. The traditional motor imagery (MI) paradigm faces limitations due to ill-defined mental tasks and the variability of the induced sensorimotor rhythm (SMR) features. Studies involving large-scale subject cohorts have reported that conventional MI-BCI achieves only around 70 75% accuracy in binary classification, with an inefficiency rate of 35%-50%. Here, we introduce a rhythmic MI paradigm which can induce steady-state movement-related rhythms (SSMRR). A comprehensive evaluation involving 65 BCI-naïve participants was conducted to investigate whether rhythmic MI with SSMRR features can enhance MI-BCI's performance. Our results demonstrate a 4 class online decoding accuracy of 78.88%±14.80% and a binary offline decoding accuracy of nearly 90%, with an inefficiency rate below 10%, marking a substantial improvement over conventional MI-BCI. Offline evaluations also show the potential of rhythmic MI for cross-subject generalization. Furthermore, we show that the proposed rhythmic MI tasks can help participants better modulate SMR, reducing the SMR inefficiency rate from 50.77% to 23.08%. Lastly, we validate phase consistency as a neurophysiological predictor of SSMRR-based decoding, offering insights for further refining mental tasks and improving decoding algorithms. Overall, our findings demonstrate that rhythmic MI can facilitate a noninvasive BCI with high decoding accuracy and low inefficiency rate, unlocking new possibilities for human machine interaction and clinical applications such as neurorehabilitation.