A Novel Levant's Differentiator-Based Descriptor for EEG-Based Motor Intent Decoding

Motor intent (MI)-based brain-computer interfaces (BCIs) have been extensively studied to improve the performance and clinical realization of assistive robots for motor recovery in stroke patients. However, challenges arise in their low decoding performance. This can be attributed to the low spatial resolution and signal-to-noise ratio of electroencephalography (EEG), particularly in accurately deciphering hand movements, which reduces classification performance. Therefore, we have developed a novel feature extraction technique that exploits Levant's differentiators to extract distinct patterns in EEG signals and employs symmetric positive definite matrices (SPD) to effectively leverage the spatial-temporal properties of the EEG signal. Results from nine post-stroke patients and fifteen normal subjects showed an improved decoding accuracy of 99.16±0.64% and 99.30±0.69%, respectively in classifying twenty-four hand motor intents, significantly outperforming existing related methods. Thus, the proposed technique has the potential to greatly enhance the reliability and effectiveness of EEG-based control systems for post-stroke rehabilitation.Clinical Relevance- The outcome of this study can lead to better control of rehabilitation robots and improve the recovery speed of the stroke patients.