A novel levant’s differentiator-based descriptor for EEG-based motor intent decoding

Conference paper

Kulwa, F., Sarwatt, D. S., Asogbon, M., Huang, J., Khushaba, R. N., Oyemakinde, T. O., Li, G, Samuel, O., Li, H. and Li, Y. 2025. A novel levant’s differentiator-based descriptor for EEG-based motor intent decoding. 47th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society. IEEE.
AuthorsKulwa, F., Sarwatt, D. S., Asogbon, M., Huang, J., Khushaba, R. N., Oyemakinde, T. O., Li, G, Samuel, O., Li, H. and Li, Y.
TypeConference paper
Abstract

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.

Year2025
Conference47th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society
PublisherIEEE
Web address (URL)https://embc.embs.org/2025/
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Deposited31 Oct 2025
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