Rabiee Ali, Ghafoori Sima, Cetera Anna, Shahriari Yalda, Abiri Reza
Dept. of Electrical, Computer and Biomedical Engineering, University of Rhode Island, Kingston, RI, USA.
Dept. of Electrical, Computer and Biomedical Engineering University of Rhode Island, Kingston, RI, USA.
ArXiv. 2025 May 16:arXiv:2402.09447v2.
This research aims to decode hand grasps from Electroencephalograms (EEGs) for dexterous neuroprosthetic development and Brain-Computer Interface (BCI) applications, especially for patients with motor disorders. Particularly, it focuses on distinguishing two complex natural power and precision grasps in addition to a neutral condition as a no-movement condition using a new EEG-based BCI platform and wavelet signal processing. Wavelet analysis involved generating time-frequency and topographic maps from wavelet power coefficients. Then, by using machine learning techniques with novel wavelet features, we achieved high average accuracies: 85.16% for multiclass, 95.37% for No-Movement vs Power, 95.40% for No-Movement vs Precision, and 88.07% for Power vs Precision, demonstrating the effectiveness of these features in EEG-based grasp differentiation. In contrast to previous studies, a critical part of our study was permutation feature importance analysis, which highlighted key features for grasp classification. It revealed that the most crucial brain activities during grasping occur in the motor cortex, within the alpha and beta frequency bands. These insights demonstrate the potential of wavelet features in real-time neuroprosthetic technology and BCI applications.
本研究旨在从脑电图（EEG）中解码手部抓握动作，以促进灵巧神经假体的开发和脑机接口（BCI）应用，特别是针对运动障碍患者。具体而言，本研究使用基于EEG的新型BCI平台和小波信号处理，除了将中立状态作为无动作状态外，还专注于区分两种复杂的自然力量抓握和精确抓握。小波分析包括从小波功率系数生成时频图和地形图。然后，通过使用具有新颖小波特征的机器学习技术，我们实现了较高的平均准确率：多类分类为85.16%，无动作与力量抓握对比为95.37%，无动作与精确抓握对比为95.40%，力量抓握与精确抓握对比为88.07%，证明了这些特征在基于EEG抓握区分中的有效性。与以往研究不同的是，我们研究的一个关键部分是排列特征重要性分析，该分析突出了抓握分类的关键特征。结果表明，抓握过程中最关键的大脑活动发生在运动皮层的α和β频段内。这些见解证明了小波特征在实时神经假体技术和BCI应用中的潜力。
