Mohamed Abdul-Khaaliq, Aharonson Vered
School of Electrical and Information Engineering, University of Witwatersrand, Johannesburg 2050, South Africa.
Department of Basic and Clinical Sciences, Medical School, University of Nicosia, Nicosia 2421, Cyprus.
Biomimetics (Basel). 2025 Mar 18;10(3):187. doi: 10.3390/biomimetics10030187.
Improved interpretation of electroencephalography (EEG) associated with the neural control of essential hand movements, including wrist extension (WE) and wrist flexion (WF), could improve the performance of brain-computer interfaces (BCIs). These BCIs could control a prosthetic or orthotic hand to enable motor-impaired individuals to regain the performance of activities of daily living. This study investigated the interpretation of neural signal patterns associated with kinematic differences between real, regulated, isometric WE and WF movements from recorded EEG data. We used 128-channel EEG data recorded from 14 participants performing repetitions of the wrist movements, where the force, speed, and range of motion were regulated. The data were filtered into four frequency bands: delta and theta, mu and beta, low gamma, and high gamma. Within each frequency band, independent component analysis was used to isolate signals originating from seven cortical regions of interest. Features were extracted from these signals using a time-frequency algorithm and classified using Mahalanobis distance clustering. We successfully classified bilateral and unilateral WE and WF movements, with respective accuracies of 90.68% and 69.80%. The results also demonstrated that all frequency bands and regions of interest contained motor-related discriminatory information. Bilateral discrimination relied more on the mu and beta bands, while unilateral discrimination favoured the gamma bands. These results suggest that EEG-based BCIs could benefit from the extraction of features from multiple frequencies and cortical regions.
与包括腕背伸（WE）和腕掌屈（WF）在内的手部基本运动的神经控制相关的脑电图（EEG）解读的改善，可能会提高脑机接口（BCI）的性能。这些脑机接口可以控制假肢或矫形手，使运动功能受损的个体能够恢复日常生活活动的能力。本研究调查了从记录的脑电图数据中解读与真实、受控、等长腕背伸和腕掌屈运动之间的运动学差异相关的神经信号模式。我们使用了从14名参与者进行腕部运动重复记录的128通道脑电图数据，其中力、速度和运动范围是受控的。数据被过滤到四个频段：δ和θ、μ和β、低γ和高γ。在每个频段内，使用独立成分分析来分离源自七个感兴趣皮质区域的信号。使用时频算法从这些信号中提取特征，并使用马氏距离聚类进行分类。我们成功地对双侧和单侧腕背伸和腕掌屈运动进行了分类，各自的准确率分别为90.68%和69.80%。结果还表明，所有频段和感兴趣区域都包含与运动相关的鉴别信息。双侧鉴别更多地依赖于μ和β频段，而单侧鉴别则更倾向于γ频段。这些结果表明，基于脑电图的脑机接口可以从多个频率和皮质区域提取特征中受益。
