School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300130, People's Republic of China.
State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, People's Republic of China.
J Neural Eng. 2024 Jun 19;21(3). doi: 10.1088/1741-2552/ad5109.
. Motor-related brain-computer interface (BCI) have a broad range of applications, with the detection of premovement intentions being a prominent use case. However, the electroencephalography (EEG) features during the premovement phase are not distinctly evident and are susceptible to attentional influences. These limitations impede the enhancement of performance in motor-based BCI. The objective of this study is to establish a premovement BCI encoding paradigm that integrates the preparatory movement state and validates its feasibility in improving the detection of movement intentions.. Two button tasks were designed to induce subjects into a preparation state for two movement intentions (left and right) based on visual guidance, in contrast to spontaneous premovement. The low frequency movement-related cortical potentials (MRCPs) and high frequency event-related desynchronization (ERD) EEG data of 14 subjects were recorded. Extracted features were fused and classified using task related common spatial patterns (CSP) and CSP algorithms. Differences between prepared premovement and spontaneous premovement were compared in terms of time domain, frequency domain, and classification accuracy.. In the time domain, MRCPs features reveal that prepared premovement induce lower amplitude and earlier latency on both contralateral and ipsilateral motor cortex compared to spontaneous premovement, with susceptibility to the dominant hand's influence. Frequency domain ERD features indicate that prepared premovement induce lower ERD values bilaterally, and the ERD recovery speed after button press is the fastest. By using the fusion approach, the classification accuracy increased from 78.92% for spontaneous premovement to 83.59% for prepared premovement (< 0.05). Along with the 4.67% improvement in classification accuracy, the standard deviation decreased by 0.95.. The research findings confirm that incorporating a preparatory state into premovement enhances neural representations related to movement. This encoding enhancement paradigm effectively improves the performance of motor-based BCI. Additionally, this concept has the potential to broaden the range of decodable movement intentions and related information in motor-related BCI.
. 运动相关的脑机接口（BCI）具有广泛的应用，其中预运动意图的检测是一个突出的应用案例。然而，在预运动阶段，脑电图（EEG）特征并不明显，并且容易受到注意力的影响。这些局限性阻碍了基于运动的 BCI 的性能提升。本研究的目的是建立一种预运动 BCI 编码范式，该范式整合了预备运动状态，并验证其在提高运动意图检测方面的可行性。. 设计了两个按钮任务，通过视觉引导，使被试者进入两种运动意图（左和右）的预备状态，与自发的预运动相比。记录了 14 名被试者的低频运动相关皮质电位（MRCPs）和高频事件相关去同步化（ERD）EEG 数据。使用任务相关的共空间模式（CSP）和 CSP 算法融合和分类提取的特征。. 在时域方面，MRCPs 特征显示，与自发的预运动相比，预备的预运动在对侧和同侧运动皮层上诱导出更低的振幅和更早的潜伏期，并且对手的主导性敏感。频域 ERD 特征表明，预备的预运动双侧诱发的 ERD 值较低，并且按下按钮后 ERD 的恢复速度最快。通过使用融合方法，自发预运动的分类准确率从 78.92%提高到 83.59%，差异有统计学意义（<0.05）。与分类准确率提高 4.67%相对应的是，标准偏差降低了 0.95。. 研究结果证实，将预备状态纳入预运动中可以增强与运动相关的神经表示。这种编码增强范式可以有效提高基于运动的 BCI 的性能。此外，这一概念还有潜力拓宽运动相关 BCI 中可解码运动意图和相关信息的范围。
