Xu Kunyu, Huang Yu-Yu, Duann Jeng-Ren
Institute of Cognitive Neuroscience, National Central University, Taoyuan, Taiwan.
Institute for Neural Computation, University of California, San Diego, San Diego, CA, United States.
Front Hum Neurosci. 2019 Aug 30;13:302. doi: 10.3389/fnhum.2019.00302. eCollection 2019.
Motor imagery (MI) has been widely used to operate brain-computer interface (BCI) systems for rehabilitation and some life assistive devices. However, the current performance of an MI-based BCI cannot fully meet the needs of its in-field applications. Most of the BCIs utilizing a generalized feature for all participants have been found to greatly hamper the efficacy of the BCI system. Hence, some attempts have made on the exploration of subject-dependent parameters, but it remains challenging to enhance BCI performance as expected. To this end, in this study, we used the independent component analysis (ICA), which has been proved capable of isolating the pure motor-related component from non-motor-related brain processes and artifacts and extracting the common motor-related component across MI, motor execution (ME), and motor observation (MO) conditions. Then, a sliding window approach was used to detect significant mu-suppression from the baseline using the electroencephalographic (EEG) alpha power time course and, thus, the success rate of the mu-suppression detection could be assessed on a single-trial basis. By comparing the success rates using different parameters, we further quantified the extent of the improvement in each motor condition to evaluate the effectiveness of both generalized and individualized parameters. The results showed that in ME condition, the success rate under individualized latency and that under generalized latency was 90.0% and 77.75%, respectively; in MI condition, the success rate was 74.14% for individual latency and 58.47% for generalized latency, and in MO condition, the success rate was 67.89% and 61.26% for individual and generalized latency, respectively. As can be seen, the success rate in each motor condition was significantly improved by utilizing an individualized latency compared to that using the generalized latency. Moreover, the comparison of the individualized window latencies for the mu-suppression detection across different runs of the same participant as well as across different participants showed that the window latency was significantly more consistent in the intra-subject than in the inter-subject settings. As a result, we proposed that individualizing the latency for detecting the mu-suppression feature for each participant might be a promising attempt to improve the MI-based BCI performance.
运动想象（MI）已被广泛用于操作脑机接口（BCI）系统，以用于康复治疗和一些生活辅助设备。然而，基于MI的BCI目前的性能尚不能完全满足其实际应用的需求。大多数为所有参与者使用通用特征的BCI已被发现极大地阻碍了BCI系统的功效。因此，人们已尝试探索依赖于个体的参数，但要按预期提高BCI性能仍具有挑战性。为此，在本研究中，我们使用了独立成分分析（ICA），该方法已被证明能够从与运动无关的脑电活动和伪迹中分离出与运动相关的纯成分，并提取跨运动想象（MI）、运动执行（ME）和运动观察（MO）条件下的共同运动相关成分。然后，采用滑动窗口方法，利用脑电图（EEG）α波功率时间进程从基线中检测显著的μ波抑制，从而可以在单次试验的基础上评估μ波抑制检测的成功率。通过比较使用不同参数时的成功率，我们进一步量化了每种运动条件下的改善程度，以评估通用参数和个性化参数的有效性。结果表明，在ME条件下，个性化潜伏期和通用潜伏期下的成功率分别为90.0%和77.75%；在MI条件下，个性化潜伏期和通用潜伏期下的成功率分别为74.14%和58.47%，在MO条件下，个性化潜伏期和通用潜伏期下的成功率分别为67.89%和61.26%。可以看出，与使用通用潜伏期相比，利用个性化潜伏期可显著提高每种运动条件下的成功率。此外，对同一参与者不同试验以及不同参与者之间用于μ波抑制检测的个性化窗口潜伏期的比较表明，窗口潜伏期在个体内部比个体之间的设置中更具一致性。因此，我们提出为每个参与者个性化检测μ波抑制特征的潜伏期可能是提高基于MI的BCI性能的一种有前景的尝试。
