Faculty of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, Kanagawa, 223-8522, Japan.
Department of Rehabilitation Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo, 160-8582, Japan.
J Neuroeng Rehabil. 2018 Nov 1;15(1):93. doi: 10.1186/s12984-018-0440-5.
Physical motor exercise aided by an electroencephalogram (EEG)-based brain-computer interface (BCI) is known to improve motor recovery in patients with stroke. In such a BCI paradigm, event-related desynchronization (ERD) in the alpha and beta bands extracted from EEG recorded over the primary sensorimotor area (SM1) is often used, since ERD has been suggested to be associated with an increase of corticospinal excitability. Recently, we demonstrated a novel online lock-in amplifier (LIA) algorithm to estimate the amplitude modulation of motor-related SM1 ERD. With this algorithm, the delay time, accuracy, and stability to estimate motor-related SM1 ERD were significantly improved compared with the conventional fast Fourier transformation (FFT) algorithm. These technical improvements to extract an ERD trace imply a potential advantage for a better trace of the excitatory status of the SM1 in a BCI context. Therefore, the aim of this study was to assess the precision of LIA-based ERD tracking for estimation of corticospinal excitability using a transcranial magnetic stimulation (TMS) paradigm.
The motor evoked potentials (MEPs) induced by single-pulse TMS over the primary motor cortex depending on the magnitudes of SM1 ERD (i.e., 35% and 70%) extracted by the online LIA or FFT algorithm were monitored during a motor imagery task of wrist extension in 17 healthy participants. Then, the peak-to-peak amplitudes of MEPs and their variabilities were assessed to investigate the precision of the algorithms.
We found greater MEP amplitude evoked by single-pulse TMS triggered by motor imagery-related alpha SM1 ERD than at rest. This enhancement was associated with the magnitude of ERD in both FFT and LIA algorithms. Moreover, we found that the variabilities of peak-to-peak MEP amplitudes at 35% and 70% ERDs calculated by the novel online LIA algorithm were smaller than those extracted using the conventional FFT algorithm.
The present study demonstrated that the calculation of motor imagery-related SM1 ERDs using the novel online LIA algorithm led to a more precise estimation of corticospinal excitability than when the ordinary FFT-based algorithm was used.
基于脑电图（EEG）的脑机接口（BCI）辅助的物理运动锻炼已被证明可以改善中风患者的运动功能恢复。在这种 BCI 范式中，通常使用从初级感觉运动区（SM1）记录的 EEG 中提取的 alpha 和 beta 频段的事件相关去同步（ERD），因为 ERD 已被证明与皮质脊髓兴奋性的增加有关。最近，我们展示了一种新的在线锁相放大器（LIA）算法，用于估计与运动相关的 SM1 ERD 的幅度调制。与传统的快速傅里叶变换（FFT）算法相比，该算法显著提高了估计与运动相关的 SM1 ERD 的延迟时间、准确性和稳定性。这些提取 ERD 轨迹的技术改进意味着在 BCI 环境中，更好地跟踪 SM1 的兴奋性状态具有潜在优势。因此，本研究的目的是使用经颅磁刺激（TMS）范式评估基于 LIA 的 ERD 跟踪对皮质脊髓兴奋性估计的精度。
在 17 名健康参与者的腕部伸展运动想象任务期间，监测由初级运动皮层的单次脉冲 TMS 诱导的运动诱发电位（MEP），这些 MEP 取决于由在线 LIA 或 FFT 算法提取的 SM1 ERD（即 35%和 70%）的幅度。然后，评估 MEP 峰值-峰值幅度及其变异性，以研究算法的精度。
我们发现，与静息状态相比，由与运动想象相关的 alpha SM1 ERD 触发的单次脉冲 TMS 诱发的 MEP 幅度更大。这种增强与 FFT 和 LIA 算法中的 ERD 幅度相关。此外，我们发现，使用新型在线 LIA 算法计算的 35%和 70%ERD 时的 MEP 峰值-峰值幅度变异性小于使用传统 FFT 算法提取的变异性。
本研究表明，与普通基于 FFT 的算法相比，使用新型在线 LIA 算法计算与运动想象相关的 SM1 ERD 可更精确地估计皮质脊髓兴奋性。
