Defitech Chair in Non-Invasive Brain-Machine Interface, Center for Neuroprosthetics, School of Engineering, Ecole Polytechnique Fédérale de Lausanne Lausanne, Switzerland ; Laboratory for Experimental Research on Behavior, Institute of Psychology, University of Lausanne Lausanne, Switzerland.
Defitech Chair in Non-Invasive Brain-Machine Interface, Center for Neuroprosthetics, School of Engineering, Ecole Polytechnique Fédérale de Lausanne Lausanne, Switzerland.
Front Neurosci. 2014 Aug 1;8:222. doi: 10.3389/fnins.2014.00222. eCollection 2014.
Early detection of movement intention could possibly minimize the delays in the activation of neuroprosthetic devices. As yet, single trial analysis using non-invasive approaches for understanding such movement preparation remains a challenging task. We studied the feasibility of predicting movement directions in self-paced upper limb center-out reaching tasks, i.e., spontaneous movements executed without an external cue that can better reflect natural motor behavior in humans. We reported results of non-invasive electroencephalography (EEG) recorded from mild stroke patients and able-bodied participants. Previous studies have shown that low frequency EEG oscillations are modulated by the intent to move and therefore, can be decoded prior to the movement execution. Motivated by these results, we investigated whether slow cortical potentials (SCPs) preceding movement onset can be used to classify reaching directions and evaluated the performance using 5-fold cross-validation. For able-bodied subjects, we obtained an average decoding accuracy of 76% (chance level of 25%) at 62.5 ms before onset using the amplitude of on-going SCPs with above chance level performances between 875 to 437.5 ms prior to onset. The decoding accuracy for the stroke patients was on average 47% with their paretic arms. Comparison of the decoding accuracy across different frequency ranges (i.e., SCPs, delta, theta, alpha, and gamma) yielded the best accuracy using SCPs filtered between 0.1 to 1 Hz. Across all the subjects, including stroke subjects, the best selected features were obtained mostly from the fronto-parietal regions, hence consistent with previous neurophysiological studies on arm reaching tasks. In summary, we concluded that SCPs allow the possibility of single trial decoding of reaching directions at least 312.5 ms before onset of reach.
早期检测运动意图可能会最大限度地减少神经假体激活的延迟。然而，使用非侵入性方法来理解这种运动准备的单次试验分析仍然是一项具有挑战性的任务。我们研究了在自我启动的上肢中心到中心伸出任务（即无需外部提示即可执行的自发性运动，可更好地反映人类的自然运动行为）中预测运动方向的可行性，我们报告了轻度中风患者和健康参与者的非侵入性脑电图（EEG）记录的结果。先前的研究表明，低频 EEG 振荡受运动意图的调制，因此可以在运动执行之前进行解码。受这些结果的启发，我们研究了运动起始前的慢皮层电位（SCP）是否可以用于分类运动方向，并使用 5 倍交叉验证评估性能。对于健康受试者，我们在运动开始前 62.5ms 时使用正在进行的 SCP 幅度获得了平均解码准确率为 76%（25%的机会水平），在运动开始前 875 到 437.5ms 之间具有高于机会水平的表现。中风患者的平均解码准确率为 47%，且为其瘫痪手臂的结果。对不同频率范围（即 SCP、delta、theta、alpha 和 gamma）的解码准确性进行比较，结果表明 0.1 到 1Hz 之间滤波的 SCP 具有最佳的准确性。在所有受试者中，包括中风患者，最佳选择的特征主要来自额顶区域，因此与之前关于手臂伸展任务的神经生理学研究一致。总之，我们得出结论，SCP 允许至少在运动开始前 312.5ms 进行单次试验解码的可能性。
