Jiang Ning, Gizzi Leonardo, Mrachacz-Kersting Natalie, Dremstrup Kim, Farina Dario
Department Neurorehabilitaion Engineering, Bernstein Focus Neurotechnology (BFNT) Göttingen, Bernstein Center for Computational Neuroscience (BCCN), University Medical Center Göttingen, Georg-August University, Göttingen, Germany.
Pain Clinic Center for Anesthesiology, Emergency and Intensive Care Medicine, University Hospital Göttingen, Göttingen, Germany.
Clin Neurophysiol. 2015 Jan;126(1):154-9. doi: 10.1016/j.clinph.2014.05.003. Epub 2014 May 20.
Applications of brain-computer interfacing (BCI) in neurorehabilitation have received increasing attention. The intention to perform a motor task can be detected from scalp EEG and used to control rehabilitation devices, resulting in a patient-driven rehabilitation paradigm. In this study, we present and validate a BCI system for detection of gait initiation using movement related cortical potentials (MRCP).
The templates of MRCP were extracted from 9-channel scalp EEG during gait initiation in 9 healthy subjects. Independent component analysis (ICA) was used to remove artifacts, and the Laplacian spatial filter was applied to enhance the signal-to-noise ratio of MRCP. Following these pre-processing steps, a matched filter was used to perform single-trial detection of gait initiation.
ICA preprocessing was shown to significantly improve the detection performance. With ICA preprocessing, across all subjects, the true positive rate (TPR) of the detection was 76.9±8.97%, and the false positive rate was 2.93±1.09 per minute.
The results demonstrate the feasibility of detecting the intention of gait initiation from EEG signals, on a single trial basis.
The results are important for the development of new gait rehabilitation strategies, either for recovery/replacement of function or for neuromodulation.
脑机接口（BCI）在神经康复中的应用受到越来越多的关注。执行运动任务的意图可从头皮脑电图中检测出来，并用于控制康复设备，从而形成一种以患者为驱动的康复模式。在本研究中，我们展示并验证了一种利用运动相关皮层电位（MRCP）检测步态起始的BCI系统。
从9名健康受试者步态起始过程中的9通道头皮脑电图中提取MRCP模板。使用独立成分分析（ICA）去除伪迹，并应用拉普拉斯空间滤波器提高MRCP的信噪比。经过这些预处理步骤后，使用匹配滤波器对步态起始进行单次试验检测。
ICA预处理显示能显著提高检测性能。经过ICA预处理，在所有受试者中，检测的真阳性率（TPR）为76.9±8.97%，假阳性率为每分钟2.93±1.09。
结果证明了在单次试验基础上从脑电信号中检测步态起始意图的可行性。
这些结果对于开发新的步态康复策略很重要，无论是用于功能恢复/替代还是神经调节。
