Gabsteiger Florian, Leutheuser Heike, Reis Pedro, Lochmann Matthias, Eskofier Bjoern M
Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:3861-4. doi: 10.1109/EMBC.2014.6944466.
Analysis of electroencephalography (EEG) recorded during movement is often aggravated or even completely hindered by electromyogenic artifacts. This is caused by the overlapping frequencies of brain and myogenic activity and the higher amplitude of the myogenic signals. One commonly employed computational technique to reduce these types of artifacts is Independent Component Analysis (ICA). ICA estimates statistically independent components (ICs) that, when linearly combined, closely match the input (sensor) data. Removing the ICs that represent artifact sources and re-mixing the sources returns the input data with reduced noise activity. ICs of real-world data are usually not perfectly separated, actual sources, but a mixture of these sources. Adding additional input signals, predominantly generated by a single IC that is already part of the original sensor data, should increase that IC's separability. We conducted this study to evaluate this concept for ICA-based electromyogenic artifact reduction in EEG using EMG signals as additional inputs. To acquire the appropriate data we worked with nine human volunteers. The EEG and EMG were recorded while the study volunteers performed seven exercises designed to produce a wide range of representative myogenic artifacts. To evaluate the effect of the EMG signals we estimated the sources of each dataset once with and once without the EMG data. The ICs were automatically classified as either myogenic' or non-myogenic'. We removed the former before back projection. Afterwards we calculated an objective measure to quantify the artifact reduction and assess the effect of including EMG signals. Our study showed that the ICA-based reduction of electromyogenic artifacts can be improved by including the EMG data of artifact-inducing muscles. This approach could prove beneficial for locomotor disorder research, brain-computer interfaces, neurofeedback, and most other areas where brain activity during movement has to be analyzed.
运动过程中记录的脑电图(EEG)分析常常会因肌电伪迹而加剧,甚至完全受阻。这是由大脑和肌源性活动的频率重叠以及肌源性信号的较高幅度所导致的。一种常用的减少此类伪迹的计算技术是独立成分分析(ICA)。ICA估计统计独立成分(IC),这些成分在进行线性组合时,能与输入(传感器)数据紧密匹配。去除代表伪迹源的IC并重新混合这些源,可使输入数据的噪声活动降低。实际数据的IC通常并非完美分离的实际源,而是这些源的混合。添加主要由已作为原始传感器数据一部分的单个IC生成的额外输入信号,应能提高该IC的可分离性。我们开展这项研究,以评估使用肌电图(EMG)信号作为额外输入来基于ICA减少EEG中肌电伪迹的这一概念。为获取合适的数据,我们与九名人类志愿者合作。在研究志愿者进行旨在产生广泛代表性肌电伪迹的七项运动时,记录EEG和EMG。为评估EMG信号的效果,我们分别在有和没有EMG数据的情况下对每个数据集估计一次源。IC会自动分类为“肌源性”或“非肌源性”。在反向投影之前,我们去除前者。之后,我们计算一个客观指标来量化伪迹减少情况,并评估包含EMG信号的效果。我们的研究表明,通过纳入诱发伪迹肌肉的EMG数据,可以改进基于ICA的肌电伪迹减少方法。这种方法可能对运动障碍研究、脑机接口、神经反馈以及大多数其他必须分析运动过程中大脑活动的领域有益。