Leutheuser Heike, Gabsteiger Florian, Hebenstreit Felix, Reis Pedro, Lochmann Matthias, Eskofier Bjoern
Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:6804-7. doi: 10.1109/EMBC.2013.6611119.
Electromyogenic or muscle artifacts constitute a major problem in studies involving electroencephalography (EEG) measurements. This is because the rather low signal activity of the brain is overlaid by comparably high signal activity of muscles, especially neck muscles. Hence, recording an artifact-free EEG signal during movement or physical exercise is not, to the best knowledge of the authors, feasible at the moment. Nevertheless, EEG measurements are used in a variety of different fields like diagnosing epilepsy and other brain related diseases or in biofeedback for athletes. Muscle artifacts can be recorded using electromyography (EMG). Various computational methods for the reduction of muscle artifacts in EEG data exist like the ICA algorithm InfoMax and the AMICA algorithm. However, there exists no objective measure to compare different algorithms concerning their performance on EEG data. We defined a test protocol with specific neck and body movements and measured EEG and EMG simultaneously to compare the InfoMax algorithm and the AMICA algorithm. A novel objective measure enabled to compare both algorithms according to their performance. Results showed that the AMICA algorithm outperformed the InfoMax algorithm. In further research, we will continue using the established objective measure to test the performance of other algorithms for the reduction of artifacts.
肌源性或肌肉伪迹在涉及脑电图（EEG）测量的研究中是一个主要问题。这是因为大脑相当低的信号活动被肌肉，尤其是颈部肌肉相对较高的信号活动所叠加。因此，据作者所知，目前在运动或体育锻炼期间记录无伪迹的EEG信号是不可行的。然而，EEG测量被用于各种不同领域，如诊断癫痫和其他脑部相关疾病或用于运动员的生物反馈。可以使用肌电图（EMG）记录肌肉伪迹。存在各种用于减少EEG数据中肌肉伪迹的计算方法，如独立成分分析（ICA）算法InfoMax和自适应混合独立成分分析（AMICA）算法。然而，不存在客观的度量来比较不同算法在EEG数据上的性能。我们定义了一个包含特定颈部和身体运动的测试方案，并同时测量EEG和EMG，以比较InfoMax算法和AMICA算法。一种新颖的客观度量能够根据其性能比较这两种算法。结果表明，AMICA算法优于InfoMax算法。在进一步的研究中，我们将继续使用既定的客观度量来测试其他用于减少伪迹的算法的性能。
