Comparison of the AMICA and the InfoMax algorithm for the reduction of electromyogenic artifacts in EEG data

Abstract

Abstract-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.

Identifying key factors for improving ICA-based decomposition of EEG data in mobile and stationary experiments

“…They found that the best approach was to compute the ICA on filtered continuous data including EXG. However, the applied filter was a band-pass filter of 0.16-40 Hz, which is a low high-pass filter compared to previous studies that used filters of 0.5 Hz or higher (Delorme et al, 2012;Leutheuser et al, 2013).…”

ICA-based reduction of electromyogenic artifacts in EEG data: Comparison with and without EMG data

“…In [12] we introduced a novel measure to calculate the artifact reduction by measuring features on resting state data and to compare them to features from the data before and after the artifact reduction. The steps for calculating the improvement are shown in Fig.…”

“…Using this simulation tool we created data with known artifact reductions, denoted m SNR , in the range of 0 % to 100 % [12]:…”

“…C7 is the approximate location of the seventh cervical vertebra. First shown in [12]. exported to EEGLab [15], a widely used Matlab (MathWorks Inc., Natick, USA) toolbox for EEG data, for further analysis.…”

“…We chose the AMICA algorithm for this study, as it was shown to be superior to the InfoMax algorithm for removing electromyogenic artifacts in EEG data [12]. We used the AMICA implementation by Palmer et al [18].…”