Comparison of Two Algorithms to Reduce Muscular Movement Artifacts in EEG Data

Muscular movement artifacts constitute a major problem in studies involving electroencephalography (EEG) measurements. EEG measurements are used in a variety of different fields like diagnosing epilepsy and other brain related diseases or in biofeedback for athletes. A major drawback is that EEG is susceptible to artifacts of neck muscles due to the low signal amplitude of the electrical activity of the brain. Hence, recording an artifact-free EEG signal during movement or physical exercise is not feasible at the moment. These additional artifacts can be recorded using electromyography (EMG). Various computational methods for the reduction of muscle artifacts in EEG data exist like the ICA algorithm 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 ICA algorithm InfoMax 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 ICA algorithm. In further research, we will continue using our novel objective measure to test the performance of other artifact removal algorithms.