Muscle artifacts in multichannel EEG: characteristics and reduction.

ABSTRACT

OBJECTIVE: To study the characteristics of unintentional muscle activities in clinical EEG, and to develop a high-throughput method to reduce them for better revealing drug or biological effects on EEG. METHODS: Two clinical EEG datasets are involved. Pure muscle signals are extracted from EEG using Independent Component Analysis (ICA) for studying their characteristics. A high-throughput method called ICA-SR is introduced based on a new feature named Spectral Ratio (SR). RESULTS: The spectral and temporal characteristics of the muscle artifacts are illustrated using representative muscle signals. The spatial characteristics are presented at both the group- and the subject-level, and are consistent under three different electrode reference methodologies. Objectively compared with an existing method, ICA-SR is shown to reduce more artifacts, while introduce less distortion to EEG. Its effectiveness is further demonstrated in real clinical EEG with the help of a CO(2)-inhalation EEG recording session. CONCLUSION: The characteristics of unintentional muscle activities align with the reported characteristics of controlled muscle activities. Artifact spatial characteristics can be EEG equipment dependent. The ICA-SR method can effectively and efficiently process clinical EEG. SIGNIFICANCE: Armed with advanced signal processing algorithms, this study expands our knowledge of muscle activities in EEG from muscle-controlled experiments to general clinical trials. The ICA-SR method provides an urgently needed solution with validated performance for efficiently processing large volumes of clinical EEG.