Detection of muscle artefact in the normal human awake EEG.

OBJECTIVES: A study was performed to investigate automatic detection of muscle artefact, using time domain and frequency domain methods. The evaluation focussed on epoch length and performance of detection.

METHODS: EEG data were recorded in 21 normal adult subjects for 50 min during awake state. Investigated positions included central, temporal and parietal scalp electrodes. Expert annotation of muscle artefact was performed by accurate visual marking in a randomised test-set of the data, which allowed for intra-expert comparison. For time domain detection, the parameter set consisted of slope and maximum/minimum amplitude. Parameters in the frequency domain were absolute and relative 'high beta' power (>25 Hz) and spectral edge frequency. Distributions as calculated from a reference period in each subject were used to investigate the statistics of the parameter ranges. Detection thresholds were calculated from these distributions per subject, and performance was compared to constant (empirical) thresholds for the entire data set.

RESULTS: Results indicate a 1 s epoch length as optimal for detection of muscle artefact. The analysis using a slope threshold or absolute 'high beta' power showed the best results in sensitivity (80%) and specificity (90%), matching the expert's performance.

CONCLUSIONS: Constant threshold settings performed better than statistical thresholds per subject.