On detection of pathological tremor in electroencephalograms

This study examines the feasibility of online detection of tremor-related component in noninvasively acquired multichannel electroencephalographic (EEG) signals. In particular, performances of different feature extraction techniques, ranging from time-frequency and time-scale analysis to blind source separation of EEG signals are mutually compared and their suitability for online tremor detection in EEG discussed. The results on EEG signals from five tremor-affected patients demonstrate that, under constraint of high frequency resolution, the time-frequency analysis combined with support vector machine classifiers offers acceptable accuracy in tremor detections (sensitivity ≥ 90% at specificity of 90% or above). The other tested approaches either fail to reliably identify the presence of the tremor-related EEG component or suffer from large inter-subject and/or inter-trail variability.