EEG alpha activity detection by fuzzy reasoning

Automated systems are needed to assist the tedious visual analysis of polygraphic recordings. Most systems need the detection of different electroencephalogram (EEG) waveforms. The problem in the automated detection of alpha activity is the large inter-individual variability of its amplitude and duration. In this work, a fuzzy reasoning-based method for the detection of alpha activity was designed and tested. The ranges of the fuzzy rules were determined based on feature statistics. The advantage of the presented detector is that no alpha amplitude threshold needs to be selected. The performance of the alpha detector with four modifications was assessed with ROC curves. When the true positive rate was 85%, the false positive rate was 13%, which is sufficient for sleep EEG analysis.