Comparison of Non-linear and Linear Models of Single Channel EEG in patients and normal subjects

Abstract

AbstractThis article examines the possibility of using non-linear models(Support Vector Regression) to model the single channel EEG signals from psychiatric patients and a group of normal participants, to predict psychology trait ratings, like attention, anxiety, alertness, fatigue, sleepiness and depression. It used linear models as benchmarks, and the results showed non-linear models outperformed the benchmarks, as well as more advanced linear methods, like principle component regression. It is thus concluded that using single channel in practical situations to monitor these traits would be possible.