The automatic detection and classification of epileptic EEG are significant in the evaluation
of patients with epilepsy. This paper presents a new EEG classification approach based on
the extreme learning machine (ELM) and nonlinear dynamical features. The theory of
nonlinear dynamics has been a powerful tool for understanding brain electrical activities.
Nonlinear features extracted from EEG signals such as approximate entropy (ApEn), Hurst
exponent and scaling exponent obtained with detrended fluctuation analysis (DFA) are …