A Smart Ambulatory Cognitive State Taxonomy System Through EEG Signal Analysis

Human mental state classification using Electroen-cephalogram(EEG) signal is one of the dynamic investigating areas in developing intelligent machine frameworks. In this paper, we propose an ambulatory cognitive state classification system using statistical feature analysis. The aim is to develop a realtime system which captures human cognitive states through computer-mediated help based on physiological signals. The walking cognitive state estimator is developed using a realtime Augmented Cognition (AugCog) framework. Six different normality testing methods i.e. Kolmogorov-Smirnov, Anderson Darling, Cramer-Von Mises, Shapiro Wilk, Jarque-Bera, D'Ago-stino and Pearson's test are applied to achieve better accuracy. Box - Cox - transformation is also applied to ensure whether the data follows a normal distribution. We apply Fisher Discriminant Ratio (FDR) for selecting the most discriminative feature amongst all the statistical features (i.e. Mean, Median, Mode, Standard Deviation, Variance, Skewness and Kurtosis) obtained from the signal. Various machine learning methods are applied on the extracted features for training, testing and validation. Probabilistic neural network (P-NN) with Kurtosis feature is found to produces a maximum accuracy of 92.61 % as compared to other classifiers.