Human Cognitive State Classification Through Ambulatory Eeg Signal Analysis

Human cognitive state classification using Electroencephalogram (EEG) signal is one of the dynamic exploring regions in emerging smart machine systems. It motivates to build estimator for revealing the human cognitive behaviours for different environments. In this work, we present an operational human cognitive behaviour detection system based on ambulatory EEG signal analysis. A novel event driven environment is created using external stimuli to collect those signals. They are captured using 14 channel Emotiv neuro-headset. Mel-Frequency Cepstral Coefficients (MFCC) is a very popular feature extraction method in acoustic signal processing. In spite of it's popularity, it has not been explored much when it comes to EEG signals. This paper proposes a novel technique of applying MFCC feature extraction method for ambulatory EEG signals and the results are found to be promising. Besides other feature extraction methods such as: Power Spectral Density (PSD), Discrete Wavelet Transformation (DWT) are used to extract intrinsic features from EEG signal. A statistical technique, Fisher Discriminant Ratio (FDR) is applied on them to evaluate the robustness of each feature. A higher FDR value is observed from MFCC features which demonstrate its discriminative power during classification. Different machine learning techniques such as Probabilistic neural network (P-NN), k-nearest neighbor (kNN), multi-class support vector machine (MCSVM) are applied for training, testing and validating the extracted features. A comparative analysis is discussed for each classifier coupled with each feature. It is found that P-NN with MFCC feature produces the maximum accuracy of 92.61% amongst all the remaining classifiers.