Pragmatic Approach for EEG-based Affect Classification

Abstract

Mapping human cognition into automated analysis is the key area of research due to its fascinating applications in almost every area of developing artificially intelligent machines. The best way to understand the functioning of brain is to study electroencephalogram (EEG) patterns, therefore, a lot of research has been directed towards studying EEG signals. Since, EEG recordings are subject dependent and exhibit variations due to external influences or type of recording instruments, it is hard to develop a generalized affect categorization system that can provide robust affect labelling to the EEG patterns. To overcome this, proposed work presents a novel general framework for affect-based cognitive analysis. The proposed system involves following steps: pre-processing, feature selection, Generalized Procrustes Analysis (GPA) step to reduce the inter-class and intra-class variance and finally, the processed pattern is passed on to a trained classifier for classifying the pattern into appropriate affect categories. The presented approach has been tested on single as well as multiple subjects’ EEG data taken from two different datasets, Database for Emotion Analysis using Physiological signals (DEAP) and SJTU Emotion EEG Dataset (SEED) and performance of popular classifiers are assessed. The experimental results suggest that SVM classifier is the best among the selected ones for classifying single as well as mixed subjects’ data.