EEG analysis for understanding stress based on affective model basis function

Coping with stress has shown to be able to avoid many complications in medical condition. In this paper we present an alternative method in analyzing and understanding stress using the four basic emotions of happy, calm, sad and fear as our basis function. Electroencephalogram (EEG) signals were captured from the scalp of the brain and measured in responds to various stimuli from the four basic emotions to stimulating stress base on the IAPS emotion stimuli. Features from the EEG signals were extracted using the Kernel Density Estimation (KDE) and classified using the Multilayer Perceptron (MLP), a neural network classifier to obtain accuracy of the subject’s emotion leading to stress. Results have shown the potential of using the basic emotion basis function to visualize the stress perception as an alternative tool for engineers and psychologist. Keywords: Electroencephalography (EEG), Kernel Density Estimation (KDE), Multi-layer Perceptron (MLP), Valance (V), Arousal (A

Analyzing brain activity in understanding cultural and language interaction for depression and anxiety

Human brain has always been considered as a black box and is the source of all emotions. Analyzing cultural and language role through human emotion by looking at the brain activity can thus help us understand depression and stress better. This paper focuses on understanding and analyzing undergraduate students’ emotions with different background and culture after completing their semester final examination. Brain wave signals were captured using EEG device and analyzed through proposing an affective computation model. EEG signal was collected from 8 healthy subjects from different states of Malaysia with different dialects where each subject was emotionally induced with audio and video emotion stimuli using the International Affective Pictures and System (IAPS). Features were extracted from the captured EEG signals using Kernel Density Estimation (KDE), which was then categorized into four basic emotions of happy, calm, sad and fear using the Multi-layer Perceptron (MLP). Results of the study show potential of using such analysis in understanding stress, anxiety and depression