Bi-directional Long Short-Term Memory model to analyze psychological effects on gamers

Abstract

With the increasing popularity of android gaming applications on smart phones, detection of emotional states of hard-core gamers become the interest of study among psychologists. Although there exist a few interesting research works on the impact of video games over the child and adult group, most of them are only able to throw light on psychological aspects associated with the said cognitive task. The real-time detection of emotional states of the player while playing video game is still an unexplored area of research. The present work feels the void by proposing a novel scheme of detecting the emotional changes of human subject from their electroencephalographic (EEG) signal acquired during their engagement in playing video games. The problem is formulated in the settings of pattern classification, which involves four main steps: Data collection, pre-processing and artifact removal, feature extraction and classification.The novelty of the work lies in extracting the emotional content with a high recognition rate from the acquired EEG response using a deep learning algorithm. The primary contribution of the paper lies in efficient usage of a novel phase-sensitive Common Spatial Pattern algorithm for feature extraction and design of an attention-based Bi-directional Long Short-Term Memory (Bi-LSTM) network for classifying the emotional states of a video-game player into five classes: happiness, sadness, surprise, anger and neutral. Moreover, the scarcity of labeled data in EEG-based brain–computer​ interfacing (BCI) tasks is a serious issue while understanding the performance capabilities of the data-driven deep-learning models. Therefore, the present work also makes an attempt to handle the scarcity in the dimension of the extracted feature using a novel feature augmentation algorithm before feeding the feature-vector to the proposed Bi-LSTM network. Experiments undertaken yield productive and conclusive results that validate the efficacy of the proposed framework with the accuracy rate of 88.71%.