Abstract
Emotions are viewed as an important aspect of human interactions and conversations, and allow effective and logical decision making. Emotion recognition uses low-cost wearable electroencephalography (EEG) headsets to collect brainwave signals and interpret these signals to provide information on the mental state of a person, with the implementation of a virtual reality environment in different applications; the gap between human and computer interaction, as well as the understanding process, would shorten, providing an immediate response to an individual’s mental health. This study aims to use a virtual reality (VR) headset to induce four classes of emotions (happy, scared, calm, and bored), to collect brainwave samples using a low-cost wearable EEG headset, and to run popular classifiers to compare the most feasible ones that can be used for this particular setup. Firstly, we attempt to build an immersive VR database that is accessible to the public and that can potentially assist with emotion recognition studies using virtual reality stimuli. Secondly, we use a low-cost wearable EEG headset that is both compact and small, and can be attached to the scalp without any hindrance, allowing freedom of movement for participants to view their surroundings inside the immersive VR stimulus. Finally, we evaluate the emotion recognition system by using popular machine learning algorithms and compare them for both intra-subject and inter-subject classification. The results obtained here show that the prediction model for the four-class emotion classification performed well, including the more challenging inter-subject classification, with the support vector machine (SVM Class Weight kernel) obtaining 85.01% classification accuracy. This shows that using less electrode channels but with proper parameter tuning and selection features affects the performance of the classifications.
Highlights
Emotions are viewed as an important aspect of human interactions and conversations, and allow effective and logical decision-making [1]
The experiment commenced by using the intra-subject variability approach to perform the emotion classification, i.e., a common experimentation approach used in prior studies due to its lower complexity and higher classification accuracy results
The used dataset used for this experiment was conducted u forest (DRF), gradient boosting machine (GBM), and naïve
Summary
Emotions are viewed as an important aspect of human interactions and conversations, and allow effective and logical decision-making [1]. To understand how these responses are made or decided, multiple neurophysiological devices collect the bio-signals that are emitted within the human body. Such devices include electrocardiograms (ECGs) [2] which measure the heartbeat; electromyograms (EMGs) [3], which measure muscle movements; electrodermal activity (EDA), which measures skin conductance; electrooculograms (EOGs), which measure eye movements; and electroencephalography (EEG) [4,5,6,7,8,9,10], which measures brainwave signals directly from the brain. Emotion recognition using EEG signals has attracted many researchers with the aim of understanding the evocation of the emotional responses from the human brain [9,11,12,13].
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