Database for an emotion recognition system based on EEG signals and various computer games – GAMEEMO

• Published in: Biomedical signal processing and control Vol. 60; p. 101951
• Main Authors: Alakus, Talha Burak, Gonen, Murat, Turkoglu, Ibrahim
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2020
• Subjects: Computer games; EEG signals; Emotion estimation; Pattern recognition; Signal processing; Wearable technology; Signal processing; EEG signals; Pattern recognition; Computer games; Emotion estimation; Wearable technology
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2020.101951

[Display omitted] •Provides EEG-based emotion data with respect to computer games.•EEG data are collected with a wearable and portable device that differs from the traditional approaches.•One of the main ideas of the study is to demonstrate the performance of the wearable EEG device in comparison with the traditional ones.•Another purpose of the study is to show the success of computer games in contrast to the other aural-visual stimuli methods composed of video clips and short movies. In this study, electroencephalography-based data for emotion recognition analysis are introduced. EEG signals were collected from 28 different subjects with a wearable and portable EEG device called the 14-channel EMOTIV EPOC+. Subjects played 4 different computer games that captured emotions (boring, calm, horror and funny) for 5 min, and the EEG data available for each subject consisted of 20 min in total. The subjects rated each computer game based on the scale of arousal and valence by applying the SAM form. We provide both raw and preprocessed EEG data with.csv and. mat format in our data repository. Each subject's rating score and SAM form are also available. With this work, we aim to provide an emotion dataset based on computer games, which is a new method in terms of collecting brain signals. Additionally, we want to determine the success of the portable EEG device and compare the success of this device with classical EEG devices. Finally, we perform pattern recognition and signal-processing methods to observe the performance of our dataset and to classify EEG signals based on the arousal-valence emotion dimension and positive/negative emotions. The database will be publicly available, and researchers can use the dataset for analyzing signals for their own proposed method in the literature.