Database for an emotion recognition system based on EEG signals and various computer games - GAMEEMO - Alakus, Talha Burak; Gonen, Murat; Turkoglu, Ibrahim (Elsevier Sci Ltd, 2020)
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. (C) 2020 Elsevier Ltd. All rights reserved.
Keywords: Emotion estimation; EEG signals; Pattern recognition; Signal processing; Wearable technology; Computer games