EEG dataset for energy data visualizations

Omer Faruk Kucukler; Abbes Amira; Hossein Malekmohamadi

doi:10.1016/j.dib.2023.109933

EEG dataset for energy data visualizations

Data Brief. 2023 Dec 10:52:109933. doi: 10.1016/j.dib.2023.109933. eCollection 2024 Feb.

Authors

Omer Faruk Kucukler¹, Abbes Amira^{1

2}, Hossein Malekmohamadi¹

Affiliations

¹ Institute of Artificial Intelligence, De Montfort University, Leicester, UK.
² Department of Computer Science, University of Sharjah, Sharjah, UAE.

Abstract

User behavior plays a substantial role in shaping household energy use. Nevertheless, the methodologies employed by researchers to examine user behavior exhibit certain limitations in terms of their reach. The present article introduces an openly accessible collection of electroencephalography (EEG) recordings, comprising EEG data collected from individuals who were subjected to energy data visualizations. The dataset comprises EEG recordings obtained from 28 individuals who were in good health. The EEG recordings were collected using a 32-channel EMOTIV EEG device, and the international 10-20 electrode system was employed for precise electrode placement. The energy data visualizations were generated and showcased utilizing the PsychoPy software. To ascertain the participants' affective state, they were requested to rate the valence and arousal of each stimulus through the utilization of a self-assessment manikin (SAM). Additionally, three inquiries were posed for every stimulation. The dataset includes both original data visualizations and ratings. Additionally, the raw EEG data has been divided into segments consisting of data visualizations and neutral images, with the use of event markers, in order to assist analysis. The EEG recordings were recorded and stored utilizing the EMOTIVPro application, whereas the subjective reactions were captured and preserved using the PsychoPy application. Furthermore, the generation of synthetic EEG data is accomplished by employing the Generative Adversarial Network (GAN) architecture on the acquired EEG dataset. The synthetic EEG data created is integrated with empirical EEG data, and afterwards subjected to qualitative and quantitative analysis in order to improve performance. The dataset presented herein showcases a pioneering utilization of EEG investigation and offers a valuable foundation for scholars in the domains of computer science, energy conservation, artificial intelligence, brain-computer interfaces, and human-computer interaction.

Keywords: Brain-computer interface; Data visualization; Electroencephalography; Energy efficiency; Generative adversarial networks; Human-computer interaction.