EEG dataset for energy data visualizations
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)...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-02-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340923009654 |
| _version_ | 1797317097940844544 |
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| author | Omer Faruk Kucukler Abbes Amira Hossein Malekmohamadi |
| author_facet | Omer Faruk Kucukler Abbes Amira Hossein Malekmohamadi |
| author_sort | Omer Faruk Kucukler |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T03:29:55Z |
| format | Article |
| id | doaj.art-06fc0bc02a604ba88cee4f9a8891e715 |
| institution | Directory Open Access Journal |
| issn | 2352-3409 |
| language | English |
| last_indexed | 2024-03-08T03:29:55Z |
| publishDate | 2024-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj.art-06fc0bc02a604ba88cee4f9a8891e7152024-02-11T05:10:34ZengElsevierData in Brief2352-34092024-02-0152109933EEG dataset for energy data visualizationsOmer Faruk Kucukler0Abbes Amira1Hossein Malekmohamadi2Institute of Artificial Intelligence, De Montfort University, Leicester, UK; Corresponding author.Institute of Artificial Intelligence, De Montfort University, Leicester, UK; Department of Computer Science, University of Sharjah, Sharjah, UAEInstitute of Artificial Intelligence, De Montfort University, Leicester, UKUser 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.http://www.sciencedirect.com/science/article/pii/S2352340923009654ElectroencephalographyEnergy efficiencyData visualizationBrain-computer interfaceHuman-computer interactionGenerative adversarial networks |
| spellingShingle | Omer Faruk Kucukler Abbes Amira Hossein Malekmohamadi EEG dataset for energy data visualizations Data in Brief Electroencephalography Energy efficiency Data visualization Brain-computer interface Human-computer interaction Generative adversarial networks |
| title | EEG dataset for energy data visualizations |
| title_full | EEG dataset for energy data visualizations |
| title_fullStr | EEG dataset for energy data visualizations |
| title_full_unstemmed | EEG dataset for energy data visualizations |
| title_short | EEG dataset for energy data visualizations |
| title_sort | eeg dataset for energy data visualizations |
| topic | Electroencephalography Energy efficiency Data visualization Brain-computer interface Human-computer interaction Generative adversarial networks |
| url | http://www.sciencedirect.com/science/article/pii/S2352340923009654 |
| work_keys_str_mv | AT omerfarukkucukler eegdatasetforenergydatavisualizations AT abbesamira eegdatasetforenergydatavisualizations AT hosseinmalekmohamadi eegdatasetforenergydatavisualizations |