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EEG dataset for energy data visualizations.

Omer Faruk Kucukler1, Abbes Amira1,2, Hossein Malekmohamadi1

  • 1Institute of Artificial Intelligence, De Montfort University, Leicester, UK.

Data in Brief
|December 21, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel dataset of electroencephalography (EEG) recordings from individuals viewing energy data visualizations. This resource supports research in energy conservation and human-computer interaction.

Keywords:
Brain-computer interfaceData visualizationElectroencephalographyEnergy efficiencyGenerative adversarial networksHuman-computer interaction

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Area of Science:

  • Neuroscience
  • Energy Science
  • Computer Science

Background:

  • User behavior significantly impacts household energy consumption.
  • Current research methods for studying user behavior have limitations in scope.
  • Understanding cognitive and affective responses to energy data is crucial.

Purpose of the Study:

  • To introduce a publicly available dataset of electroencephalography (EEG) recordings.
  • To facilitate research on user responses to energy data visualizations.
  • To provide a foundation for advancements in energy conservation and human-computer interaction.

Main Methods:

  • Collected EEG data from 28 healthy individuals using a 32-channel EMOTIV device and the international 10-20 electrode system.
  • Generated energy data visualizations with PsychoPy software and recorded participants' affective states using Self-Assessment Manikin (SAM) and questionnaires.
  • Utilized Generative Adversarial Networks (GANs) to create synthetic EEG data, integrated with empirical data for enhanced analysis.

Main Results:

  • The dataset includes raw EEG recordings, segmented data (visualizations, neutral images), subjective ratings (valence, arousal), and synthetic EEG data.
  • Event markers were used to segment EEG data corresponding to specific stimuli.
  • Generated synthetic EEG data using GANs and integrated it with real EEG data for analysis.

Conclusions:

  • The presented dataset is a pioneering resource for studying brain activity related to energy data visualization.
  • Offers a valuable foundation for researchers in computer science, energy conservation, AI, brain-computer interfaces, and HCI.
  • Enables novel investigations into the cognitive and affective dimensions of energy consumption behavior.