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A dataset of EEG signals from a single-channel SSVEP-based brain computer interface
Giovanni Acampora1,2, Pasquale Trinchese2, Autilia Vitiello2
1Istituto Nazionale di Fisica Nucleare, Sezione di Napoli, 80126 Naples, Italy.
Data in Brief
|March 4, 2021
Summary
This study introduces a new electroencephalography dataset for brain-computer interfaces (BCI) using a comfortable, single-channel dry sensor. This data supports research in Internet of Things (IoT) applications and human-computer interaction.
Area of Science:
- Neuroscience
- Computer Science
- Human-Computer Interaction
Background:
- Brain-computer interfaces (BCI) traditionally use cumbersome equipment.
- There is a growing need for user-friendly BCI for broader applications.
- Internet of Things (IoT) applications can benefit from seamless human-machine integration.
Purpose of the Study:
- To present a novel electroencephalography (EEG) dataset.
- To facilitate research in portable Steady State Visual Evoked Potentials (SSVEP)-based BCI.
- To support the development of next-generation IoT applications.
Main Methods:
- Acquisition of EEG data using repetitive visual stimuli.
- Experiments utilized four distinct flickering frequencies.
- Employed a single-channel dry-sensor acquisition device for enhanced user comfort.
Main Results:
- A comprehensive dataset of SSVEP-BCI data was collected.
- The use of a dry-sensor device demonstrated improved user comfort.
- The dataset is suitable for various human-computer interaction research.
Conclusions:
- The presented dataset is a valuable resource for SSVEP-BCI research.
- The single-channel dry-sensor approach shows promise for future BCI applications.
- This work contributes to the advancement of IoT-integrated human-computer interaction.
