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An 18-subject EEG data collection using a visual-oddball task, designed for benchmarking algorithms and headset

Kay Robbins1, Kyung-Min Su1, W David Hairston2

  • 1Department of Computer Science, University of Texas-San Antonio, San Antonio, TX 78249, USA.

Data in Brief
|December 12, 2017
PubMed
Summary
This summary is machine-generated.

This study releases electroencephalogram (EEG) data from 18 subjects performing a visual oddball task. The dataset supports benchmarking various EEG analysis algorithms, including artifact detection and blink classification.

Keywords:
EEGEEG study format (ESS)EEGLABHierarchical event descriptor (HED) tagsVisually evoked potential

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Data Science

Background:

  • The visual oddball task is a standard paradigm for eliciting event-related potentials.
  • Electroencephalogram (EEG) data is crucial for understanding brain activity and developing neuro-computational tools.
  • Standardized datasets are essential for reproducible research and algorithm development in EEG analysis.

Purpose of the Study:

  • To release a well-characterized EEG dataset for the benchmarking of various signal processing and machine learning algorithms.
  • To provide a resource for researchers developing and testing methods for artifact detection, classification, and automated annotation of EEG data.
  • To facilitate the advancement of EEG analysis techniques through open data sharing.

Main Methods:

  • Acquisition of 64-channel EEG, 4-channel EOG, and 2 mastoid reference channels using a Biosemi Active 2 EEG headset.
  • Data collected from 18 human subjects performing a standard visual oddball task.
  • Data provided in three formats to maximize compatibility and usability for diverse algorithmic approaches.

Main Results:

  • A comprehensive dataset comprising EEG and EOG recordings from 18 subjects is now publicly available.
  • The dataset has been utilized in multiple research projects for algorithm development and validation.
  • The data facilitates the testing of algorithms for artifact detection, classification, transfer learning, preprocessing, blink detection, and automated annotation.

Conclusions:

  • The release of this EEG dataset provides a valuable resource for the scientific community.
  • This data enables rigorous benchmarking and comparison of diverse EEG analysis algorithms.
  • The availability of this dataset promotes reproducibility and accelerates innovation in the field of EEG signal processing and interpretation.