Open access repository for hybrid EEG-NIRS data

Jaeyoung Shin, Alexander Von Luhmann, Benjamin Blankertz, Do Won Kim, Jan Mehnert, Jichai Jeong, Han Jeong Hwang, Klaus Robert Muller

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)


Recently, in order to overcome the disadvantages of unimodal brain-imaging modalities such as low signal-to-noise ratio and vulnerability to motion artifact and to improve system performance, a multimodal imaging system (so-called hybrid system) has been emerging as an attractive alternative. In the present study, to meet the increasing demand on a hybrid brain-imaging data, we introduce open access datasets of electroencephalography (EEG) and near-infrared spectroscopy (NIRS) simultaneously measured during various cognitive tasks. The datasets contain BCI data such as motor imagery (MI)-, and mental arithmetic (MA), and word generation (WG)-related brain signals, and cognitive task data such as n-back (NB)-, and discrimination/selection response (DSR)-related brain signals. We provide the reference results of these datasets, which were validated using analysis pipelines widely used in related research fields. In particular, it was confirmed from classification analysis that a hybrid EEG-NIRS system can yield better classification accuracy than each of unimodal brain-imaging systems.

Original languageEnglish
Title of host publication2018 6th International Conference on Brain-Computer Interface, BCI 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Electronic)9781538625743
Publication statusPublished - 2018 Mar 9
Event6th International Conference on Brain-Computer Interface, BCI 2018 - GangWon, Korea, Republic of
Duration: 2018 Jan 152018 Jan 17

Publication series

Name2018 6th International Conference on Brain-Computer Interface, BCI 2018


Other6th International Conference on Brain-Computer Interface, BCI 2018
Country/TerritoryKorea, Republic of

Bibliographical note

Funding Information:
ACKNOWLEDGEMENT This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (2017-0-00451).

Publisher Copyright:
© 2018 IEEE.


  • Brain-computer interface
  • EEG
  • Hybrid BCI
  • NIRS
  • Open access dataset

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Behavioral Neuroscience


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