Open Access Dataset for EEG+NIRS Single-Trial Classification

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

Research output: Contribution to journalArticlepeer-review

126 Citations (Scopus)


We provide an open access dataset for hybrid brain-computer interfaces (BCIs) using electroencephalography (EEG) and near-infrared spectroscopy (NIRS). For this, we conducted two BCI experiments (left versus right hand motor imagery; mental arithmetic versus resting state). The dataset was validated using baseline signal analysis methods, with which classification performance was evaluated for each modality and a combination of both modalities. As already shown in previous literature, the capability of discriminating different mental states can be enhanced by using a hybrid approach, when comparing to single modality analyses. This makes the provided data highly suitable for hybrid BCI investigations. Since our open access dataset also comprises motion artifacts and physiological data, we expect that it can be used in a wide range of future validation approaches in multimodal BCI research.

Original languageEnglish
Article number7742400
Pages (from-to)1735-1745
Number of pages11
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Issue number10
Publication statusPublished - 2017 Oct


  • Brain-computer interface (BCI)
  • electroen-cephalography (EEG)
  • hybrid BCI
  • mental arithmetic
  • motor imagery
  • near-infrared spectroscopy (NIRS)
  • open access dataset

ASJC Scopus subject areas

  • Internal Medicine
  • Neuroscience(all)
  • Biomedical Engineering
  • Rehabilitation


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