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

doi:10.1109/TNSRE.2016.2628057

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 journal › Article › peer-review

126 Citations (Scopus)

Abstract

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 language	English
Article number	7742400
Pages (from-to)	1735-1745
Number of pages	11
Journal	IEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume	25
Issue number	10
DOIs	https://doi.org/10.1109/TNSRE.2016.2628057
Publication status	Published - 2017 Oct

Keywords

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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10.1109/TNSRE.2016.2628057

Cite this

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title = "Open Access Dataset for EEG+NIRS Single-Trial Classification",

abstract = "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.",

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author = "Jaeyoung Shin and {Von Luhmann}, Alexander and Benjamin Blankertz and Kim, {Do Won} and Jichai Jeong and Hwang, {Han Jeong} and Muller, {Klaus Robert}",

note = "Funding Information: Manuscript received March 16, 2016; revised August 26, 2016; accepted October 27, 2016. Date of publication November 11, 2016; date of current version October 23, 2017. This research was supported in part by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2014R1A6A3A03057524) and Ministry of Science, ICT & Future Planning (NRF-2015R1C1A1A02037032), and supported the Brain Korea 21 PLUS Program through the NRF funded by the Ministry of Education, the Korea University Grant and BMBF (#01GQ0850, Bernstein Focus: Neurotechnology). (Corresponding authors: Han-Jeong Hwang and Klaus-Robert M{\"u}ller.) J. Shin and A. von L{\"u}hmann are with the Machine Learning Group, Department of Computer Science, Berlin Institute of Technology, 10587 Berlin, Germany. Publisher Copyright: {\textcopyright} 2001-2011 IEEE.",

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language = "English",

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AU - Von Luhmann, Alexander

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AU - Kim, Do Won

AU - Jeong, Jichai

AU - Hwang, Han Jeong

AU - Muller, Klaus Robert

N1 - Funding Information: Manuscript received March 16, 2016; revised August 26, 2016; accepted October 27, 2016. Date of publication November 11, 2016; date of current version October 23, 2017. This research was supported in part by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2014R1A6A3A03057524) and Ministry of Science, ICT & Future Planning (NRF-2015R1C1A1A02037032), and supported the Brain Korea 21 PLUS Program through the NRF funded by the Ministry of Education, the Korea University Grant and BMBF (#01GQ0850, Bernstein Focus: Neurotechnology). (Corresponding authors: Han-Jeong Hwang and Klaus-Robert Müller.) J. Shin and A. von Lühmann are with the Machine Learning Group, Department of Computer Science, Berlin Institute of Technology, 10587 Berlin, Germany. Publisher Copyright: © 2001-2011 IEEE.

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Open Access Dataset for EEG+NIRS Single-Trial Classification

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