Open Access Dataset for EEG+NIRS Single-Trial Classification

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 25; no. 10; pp. 1735 - 1745
• Main Authors: Shin, Jaeyoung, von Luhmann, Alexander, Blankertz, Benjamin, Kim, Do-Won, Jeong, Jichai, Hwang, Han-Jeong, Muller, Klaus-Robert
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Brain; Brain–computer interface (BCI); Classification; Data processing; Datasets; EEG; Electrocardiography; Electrodes; electroen-cephalography (EEG); Electroencephalography; hybrid BCI; I.R. radiation; Imagery; Infrared spectra; Infrared spectroscopy; Interfaces; Mathematics; mental arithmetic; Mental task performance; Motion measurement; motor imagery; Near infrared radiation; near-infrared spectroscopy (NIRS); Open Access; open access dataset; Signal analysis; Visualization
• ISSN: 1534-4320; 1558-0210
• DOI: 10.1109/TNSRE.2016.2628057

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.