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 vs. right hand motor imagery; mental arithmetic vs. 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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