…health through collecting and analyzing neuroimaging, behavioral, and genetic data (Alexander et…
A multi-day EEG dataset for motor imagery brain-computer interface (MI-BCI) research collected at Shanghai University (WBCIC-SHU). It contains recordings from 51 healthy, BCI-naive participants who performed left- and right-hand motor imagery over three sessions on different days. A subset of 11 participants also performed a three-class paradigm that adds foot-hooking imagery. Recordings were made with a 59-channel EEG system and the dataset is accompanied by benchmark classification results using CSP, FBCSP, EEGNet, deepConvNet and FBCNet.
…health through collecting and analyzing neuroimaging, behavioral, and genetic data (Alexander et…
A motor imagery EEG dataset from 54 healthy participants performing binary left/right hand grasping imagery across two sessions on different days. Recordings were made with 62 EEG channels plus 4 EMG channels at 1000 Hz. The data belong to a larger multi-paradigm BCI collection (MI, ERP, SSVEP) used to investigate BCI illiteracy and variation in decoding performance across subjects and sessions.
…health through collecting and analyzing neuroimaging, behavioral, and genetic data (Alexander et…
Open-access EEG dataset from Experiment B of Shin et al. (2017), recorded alongside NIRS for hybrid brain-computer interface research. 29 healthy participants performed cued mental arithmetic (serial subtraction) versus resting-baseline trials across alternating sessions. This derivative release contains preprocessed 30-channel EEG (CAR, 0.5-50 Hz bandpass, ICA-based EOG rejection, 200 Hz) with HED event annotations, formatted for benchmarking single-trial classification in MOABB.
…health through collecting and analyzing neuroimaging, behavioral, and genetic data (Alexander et…
This dataset contains 64-channel EEG recordings and behavioural ratings from four participants who read and evaluated 210 short English-language texts: Haiku, Senryu, and non-poetic control texts. Each text was rated on aesthetic appeal, vivid imagery, being moved, originality, and creativity. These participants' recordings were acquired in segments because of session interruptions and were concatenated during preprocessing. They are released as a companion to the main Poetry Assessment EEG Dataset 1 for completeness and transparency.
This dataset provides high-density (128-channel) EEG and mouse-tracking data from 31 adults aged 18-33, collected in 2020. It includes resting-state recordings and task recordings from food preference choices and semantic judgments. The resource is intended for studying the dynamic decision processes underlying binary choices in the human brain.
…health through collecting and analyzing neuroimaging, behavioral, and genetic data (Alexander et…