BNCI 2014-001 Motor Imagery dataset
- Citations
- 89
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- 672 MB
- Updated
- May 16, 2026
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This dataset combines high-density electroencephalography (EEG) with concurrent physiological recordings (ECG, EOG) and continuous behavioral metrics during transcranial electrical stimulation (tES). The study includes 19 participants performing a vigilance task across 62 sessions, with systematic application of nine HD-tES montages targeting three cortical regions (frontal, motor, parietal) using three waveforms (DC, 5 Hz, 30 Hz), yielding over 783 stimulation trials. The dataset supports investigation of tES effects on brain activity, physiology, fatigue, and cognitive performance.
This dataset comprises simultaneous EEG and fNIRS recordings from 12 participants performing semantic imagery tasks involving silent naming and sensory-based imagination of animals and tools. Participants engaged in visual, auditory, and tactile perception tasks while neural activity was captured using a 64-channel BioSemi EEG system and a NIRx fNIRS imaging system with integrated optodes. The multimodal neuroimaging data supports research in semantic decoding and brain-computer interface applications.
Imported from OpenNeuro ds005516
This dataset comprises electroencephalography (EEG) recordings collected to investigate the relationship between auditory streaming—the perceptual organization of sound sequences—and interoceptive awareness. The study examines how the brain processes complex auditory stimuli and integrates this information with internal bodily signals, contributing to our understanding of sensory integration and conscious perception.
This dataset comprises electroencephalographic recordings from 21 healthy subjects performing a visual P300 experiment in two conditions: personal computer and virtual reality environments. The study compares P300-based brain-computer interface performance across these modalities using a 6×6 matrix speller paradigm with 16-channel EEG acquisition at 512 Hz. The dataset includes 12 experimental blocks per session with randomized session order and stimulus presentation to evaluate physiological responses, subjective experience, and BCI performance differences between PC and VR presentation.
This dataset comprises EEG recordings from 15 individuals with autism spectrum disorder (ASD) participating in a P300-based brain-computer interface (BCI) study focused on joint-attention training in a virtual environment. Data were collected across 7 sessions using 8 EEG channels sampled at 250 Hz, with participants responding to visual stimuli (flashing objects) in a two-class paradigm (target vs. non-target). The dataset includes preprocessed, epoched data suitable for benchmarking BCI classification algorithms and investigating neural correlates of attention in ASD populations.
Imported from OpenNeuro ds005515