100 results for "multimodal neurophysiology" · page 10 of 10 · ranked by relevance
[ for motor rehabilitation applications.
This dataset contains simultaneous EEG, electrospinography (ESG), electroneurography (ENG), and electromyography (EMG) recordings from 26 healthy participants during resting state and various peripheral nerve stimulation paradigms (mixed and sensory nerve stimulation of median, tibial, and digital/toe nerves). The recordings aim to characterize somatosensory evoked potentials at the level of the spinal cord and cortex, enabling investigation of somatosensory processing across peripheral, spinal, and cortical levels. The study was pre-registered and organized according to the BIDS specification.
…the International Federation of Clinical Neurophysiology. The SD device was used to…
This dataset contains EEG recordings from elderly patients with dementia undergoing 40Hz auditory gamma-band entrainment sessions. The study investigates whether auditory stimulation modulated at 40Hz can entrain brain oscillations and enhance functional connectivity, particularly within the default mode network, as a potential intervention for Alzheimer's disease symptoms. Both short and long entrainment sessions were conducted to accommodate participant tolerance, with EEG data collected using a 19-channel 10/20 system setup.
[, 901-908. https://doi.org/10.1016/j…
This dataset (WBCIC-SHU) contains multi-day EEG recordings from 51 healthy, right-handed, BCI-naive subjects performing a motor imagery brain-computer interface paradigm across three sessions per subject. Participants imagined left-hand, right-hand, or (for a subset of 11 subjects) foot movements in response to visual and auditory cues, yielding 39,600 trials in total. The dataset is intended for benchmarking motor imagery classification algorithms such as CSP, FBCSP, EEGNet, deepConvNet, and FBCNet, and was converted to BIDS format using MOABB.
Chisco-2.0 is an imagined speech EEG dataset comprising raw EDF recordings from two participants (sub-01 and sub-02). It is intended to support research into decoding imagined speech from electroencephalographic signals, providing a resource for brain-computer interface and speech neuroscience studies.