95 results for "somatosensory cortex" · page 8 of 10 · ranked by relevance
…in the hippocampus and entorhinal cortex. Stimulation parameters are included in the…
…On the motor cortex stimulation amplitude was sometimes reduced to 1 or…
This dataset comprises simultaneous EEG and fMRI recordings from 10 subjects performing motor imagery and neurofeedback tasks. Participants completed six runs including motor localization, pre- and post-neurofeedback motor imagery, and three neurofeedback conditions (bimodal EEG-fMRI, unimodal EEG, and unimodal fMRI). The dataset provides both raw and preprocessed EEG data (64 channels at 5 kHz), structural and functional MRI data (3T Siemens, 2×2×4 mm³ resolution), and computed neurofeedback scores, enabling multi-modal neuroimaging data integration studies.
[ recordings from human patients, rodents, pigs, and non-human primates acquired using novel flexible, scalable, high-resolution micro-stereo-electrodes. The study evaluates the performance of these custom-fabricated depth electrodes for neural recording and includes direct electrical stimulation experiments to assess stimulation effects on brain activity. Raw and preprocessed epoched data are provided in iEEG-BIDS format with accompanying channel maps and derivatives.
[ dataset is a human electroencephalography resource comprising high-resolution EEG recordings from 20 participants performing multisensory perception and mental imagery tasks. Signals were acquired at 1000 Hz sampling rate during exposure to unimodal (visual and auditory) and multimodal stimuli, with participants providing subjective vividness ratings. Technical validation through event-related potentials and power spectral density analyses confirmed distinct neural responses across stimulus conditions, supporting applications in neural decoding, perception, and cognitive modeling.
…channels positioned around the motor cortex (standard 1005 montage). Online bandpass filtering…
This dataset contains magnetoencephalography (MEG) recordings from a Neuromag phantom used to characterize device-specific artifacts generated by deep brain stimulation (DBS) systems. The study investigates how DBS-related electromagnetic interference affects MEG signal quality and measurement reliability, providing essential reference data for researchers working with concurrent DBS-MEG recordings in clinical and research settings.