Showing 10 of 581 datasets · page 15 of 59
This dataset contains EEG recordings collected as part of a delayed repetition free recall (DelayRepFR) memory experiment, converted to BIDS format for reuse and analysis. It is a read-only derivative distribution intended for the OpenNeuro and NEMAR platforms, part of a broader effort by Michael Kahana's lab studying human memory processes.
NOD-EEG provides electroencephalography (EEG) recordings collected from the same subjects and using the same naturalistic ImageNet stimuli as the previously published NOD-fMRI dataset, extending the Natural Object Dataset (NOD) to include temporal dynamics of visual object recognition. Combined with companion MEG data, this multimodal resource enables investigation of neural mechanisms underlying object recognition across naturalistic scenes with both high spatial and high temporal resolution. The dataset includes raw and preprocessed EEG time series and epoched data, along with detailed trial-level event metadata.
NOD-MEG provides magnetoencephalography (MEG) recordings from human participants viewing large-scale naturalistic ImageNet stimuli, extending the previously collected Natural Object Dataset (NOD-fMRI) with temporally resolved neural data. Combined with corresponding fMRI and EEG datasets from the same subjects, NOD-MEG enables multimodal investigation of object recognition across both spatial and temporal domains. The dataset is intended as a resource for studying neural mechanisms of visual object recognition under naturalistic viewing conditions.
This dataset presents multimodal simultaneous fMRI/EEG data collected from participants performing an auditory category learning task, alongside resting-state fMRI scans. Each dataset includes 63-channel EEG with additional ECG, EOG, facial EMG, and skin conductance recordings, a physiological file capturing respiration and finger-pulse signals, and structural MRI (T1-weighted and PD-weighted UTE) scans for anatomical reference and electrode localization. Collected at the Combinatorial NeuroImaging core facility of the Leibniz Institute for Neurobiology, the data support research into multi-dimensional auditory category learning and its neural correlates.
This dataset contains non-invasive electrophysiological recordings from the human spinal cord in 30 healthy young adults during a temporal expectation paradigm. Subjects received electric median nerve stimulation preceded by an auditory cue with either fixed or variable timing, allowing investigation of whether temporal expectation modulates initial sensory processing at the spinal level. Recordings used a specialized multi-ring electrode montage around the neck to capture spinal cord signals.
This dataset contains invasive electrophysiological recordings from the human cervical spinal cord in patients with neuropathic pain who had epidural electrodes implanted for pain management. The study investigates whether temporal expectation, manipulated via variable versus fixed cue-to-stimulus intervals, influences initial sensory processing of median nerve stimulation at the level of the spinal cord. Eight patients performed a counting task while receiving auditory cues followed by single or double electric median nerve stimuli.
This dataset contains electrocorticography (ECoG) recordings from three cohorts of C57BL/6 mice examining how transcranially delivered diagnostic ultrasound (tDUS) modulates cortical responses to a blinking light stimulus. Mice were exposed to combinations of tDUS and light stimulation, with electrodes targeting visual and somatosensory cortices, to investigate ultrasound's potential to intentionally modulate brain activity for future clinical and therapeutic applications.
This dataset contains EEG recordings from an experiment in which participants alternately perceived and imagined visual images based on auditory prompts. Each trial consisted of a 6-second perception phase, during which participants viewed an image, followed by a 6-second imagination phase with eyes closed. The dataset is intended to support research on EEG-based analysis of perception and mental imagery processes, such as deep learning approaches to EEG decoding.
This dataset comprises stereoelectroencephalography (SEEG) resting state recordings acquired from two patients with epilepsy. The intracranial recordings were obtained using depth electrodes implanted for clinical evaluation purposes, providing insight into resting-state neural activity in epileptic patients.
This dataset comprises raw EEG recordings collected to investigate the temporal dynamics underlying object space dimensions in human perception. Participants completed a rapid image presentation task while EEG data were recorded, enabling analysis of the time course of object representation. The dataset includes behavioral event data alongside BIDS-formatted EEG recordings, supporting research into visual object processing and neural representational geometry.