on004774
Automatic Evoked Response Detection (ER-Detect) dataset
iEEG
[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on004774-blue…
- Participants
- 14
- Size
- 30.2 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
Discover datasets
100 results for "clinical electrophysiology" · page 7 of 10 · ranked by relevance
-
- on004696
HAPwave_bids
iEEGThis dataset comprises intracranial EEG recordings from 8 patients during single-pulse electrical stimulation of the limbic system. The data were collected to investigate electrical stimulation-driven network interactions in human brain circuits, with stimulation parameters of 200 microseconds duration, biphasic waveform, and 6 mA amplitude (4 mA for one subject). The dataset is organized according to BIDS specification and includes raw iEEG data with associated metadata and event timing information.
- Participants
- 8
- Size
- 19.5 GB
- Version
- v1.0.0
- Updated
- Jul 22, 2026
- nm000239
P-ary m-sequence-based c-VEP dataset from Martínez-Cagigal et al. (2023)
EEGThis dataset comprises EEG recordings from 16 healthy participants performing a code-modulated visual evoked potential (c-VEP) brain-computer interface task using p-ary m-sequences. The study evaluates non-binary m-sequence stimulation patterns to enhance user comfort in c-VEP-based BCIs. Data were collected across 5 sessions per subject with 8 runs per session at 256 Hz sampling rate using 16 EEG channels, providing a comprehensive resource for BCI research and algorithm development.
- Participants
- 16
- Citations
- 27
- HED
- v8.4.0
- Size
- 764 MB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
- on007949
Auditory Streaming and Interoception
EEGThis 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.
- Participants
- 22
- Channels
- 1 (other)
- Size
- 7.25 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
- on004837
Magnetoencephalographic (MEG) Pitch and Duration Mismatch Negativity (MMN) in First-Episode Psychosis
ANAT MEG…Psychosis (FEAFF) Further information about clinical, neuropsychological, demographic and medication data can…
- Participants
- 60
- Size
- 120 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
- on006848
AlphaDirection1: EEG, ECG, PPG in the resting state and working memory for sequentially and simultaneously presented digits
BEH EEGThis dataset comprises 64-channel EEG recordings with concurrent ECG and PPG measurements from 30 healthy young adults during resting state and a verbal working memory task. Participants performed digit span recall under four presentation modes (simultaneous, fast sequential, fast sequential with delay, and slow sequential), enabling investigation of neural oscillations and peripheral physiological responses during working memory encoding, maintenance, and retrieval. The dataset includes behavioral accuracy measures and trial-by-trial performance metrics.
- Participants
- 30
- Channels
- 63 (10-10)
- Size
- 41.4 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
- on003474
EEG: Probabilistic Selection and Depression
EEG…Some have been clinically interviewed. For some subjects (maybe all?), HEOG and…
- Participants
- 122
- Channels
- 66 (10-10)
- Size
- 16.6 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
- on005540
EmoEEG-MC: A Multi-Context Emotional EEG Dataset for Cross-Context Emotion Decoding
BEH EEGEmoEEG-MC is a multi-context emotional EEG dataset comprising 64-channel EEG and peripheral physiological recordings from 60 participants exposed to video-induced and imagery-induced emotional stimuli across seven emotion categories (joy, inspiration, tenderness, fear, disgust, sadness, and neutral). This dataset addresses the critical gap in cross-context emotion decoding by enabling investigation of how emotional neural responses generalize across different elicitation contexts, with demonstrated classification accuracies of 66.7% for binary emotion classification and 28.9% for seven-category emotion classification using machine learning approaches.
- Participants
- 60
- Channels
- 64
- Size
- 70.4 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
- nm000347
Shi et al. 2025 — HEFMI-ICH: a hybrid EEG-fNIRS motor imagery dataset for brain-computer interface in intracerebral hemorrhage
EEG…mixed (17 healthy, 20 ICH patients) Clinical population: intracerebral hemorrhage (ICH) Age…
- Participants
- 37
- Channels
- 32 (10-10)
- Citations
- 4
- HED
- v8.4.0
- Size
- 2.58 GB
- Version
- v1.0.1
- Updated
- Jul 10, 2026
- on004789
Delayed Free Recall of Word Lists
BEH iEEG…The data was collected at clinical sites across the country as part…
- Participants
- 280
- Size
- 576 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026