on006104
EEG dataset for speech decoding
EEG
…contains EEG recordings from a phoneme discrimination task with TMS. The data…
- Participants
- 24
- Channels
- 61 (10-10)
- Citations
- 6
- Size
- 83.8 GB
- Version
- v1.0.0
- Updated
- Aug 19, 2026
Discover datasets
32 results for "phoneme discrimination" · page 1 of 4 · ranked by relevance
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- on007322
Personalized smartphone notifications bias auditory salience across processing stages
EEGThis dataset contains EEG recordings from an auditory oddball paradigm designed to investigate how personalized smartphone notification sounds bias auditory salience processing at different neural processing stages. The study examines attentional and perceptual effects of self-relevant notification stimuli compared to standard oddball tones. This resource enables analysis of event-related potentials associated with salience detection and personalized auditory cues.
- Participants
- 57
- Channels
- 64 (10-10)
- Size
- 42.5 GB
- Version
- v1.0.0
- Updated
- Aug 18, 2026
- on004408
EEG responses to continuous naturalistic speech
EEG…Low-frequency cortical entrainment to speech reflects phoneme-level processing. Current Biology…
- Participants
- 19
- Channels
- 128 (biosemi)
- Citations
- 90
- Size
- 18.7 GB
- Version
- v1.0.0
- Updated
- Aug 19, 2026
- on007052
PURSUE N400 Word Processing
EEGThis dataset contains EEG data from the Word Processing task of the PURSUE project, collected to elicit the N400 event-related potential (ERP) component associated with semantic word processing. Data were collected from participants at three primarily undergraduate institutions in Southern California, Massachusetts, and Virginia during 2017 and 2018. The task design follows the ERP CORE paradigm developed by Kappenman et al. (2021).
- Participants
- 288
- Channels
- 32 (10-10)
- Size
- 8.98 GB
- Version
- v1.0.0
- Updated
- Aug 18, 2026
- on007870
Appleseed Audiobook MEG
MEG…on sublexical phoneme sequences, and one based on the phonemes in the…
- Participants
- 13
- Size
- 41.6 GB
- Version
- v1.0.0
- Updated
- Sep 8, 2026
- on002712
Numbers and Letters
ANAT BEH MEGThis dataset contains neuroimaging data from an experiment investigating the neural processing of numbers, letters, and false fonts presented as single items or strings. The study employs event-related experimental design with two runs, each utilizing distinct stimulus categories marked by specific trigger codes to enable precise temporal analysis of brain responses to different visual stimuli. Data were acquired using magnetoencephalography (MEG).
- Participants
- 25
- Size
- 310 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
- nm000229
Gwilliams et al. 2023 — Introducing MEG-MASC: a high-quality magneto-encephalography dataset for evaluating natural speech processing
ANAT MEG…We time-stamp the onset and offset of each word and phoneme…
- Participants
- 27
- Citations
- 46
- Size
- 99.3 GB
- Version
- v1.0.1
- Updated
- Jul 10, 2026
- on005415
Numbers
ANAT BEH iEEGThis dataset comprises stereoelectroencephalography (sEEG) recordings from epilepsy patients undergoing monitoring for seizure activity at Oregon Health & Science University. During monitoring, patients were presented with auditory and visual numerical stimuli that were either symbolic (Arabic numerals and spoken numbers) or non-symbolic (dot arrays and beeps), enabling investigation of numerical cognition and its neural representations in intracranial recordings.
- Participants
- 13
- Size
- 13.5 GB
- Version
- v1.0.0
- Updated
- Aug 19, 2026
- on004229
amnoise
MEG…the Sensitive Period of Native Phoneme Learning. International Journal of Environmental Research…
- Participants
- 2
- Citations
- 22
- Size
- 1.78 GB
- Version
- v1.0.0
- Updated
- Aug 19, 2026
- nm000259
Speier et al. 2017 — A comparison of stimulus types in online classification of the P300 speller using language models
EEGThis dataset contains EEG recordings from 10 healthy adults performing a P300 speller task using a 6x6 character matrix, under two stimulus conditions (Famous Faces overlay and Inverting). Data were collected across two sessions per subject with three runs each, using a 32-channel g.tec EEG system at 256 Hz. The dataset is a BIDS-formatted derivative generated via MOABB from the original data reported in Speier et al. (2017), which compared classification approaches for the P300 speller using language models.
- Participants
- 10
- Channels
- 32 (10-10)
- Citations
- 28
- HED
- v8.4.0
- Size
- 773 MB
- Version
- v1.0.4
- Updated
- Aug 20, 2026