Neural Tracking to go
Imported from OpenNeuro ds003801
- Participants
- 20
- Channels
- 24 (10-10)
- Size
- 1.15 GB
- Version
- v1.0.0
- Updated
- Jul 10, 2026
100 results for "electroencephalography analysis" · page 7 of 10 · ranked by relevance
Imported from OpenNeuro ds003801
…Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal…
…resolution of magnetoencephalography (MEG) and electroencephalography (EEG). HAD-MEEG were recorded in…
…Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal…
…Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal…
This dataset comprises EEG recordings from 24 participants across two related studies (2019 and 2021) investigating phoneme discrimination during concurrent transcranial magnetic stimulation (TMS) of motor and speech-related cortical regions. Participants listened to speech sounds—including single phonemes, phoneme pairs, and phoneme triplets (real and pseudowords)—and responded via button press, enabling exploration of articulation and coarticulation effects on neural speech decoding. The dataset supports research into cortical mechanisms underlying speech perception and motor cortex involvement in phoneme processing.
This dataset contains resting-state EEG recordings from 140 juvenile participants in Colombia, including 74 juvenile offenders and 66 non-offender controls, collected to study neurocognitive patterns associated with delinquency. Recordings were acquired using a 128-channel Biosemi ActiveTwo system during alternating eyes-closed/eyes-open resting-state paradigms. The dataset includes both preprocessed EEG data and extracted spectral power features (mean power, RMS, standard deviation, min/max power, skewness, kurtosis) across delta, theta, alpha, and beta frequency bands for each channel and epoch.
…Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal…
This dataset comprises event-related potential (ERP) recordings from 13 healthy subjects performing a visual matrix speller task using a calibrationless brain-computer interface approach. The study introduces learning from label proportions (LLP), an unsupervised classification method that exploits known target/non-target stimulus ratios to enable online BCI operation without prior calibration. Subjects performed copy-spelling tasks using a 6×7 character grid across three sessions, achieving 84.5% character accuracy without labeled training data.