Dataset.
2023
Infant EEG data to assess action prediction skills
CORA.Repositori de Dades de Recerca
doi:10.34810/data708
CORA.Repositori de Dades de Recerca
- Colomer Canyelles, Marc
- Sebastián Gallés, Núria
- Zacharaki, Konstantina
The attached materials include two datasets. First, a dataset with the raw and unprocessed
data. Second, a dataset with the data processed and ready to be used for final analysis.
Raw_datasets: Raw data collected with EEG (Hydrocel 128, Geodesic) from 52 infants while
they were watching an unfamiliar person reaching for an object. The goal of the study was to
explore if infants predicted the actions of the person, indexed by a desynchronization of alpha oscillations measured over sensorimotor electrodes (left central electrodes) during the
anticipatory period. Raw data needs to be processed to remove artifacts, segmented in epochs, and transformed to the time-frequency domain to measure changes in alpha power.
Processed: Processed and artifact-free event-related EEG data transformed to time-frequency.
The MADE pipeline was used to process, clean, and segment the data. The function newtimef from EEGLab was used to transform the data to time-frequency.
No hay resultados en la búsqueda
No hay resultados en la búsqueda
×
1 Versiones
1 Versiones
CORA.Repositori de Dades de Recerca
doi:10.34810/data708
Dataset. 2023
INFANT EEG DATA TO ASSESS ACTION PREDICTION SKILLS
CORA.Repositori de Dades de Recerca
- Colomer Canyelles, Marc
- Sebastián Gallés, Núria
- Zacharaki, Konstantina
The attached materials include two datasets. First, a dataset with the raw and unprocessed
data. Second, a dataset with the data processed and ready to be used for final analysis.
Raw_datasets: Raw data collected with EEG (Hydrocel 128, Geodesic) from 52 infants while
they were watching an unfamiliar person reaching for an object. The goal of the study was to
explore if infants predicted the actions of the person, indexed by a desynchronization of alpha oscillations measured over sensorimotor electrodes (left central electrodes) during the
anticipatory period. Raw data needs to be processed to remove artifacts, segmented in epochs, and transformed to the time-frequency domain to measure changes in alpha power.
Processed: Processed and artifact-free event-related EEG data transformed to time-frequency.
The MADE pipeline was used to process, clean, and segment the data. The function newtimef from EEGLab was used to transform the data to time-frequency.
There are no results for this search