EEG Imitation tasks

The dataset used for classification consists of EEG data recorded during imitation learning tasks. The primary features and structure of the dataset are as follows:

Dataset Description
Participants:

Number of participants: 83.
Gender distribution: [e.g., 17 male, 66 female].
Age range: [e.g., 22–40 years].
All participants were right-handed and had normal or corrected-to-normal vision.

Tasks:

The dataset includes EEG recordings from three tasks:
Observation Task: Participants observed gestures performed by a demonstrator (live or via video).
Execution Task: Participants performed gestures without observing the demonstrator.
Simultaneous Observation and Execution Task: Participants simultaneously observed and executed gestures.

Data Acquisition:

EEG was recorded using [31] electrodes placed according to the [standard, e.g., 10–20 system].
Sampling rate: [e.g., 500 Hz].
Bandpass filtering: 1–50 Hz.

Features:

Raw EEG Signals: Data from all electrodes during task performance.
Extracted for each electrode using time-frequency decomposition techniques (fast Fourier transform):

Absolute Power in Alpha Band (8-13 Hz): list 1
Absolute Power in Beta Band (13–30 Hz): list 2
Relative Power in Alpha Band (8-13 Hz): list 3
Relative Power in Beta Band (13–30 Hz): list 4

Task Labels:
Observation task (live or video demonstration) - obser
Execution task (no observation) - execut
Simultaneous observation and execution task. - simult

9Demonstration Modality Labels:
Live or video format of gesture demonstration - scenario: _live or _video
Demonstrator Gender Labels - scenario: male or female

Preprocessing:

Artifacts (e.g., eye blinks, muscle movements) were removed using Independent Component Analysis (ICA).
EEG signals were segmented into epochs corresponding to task events (e.g., gesture onset).
Fast Fourier transform to get absolute and relative alpha and beta power in each trial.

These labels were used as target variables for supervised machine learning models.
This dataset forms the basis for training and testing machine learning algorithms to classify social interaction formats and identify the neural correlates associated with each classification task.