Work

Dataset for Multiple Basketball Motor Observation and Motor Imagery Based on Functional Connectivity Analysis

Year: 2026

Type: data-paper

Abstract: This study presents an electroencephalography (EEG) dataset collected during motor imagery (MI) and motor observation (MO) of basketball-specific actions, designed to support functional connectivity research and facilitate brain-computer interface (BCI) development. Thirty-five healthy right-handed participants completed three experimental protocols: single-hand dribbling, bilateral ball tapping (throwing the ball from one hand to the other), and two-person passing. The three protocols included different types of MI and MO tasks, yielding 1,560 trials per participant with a total recording duration of approximately 70 min. The dataset provides raw recordings, preprocessed data, and functional connectivity matrices based on phase-locking values (PLV) across multiple frequency bands and time windows. Additional metadata included demographic information and scores from the KVIQ-20, which assesses motor-imagery ability. This dataset enables investigations into the neural mechanisms underlying MI and MO in ecologically valid sports contexts and the evaluation of functional connectivity metrics and may even offer opportunities for the development of functional connectivity-based MI-BCI applications.

Source: Scientific Data

DOI: 10.1038/s41597-026-08165-4

Authors: Jiaqi Wang, Ming Tang, Lingrong Jia, Jinxiang Zhang, Cheng Cheng, Jiahui Han

Institution: Liaoning Normal University

Language: English

Topic: EEG and Brain-Computer Interfaces

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,490