Dual-layer electroencephalography data during real-world table tennis

被引:1
|
作者
Studnicki, Amanda [1 ]
Ferris, Daniel P. [1 ]
机构
[1] Univ Florida, J Crayton Pruitt Family Dept Biomed Engn, Gainesville, FL 32611 USA
来源
DATA IN BRIEF | 2024年 / 52卷
基金
美国国家科学基金会;
关键词
EEG; Independent component analysis; Sports neuroscience; Electrocortical dynamics; Mobile Brain Body Imaging (MoBI); EEG; ARTIFACTS;
D O I
10.1016/j.dib.2023.110024
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Real -world settings are necessary to improve the ecological validity of neuroscience research, and electroencephalography (EEG) facilitates mobile electrocortical recordings because of its easy portability and high temporal resolution. Table tennis is a whole -body, goal -directed sport that requires constant visuomotor feedback, anticipation, strategic decision -making, object interception, and performance monitoring - making it an interesting testbed for a variety of neuroscience studies. Although traditionally plagued by artifact contamination, recent advances in signal processing and hardware approaches, such as the dual -layer approach, have allowed high fidelity EEG recordings during whole -body maneuvers. Here, we present a dual -layer EEG dataset from 25 healthy human participants playing table tennis with a human opponent and a ball machine. Our dataset includes synchronized, multivariate time series recordings from 120 scalp electrodes, 120 noise electrodes, 8 neck electromyography electrodes, and inertial measurement units on the participant, paddles, and ball machine to record hit events. We also include de -identified T1 anatomical MR images and digitized electrode locations to create subject -specific head models for source localization. In addition, we provide anonymized video recordings and Adobe Premiere project files with hit events labeled (originally used to mark successful/missed hits). Researchers could use the videos to mark their own events of interest. We formatted our dataset in the Brain Imaging Data Structure (BIDS) format to facilitate data reuse and to adhere to the scientific community's new organization standard. (c) 2024 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY -NC -ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )
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页数:10
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