Supporting data for "Multimodal signal dataset for 11 intuitive movement tasks from single upper extremity during multiple recording sessions"

Dataset type: Electrophysiology, Software
Data released on September 10, 2020

Jeong J; Cho J; Shim K; Kwon B; Lee B; Lee D; Lee D; Lee S (2020): Supporting data for "Multimodal signal dataset for 11 intuitive movement tasks from single upper extremity during multiple recording sessions" GigaScience Database. http://dx.doi.org/10.5524/100788

DOI10.5524/100788

Non-invasive brain-computer interfaces (BCIs) have been developed for realizing natural bi-directional interaction between users and external robotic systems. However, the communication between users and BCI systems through artificial matching is a critical issue. Recently, BCIs have been developed to adopt intuitive decoding which is the key to solving several problems such as a small number of classes and manually matching BCI commands with device control. Unfortunately, the advances in this area have been slow owing to the lack of large and uniform datasets. This study provides a large intuitive dataset for 11 different upper-extremity movement tasks obtained during multiple recording sessions. The dataset includes 60-channel electroencephalography (EEG), 7-channel electromyography (EMG), and 4-channel electrooculography (EOG) of 25 healthy subjects collected over 3-day sessions for a total of 82,500 trials across all the subjects. We validated our dataset via neuro physiological analysis. We observed clear sensorimotor de-/activation and spatial distribution related to real-movement and motor imagery (MI), respectively. Furthermore, we demonstrated the consistency of the dataset by evaluating the classification performance of each session using a baseline machine learning method. The dataset includes the data of multiple recording sessions, various classes within the single upper-extremity, and multimodal signals. This work can be used to i) compare the brain activities associated with real-movement and imagination, ii) improve the decoding performance, and iii) analyze the differences among recording sessions. Hence, this study, as a data note, has focused on collecting data required for further advances in the BCI technology.

Additional details

Read the peer-reviewed publication(s):

(PubMed: 33034634)





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Displaying 1-10 of 25 Sample(s).




File NameSample IDData TypeFile FormatSizeRelease Date 
Tabular dataCSV2.93 KB2020-09-01
EEGGZIP226.32 GB2020-09-01
OtherGZIP19.51 GB2020-09-01
OtherGZIP14.02 GB2020-09-01
Tabular datazip72.1 MB2020-09-01
FigureTIF748.88 KB2020-09-01
Tabular datazip1.29 MB2020-09-01
FigureTIF172.4 KB2020-09-01
Tabular datazip328.65 MB2020-09-01
FigureTIF331.17 KB2020-09-01
Displaying 1-10 of 19 File(s).
Funding body Awardee Award ID Comments
Institute of Information & Communications Technology Planning & Evaluation (IITP) S Lee 2015-0-00185 Development of Intelligent Pattern Recognition Softwares for Ambulatory Brain-Computer Interface
Institute of Information & Communications Technology Planning & Evaluation (IITP) S Lee 2019-0-00079 Department of Artificial Intelligence Korea University
Institute of Information & Communications Technology Planning & Evaluation (IITP) S Lee 2017-0-00451 Development of BCI based Brain and Cognitive Computing Technology for Recognizing User’s Intentions using Deep Learning
Date Action
September 10, 2020 Dataset publish
September 10, 2020 File readme_100788.txt updated
September 10, 2020 readme_100788.txt: additional file attribute added
September 10, 2020 File readme_100788.txt updated
September 14, 2020 Manuscript Link added : 10.1093/gigascience/giaa098
October 7, 2022 Manuscript Link updated : 10.1093/gigascience/giaa098