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

Ji Hoon Jeong, Jeong Hyun Cho, Kyung Hwan Shim, Byoung Hee Kwon, Byeong Hoo Lee, Do Yeun Lee, Dae Hyeok Lee, Seong Whan Lee

Research output: Contribution to journalArticlepeer-review

35 Citations (Scopus)


Background: 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, 7-channel electromyography, and 4-channel electro-oculography of 25 healthy participants collected over 3-day sessions for a total of 82,500 trials across all the participants. Findings: We validated our dataset via neurophysiological analysis. We observed clear sensorimotor de-/activation and spatial distribution related to real-movement and motor imagery, respectively. Furthermore, we demonstrated the consistency of the dataset by evaluating the classification performance of each session using a baseline machine learning method. Conclusions: 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.

Original languageEnglish
Pages (from-to)1-15
Number of pages15
Issue number10
Publication statusPublished - 2020

Bibliographical note

Funding Information:
This work was partly supported by Institute of Information and Communications Technology Planning and Evaluation (IITP) grants funded by the Korea government (No. 2015-0-00185, Development of Intelligent Pattern Recognition Softwares for Ambulatory Brain-Computer Interface; No. 2017-0-00451, Development of BCI based Brain and Cognitive Computing Technology for Recognizing User’s Intentions using Deep Learning; No. 2019-0-00079, Department of Artificial Intelligence, Korea University).

Publisher Copyright:
© 2020 Oxford University Press. All rights reserved.


  • brain-computer interface
  • intuitive upper extremity movements
  • multimodal signals
  • multiple sessions

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications


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