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

Author:

Jeong Ji-Hoon1ORCID,Cho Jeong-Hyun1,Shim Kyung-Hwan1,Kwon Byoung-Hee1,Lee Byeong-Hoo1,Lee Do-Yeun1,Lee Dae-Hyeok1,Lee Seong-Whan12ORCID

Affiliation:

1. Department of Brain and Cognitive Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, South Korea

2. Department of Artificial Intelligence, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, South Korea

Abstract

Abstract 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.

Funder

Institute for Information and Communications Technology Promotion

Publisher

Oxford University Press (OUP)

Subject

Computer Science Applications,Health Informatics

Reference67 articles.

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3. DTP-Net: Learning to Reconstruct EEG Signals in Time-Frequency Domain by Multi-Scale Feature Reuse;IEEE Journal of Biomedical and Health Informatics;2024-05

4. Sparse Multitask Learning for Efficient Neural Representation of Motor Imagery and Execution;2024 12th International Winter Conference on Brain-Computer Interface (BCI);2024-02-26

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