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

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 electro

SUBMITTER: Jeong JH 

PROVIDER: S-EPMC7539536 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature