Continuous sensorimotor rhythm based brain computer interface learning in a large population

Abstract

Brain computer interfaces (BCIs) are valuable tools that expand the nature of communication through bypassing traditional neuromuscular pathways. The non-invasive, intuitive, and continuous nature of sensorimotor rhythm (SMR) based BCIs enables individuals to control computers, robotic arms, wheel-chairs, and even drones by decoding motor imagination from electroencephalography (EEG). Large and uniform datasets are needed to design, evaluate, and improve the BCI algorithms. In this work, we release a large and longitudinal dataset collected during a study that examined how individuals learn to control SMR-BCIs. The dataset contains over 600 hours of EEG recordings collected during online and continuous BCI control from 62 healthy adults, (mostly) right hand dominant participants, across (up to) 11 training sessions per participant. The data record consists of 598 recording sessions, and over 250,000 trials of 4 different motor-imagery-based BCI tasks. The current dataset presents one of the largest and most complex SMR-BCI datasets publicly available to date and should be useful for the development of improved algorithms for BCI control.

Challenges and Opportunities for the Future of Brain-Computer Interface in Neurorehabilitation

“…While passive/implicit learning is known to play a role in BCI control (Othmer, 2009), most human participants report developing and fine-tuning mental strategies throughout the course of training, usually involving imagination of movement (Majid et al, 2015;Ruddy et al, 2018;, or in the case of brain injured patients, attempts to make movement with the paretic limb (Blokland et al, 2012;Balasubramanian et al, 2018;Bai et al, 2020). Even for neurologically healthy participants, gaining effective control of an EEG-BCI takes many distinct sessions (Pfurtscheller et al, 2003;Stieger et al, 2021). Without seeing tangible results within the first training sessions, it is likely that patients loose motivation to continue investing effort into trying to control the BCI.…”

Internet of Things Based Emotional Sensor System to Predict the Patient’s Information Using Bioscience Technology

“…Blue Eye technology plays vital role in the field of predict the patient's information system with emotional sensors. Blue Eye Technology are classified into three parts such Sainwar et al, 2020;Stieger et al, 2021).…”

2020 International brain–computer interface competition: A review

“…To solve these problems, high-quality (open) data measured through various experiments are essential. In this regard, many universities and research institutes contribute to the advancement of technology by providing BCI data or holding competitions (Kaya et al, 2018;Shin et al, 2018;Lee M. H. et al, 2019;Jeong et al, 2020a;Stieger J. R. et al, 2021). As such, open datasets are considered an important asset for promoting BCI research and practices.…”