Research Repository

Brain-Machine Interfaces: A Tale of Two Learners

Perdikis, Serafeim and Millan, Jose del R (2020) 'Brain-Machine Interfaces: A Tale of Two Learners.' IEEE Systems Man and Cybernetics Magazine, 6 (3). pp. 12-19. ISSN 2333-942X

SMC-Mag2019_perdikis-millan.pdf - Accepted Version

Download (411kB) | Preview


Brain-machine interface (BMI) technology has rapidly matured over the last two decades, mainly thanks to the introduction of artificial intelligence (AI) methods, in particular, machine-learning algorithms. Yet, the need for subjects to learn to modulate their brain activity is a key component of successful BMI control. Blending machine and subject learning, or mutual learning, is widely acknowledged in the BMI field. Nevertheless, we posit that current research trends are heavily biased toward the machine-learning side of BMI training. In this article, we take a critical view of the relevant literature, and our own previous work, to identify the key issues for more effective mutual-learning schemes in translational BMIs that are specifically tailored to promote subject learning. We identify the main caveats in the literature on subject learning in BMI, in particular, the lack of longitudinal studies involving end users and shortcomings in quantifying subject learning, and pinpoint critical improvements for future experimental designs.

Item Type: Article
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 13 Aug 2020 11:06
Last Modified: 15 Jan 2022 01:34

Actions (login required)

View Item View Item