Toward robust 2D control using a 4-class brain-computer interface based on motor imagination

Authors: Zanchi Luca; Tortora Stefano; Menegatti Emanuele; Tonin Luca

Journal: 2024 IEEE 20TH INTERNATIONAL CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING, CASE 2024

Conference: 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE)

Publisher: IEEE Computer Society

Published: 2024

DOI: 10.1109/CASE59546.2024.10711431

Pages: 1602-1607

Research Topics: Brain–computer interface; Class (philosophy); Computer science; Control (management); Interface (matter)

Citations: 0 (source: OpenAlex)

Abstract

Brain-computer interfaces (BCIs) represent an alternative channel of communication between the user and the external environment, circumventing the need for traditional neural pathways. The capability to modulate one's own electroencephalogram (electroencephalography (EEG)) signal holds the potential to facilitate specific movements in external devices, thereby restoring or enhancing certain abilities that may have been compromised. In this study, we propose two potential configurations for a four-class, closed loop, real-time Motor Imagery (MI) brain-computer interface (BCI), with the objective of assessing the viability of these endogenous BCIs in accurately directing a cursor on the screen. Twelve healthy participants and one individual with motor disability participated in the experiment, with nine of them successfully transitioning from one-dimensional to two-dimensional cursor control. This outcome suggests that proficient control is achievable with sufficient training time.