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Updated: Aug 17, 2026

Motor Imagery Performance Through Embodied Digital Twins in a Virtual Reality-Enabled Brain-Computer Interface Environment
Published on: May 10, 2024
Classification of motor imagery tasks for brain-computer interface applications by means of two equivalent dipoles
Baharan Kamousi1, Zhongming Liu, Bin He
1Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA.
Abstract:
We have developed a novel approach using source analysis for classifying motor imagery tasks. Two-equivalent-dipoles analysis was proposed to aid classification of motor imagery tasks for brain-computer interface (BCI) applications. By solving the electroencephalography (EEG) inverse problem of single trial data, it is found that the source analysis approach can aid classification of motor imagination of left- or right-hand movement without training. In four human subjects, an averaged accuracy of classification of 80% was achieved. The present study suggests the merits and feasibility of applying EEG inverse solutions to BCI applications from noninvasive EEG recordings.

