Abstract
The present study describes neuromagnetic Geminoid control system by using single-trial decoding of bilateral hand movements as a new approach to enhance a user's ability to interact with a complex environment through a multidimensional brain-computer interface (BCI). Two healthy participants performed or imagined four types of bilateral hand movements during non-invasive magnetic field measurements to control a human-like robot (Geminoid HI-2) in real-time. By applying a nonlinear support vector machine (SVM) method to classify the four movements regarding magnetoencephalography (MEG) sensors obtained from the sensorimotor area, we found the mean accuracy of a 2-class classification using the amplitudes of neuromagnetic fields to be particularly suitable for real time control applications, with accuracies comparable to those obtained in previous studies involving unilateral hand movement. Moreover, our results demonstrated that decoding bilateral movements in real-time is a promising option to design multidimensional-control based BCI applications.
Published Version
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