Neuromagnetic Geminoid Control by BCI based on Four Bilateral Hand Movements

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.