Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

In Advanced science (Weinheim, Baden-Wurttemberg, Germany)

Motor imagery offers an excellent opportunity as a stimulus-free paradigm for brain-machine interfaces. Conventional electroencephalography (EEG) for motor imagery requires a hair cap with multiple wired electrodes and messy gels, causing motion artifacts. Here, a wireless scalp electronic system with virtual reality for real-time, continuous classification of motor imagery brain signals is introduced. This low-profile, portable system integrates imperceptible microneedle electrodes and soft wireless circuits. Virtual reality addresses subject variance in detectable EEG response to motor imagery by providing clear, consistent visuals and instant biofeedback. The wearable soft system offers advantageous contact surface area and reduced electrode impedance density, resulting in significantly enhanced EEG signals and classification accuracy. The combination with convolutional neural network-machine learning provides a real-time, continuous motor imagery-based brain-machine interface. With four human subjects, the scalp electronic system offers a high classification accuracy (93.22 ± 1.33% for four classes), allowing wireless, real-time control of a virtual reality game.

Mahmood Musa, Kwon Shinjae, Kim Hojoong, Kim Yun-Soung, Siriaraya Panote, Choi Jeongmoon, Otkhmezuri Boris, Kang Kyowon, Yu Ki Jun, Jang Young C, Ang Chee Siang, Yeo Woon-Hong


brain-machine interfaces, motor imagery brain signals, virtual reality system, wireless soft scalp electronics