Abstract:

The emerging wearable magnetoencephalography technology lays the foundation for brain-computer interface to provide high-quality data. In order to explore the feasibility of applying wearable magnetoencephalography to visual hybrid brain-computer interface, a SSVEF-Alpha hybrid brain-computer interface is designed based on steady-state visual evoked field and Alpha wave, and the performance is compared with different classification models. The results show that based on the user-dependence training method, the average classification accuracy of hybrid brain-computer interface 6 is (93.29±1.69)%, and the information transmission rate can reach 86.81 bits/min, and the user-independence training method with short data length is superior to the training-free method. This study verifies the effectiveness of visual hybrid brain-computer interface and provides a reference example for further development and design of brain-computer interface products of wearable magnetoencephalography.

Key words: brain-computer interface, wearable magnetoencephalogram, steady-state visual evoked field, Alpha wave, classification accuracy, information transmission rate