PROJECT TITLE :

A Dynamically Optimized SSVEP Brain–Computer Interface (BCI) Speller

ABSTRACT:

The aim of this study was to design a dynamically optimized steady-state visually evoked potential (SSVEP) brain–computer interface (BCI) system with enhanced performance relative to previous SSVEP BCIs in terms of the amount of things selectable on the interface, accuracy, and speed. In this approach, the row/column (RC) paradigm was used in a very SSVEP speller to extend the amount of things. The target is detected by subsequently determining the row and column coordinates. To improve spelling accuracy, we have a tendency to added a posterior processing after the canonical correlation analysis (CCA) approach to scale back the interfrequency variation between totally different subjects and named the new Signal Processing methodology CCA-RV, and designed a true-time biofeedback mechanism to extend attention on the visual stimuli. To realize reasonable online spelling speed, both mounted and dynamic approaches for setting the optimal stimulus duration were implemented and compared. Experimental results for 11 subjects recommend that the CCA-RV technique and the $64000-time biofeedback effectively increased accuracy compared with CCA and the absence of real-time feedback, respectively. In addition, each optimization approaches for setting stimulus duration achieved reasonable on-line spelling performance. But, the dynamic optimization approach yielded a higher practical information transfer rate (PITR) than the fastened optimization approach. The average online PITR achieved by the proposed adaptive SSVEP speller, including the time required for breaks between choices and error correction, was 41.08 bit/min. These results indicate that our BCI speller is promising for use in SSVEP-primarily based BCI applications.