{'fraction_non_alphanumeric': 0.02556818181818182, 'fraction_numerical': 0.003409090909090909, 'mean_word_length': 5.57089552238806, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Dynamically Optimized SSVEP Brain–Computer Interface (BCI) Speller

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 number of items selectable on the interface, accuracy, and speed. In this approach, the row/column (RC) paradigm was employed in a SSVEP speller to increase the number of items. The target is detected by subsequently determining the row and column coordinates. To improve spelling accuracy, we added a posterior processing after the canonical correlation analysis (CCA) approach to reduce the interfrequency variation between different subjects and named the new signal processing method CCA-RV, and designed a real-time biofeedback mechanism to increase attention on the visual stimuli. To achieve reasonable online spelling speed, both fixed and dynamic approaches for setting the optimal stimulus duration were implemented and compared. Experimental results for 11 subjects suggest that the CCA-RV method and the real-time biofeedback effectively increased accuracy compared with CCA and the absence of real-time feedback, respectively. In addition, both optimization approaches for setting stimulus duration achieved reasonable online spelling performance. However, the dynamic optimization approach yielded a higher practical information transfer rate (PITR) than the fixed optimization approach. The average online PITR achieved by the proposed adaptive SSVEP speller, including the time required for breaks between selections and error correction, was 41.08 bit/min. These results indicate that our BCI speller is promising for use in SSVEP-based BCI applications.