Objective: Event-related potentials (ERPs) reflect electropotential changes within specific cortical regions in response to specific events or stimuli during cognitive processes. The P300 speller is an important application of ERP-based brain-computer interfaces (BCIs), offering potential assistance to individuals with severe motor disabilities by decoding their electroencephalography (EEG) to communicate.
Methods: This study introduced a novel speller paradigm using a dynamically growing bubble (GB) visualization as the stimulus, departing from the conventional flash stimulus (TF). Additionally, we proposed a "Lock a Target by Two Flashes" (LT2F) method to offer more versatile stimulus flash rules, complementing the row and column (RC) and single character (SC) modes. We applied the "Sub and Global" multi-window mode to EEGNet (mwEEGNet) to enhance classification and explored the performance of eight other representative algorithms.
Results: Twenty healthy volunteers participated in the experiments. Our analysis revealed that our proposed pattern elicited more pronounced negative peaks in the parietal and occipital brain regions between 200 ms and 230 ms post-stimulus onset compared with the TF pattern. Compared to the TF pattern, the GB pattern yielded a 2.00% increase in online character accuracy (ACC) and a 5.39 bits/min improvement in information transfer rate (ITR) when using mwEEGNet. Furthermore, results demonstrated that mwEEGNet outperformed other methods in classification performance.
Conclusion And Significance: These results underscore the significance of our work in advancing ERP-based BCIs.
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