Abstract
P300 speller is a famous brain-computer interface (BCI) method, which translates mental attention by identifying the event-related potentials evoked by target stimulus. To improve its efficiency, subject-independent classification models and dynamical stopping strategies have been introduced into P300 speller. However, it has still not been determined whether these methods remain effective when the configurations of visual stimuli are changed. This study investigates whether subject-independent dynamical stopping model (SIDSM) can maintain high efficiency in the case of stimulus onset asynchrony (SOA) change. The SIDSM was built on a 55-subject database, and the classification efficiency was tested online with 14 new subjects. During the online experiment, four SOA conditions were tested, one of which had the same SOA as the modeling data, while the other three had different SOA settings. The SIDSM obtained comparable classification accuracy under different SOA settings. Thus, the efficiency of information transmission can be significantly improved by changing SOA only, without retraining the model. These results suggest that SIDSM has good robustness to changes in stimulus settings and can provide P300 speller with good flexibility for individual optimization.
Highlights
Brain–computer interface (BCI) is a communication system in which an individual can send messages or commands to the external world without using brain’s normal output pathways of peripheral nerves and muscles [1]
P300 is a component of the event-related potential (ERP) response evoked by an oddball paradigm [6]
We can improve the performance of P300 speller by adjusting stimulus onset asynchrony (SOA) while maintaining the calibration-free advantage of subject-independent model (SIM)
Summary
Brain–computer interface (BCI) is a communication system in which an individual can send messages or commands to the external world without using brain’s normal output pathways of peripheral nerves and muscles [1]. To the best of our knowledge, the use of P300 recognition model in different SOAs has not yet been studied Another issue affecting performance and user experience in P300 speller is the so-called calibration problem. A P300 speller system needs to set up a recognition model before it can be used, which requires collection of EEG data containing P300 responses. In this study, our hypothesis was that a subject-independent dynamic stopping model (SIDSM) could continue to work well when SOA is changed. This may provide a basis for improving the efficiency of P300 speller by selecting a suitable SOA for different individuals, while maintain the non-calibration that is very important to the user experience
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