A fisher linear discriminant analysis classifier fused with naïve Bayes for simultaneous detection in an asynchronous brain-computer interface.

Background: An asynchronous event-related potential-based brain computer interface (ERP-BCI) permits the subjects to output intentions at their own pace, which provides a more free and practical communication pathways without the need for muscle activity. The core of constructing this type of system is to discriminate both the intentions and brain states.
New Methods: This study proposes a fisher linear discriminant analysis classification algorithm fused with naïve Bayes (B-FLDA) for the ERP-BCI to simultaneous recognize the subjects' intentions, working and idle states. This method uses the spectral characteristics of visual-evoked potential and the time-domain characteristics of ERP to simultaneously detect brain states and target stimulus, and obtain the final discrimination result through probability fusion.
Results: The accuracy and the information transfer rate increase to 98.61% and 62.80 bits/min under 10 repetitions and 1 repetition, respectively. The three parameters of receiver operator characteristic curve have achieved better performance.
Comparison With Existing Methods: Ten subjects participate in this study with the proposed algorithms and two other control methods. The accuracy and information transfer rate of this algorithm are better than the other methods.
Conclusions: It indicates that the naïve Bayes-FLDA algorithm is able to improve the performance of an asynchronous BCI system by detecting the intentions and states simultaneously.
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