Target Detection Using Ternary Classification During a Rapid Serial Visual Presentation Task Using Magnetoencephalography Data

Chuncheng Zhang, Shuang Qiu, Shengpei Wang, Huiguang He

Abstract: Background: The rapid serial visual presentation (RSVP) paradigm is a high-speed paradigm of brain–computer interface (BCI) applications. The target stimuli evoke event-related potential (ERP) activity of odd-ball effect, which can be used to detect the onsets of targets. Thus, the neural control can be produced by identifying the target stimulus. However, the ERPs in single trials vary in latency and length, which makes it difficult to accurately discriminate the targets against their neighbors, the near-non-targets. Thus, it reduces the efficiency of the BCI paradigm.Methods: To overcome the difficulty of ERP detection against their neighbors, we proposed a simple but novel ternary classification method to train the classifiers. The new method not only distinguished the target against all other samples but also further separated the target, near-non-target, and other, far-non-target samples. To verify the efficiency of the new method, we performed the RSVP experiment. The natural scene pictures with or without pedestrians were used; the ones with pedestrians were used as targets. Magnetoencephalography (MEG) data of 10 subjects were acquired during presentation. The SVM and CNN in EEGNet architecture classifiers were used to detect the onsets of target.Results: We obtained fairly high target detection scores using SVM and EEGNet classifiers based on MEG data. The proposed ternary classification method showed that the near-non-target samples can be discriminated from othe...