Preliminary Study on Rapid Serial Visualization Presentation Multi-Class Target EEG Classification

• Published in: IEEE transactions on biomedical engineering Vol. 72; no. 1; pp. 90 - 101
• Main Authors: Wei, Wei, Li, Xujin, Qiu, Shuang, He, Huiguang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adult; Algorithms; Amplitudes; BCI; Biochips; Brain; Brain - physiology; Brain modeling; Brain-Computer Interfaces; Classification; Computer applications; Data analysis; EEG; Electroencephalography; Electroencephalography - classification; Electroencephalography - methods; Event-related potentials; Feasibility studies; Female; Human-computer interface; Humans; Image retrieval; Implants; Information processing; Information retrieval; Information systems; Information transfer; Latency; Male; multi-class target image retrieval; Remote sensing; RSVP; Signal Processing, Computer-Assisted; Task analysis; Toy manufacturing industry; Visualization; Young Adult
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2024.3439820

Objective: Brain-Computer Interface (BCI) provides a direct communication channel between the brain and external devices. After combining with the Rapid Serial Visualization Presentation (RSVP) paradigm, the RSVP-BCI system can be utilized for human vision-based fast information retrieval. Currently only binary classification of single-trial EEG can be achieved, also the research on the multi-class target RSVP is few, which limited information transfer rate and the application scenarios of the system. In this paper, we focus on the RSVP multi-class target image retrieval task that contains two classes of targets for achieving triple classification for RSVP-EEG. Methods: Designed two experiments, each containing two tasks with different task difficulties. We recruited 30 subjects to participate in the experiments, collected EEG data, and made the data publicly available. Moreover, we conducted behavioral analysis, ERP analysis, and proposed a model, MDCNet, for EEG classification to study the feasibility of multi-class target RSVP and the impact of task difficulty. Results: The experimental results indicated that (1) RSVP-EEG classification that includes non-target and 2-class target is feasibility; (2) the different targets in the same task will evoke P300 with the same latency and different amplitudes, and the hit rate of the target in EEG classification is positively correlated with its amplitude; (3) the information hidden in the time dimension play an important role in EEG classification; (4) the harder the task is, the latency of P300 is longer. Conclusion/Significance: The experimental analysis obtained meaningful results, which provided a theoretical basis for subsequent research.