Wei Wei, Li Xujin, Qiu Shuang, He Huiguang
IEEE Trans Biomed Eng. 2025 Jan;72(1):90-101. doi: 10.1109/TBME.2024.3439820. Epub 2025 Jan 15.
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.
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.
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.
脑机接口（BCI）在大脑与外部设备之间提供了一条直接的通信通道。与快速序列视觉呈现（RSVP）范式相结合后，RSVP-BCI系统可用于基于人类视觉的快速信息检索。目前，RSVP-BCI系统仅能实现单次试验脑电图的二分类，且针对多类目标RSVP的研究较少，这限制了系统的信息传输速率和应用场景。在本文中，我们聚焦于包含两类目标的RSVP多类目标图像检索任务，以实现RSVP-脑电图的三分类。
设计了两个实验，每个实验包含两个具有不同任务难度的任务。我们招募了30名受试者参与实验，采集脑电图数据，并将数据公开。此外，我们进行了行为分析、事件相关电位（ERP）分析，并提出了一种用于脑电图分类的模型MDCNet，以研究多类目标RSVP的可行性以及任务难度的影响。
实验结果表明：（1）包括非目标和两类目标的RSVP-脑电图分类是可行的；（2）同一任务中的不同目标会诱发潜伏期相同但波幅不同的P300，脑电图分类中目标的命中率与其波幅呈正相关；（3）时间维度中隐藏的信息在脑电图分类中起重要作用；（4）任务越难，P300的潜伏期越长。
结论/意义：实验分析取得了有意义的结果，为后续研究提供了理论依据。
