Department of Electronic Engineering, Shanghai Maritime University, Shanghai, China.
Department of Computer Software and Theory, Tongji University, Shanghai, China.
Technol Health Care. 2020;28(S1):173-180. doi: 10.3233/THC-209017.
Mental task-based brain computer interface (BCI) systems are usually developed for neural prostheses technologies and medical rehabilitation. The mental workload was too heavy for the user to manipulate BCI effectively. Fortunately, electroencephalography (EEG) signal is not only used for BCI control but also relates to the changes of mental states.
We proposed a novel method for identifying non-effective trials of Steady State Visual Evoked Potential (SSVEP)-based BCI.
We used the subject-dependent and subject-independent alertness models identifying non-effective trials of SSVEP-BCI systems.
The result implied that the subject-dependent alertness model was most useful for improving the classification accuracy in the task. However, the subject-independent alertness model could enhance the prediction ability of SSVEP-based BCI system.
In comparison to the conventional canonical correlation analysis (CCA) method without alertness-model filtering, the raise of precision was valuable for the technical development of BCI works. It demonstrated the effectiveness of our proposed subject-dependent and subject-independent methods.
基于精神任务的脑机接口（BCI）系统通常是为神经假体技术和医疗康复而开发的。精神工作量对于用户来说太重，无法有效地操作 BCI。幸运的是，脑电图（EEG）信号不仅用于 BCI 控制，而且还与精神状态的变化有关。
我们提出了一种识别基于稳态视觉诱发电位（SSVEP）的 BCI 中无效试验的新方法。
我们使用基于个体和独立于个体的警觉模型来识别 SSVEP-BCI 系统中的无效试验。
结果表明，基于个体的警觉模型对于提高任务中的分类准确性最有用。但是，独立于个体的警觉模型可以提高 SSVEP 为基础的 BCI 系统的预测能力。
与不使用警觉模型过滤的传统典型相关分析（CCA）方法相比，精度的提高对于 BCI 工作的技术发展具有重要价值。它证明了我们提出的基于个体和独立于个体的方法的有效性。
