Objective. A passive brain-computer interface recognizes its operator’s cognitive state without an explicitly performed control task. This technique is commonly used in conjunction with consumer-grade EEG devices for detecting the conditions of fatigue, attention, emotional arousal, or motion sickness. While it is easy to mount the sensors in the forehead area, which is not covered with hair, the recorded signals become greatly contaminated with eyeblink and movement artifacts, which makes it a challenge to acquire the data of suitable for analysis quality, particularly in few channel systems where a lack of spatial information limits the applicability of sophisticated signal cleaning algorithms. In this article, we demonstrate that by combining the features associated with electrocortical activities and eyeblink recognition analysis, it becomes feasible to design an accurate system for the inattention state prediction using just a single EEG sensor. Approach. Fif...

中文翻译：

---

眨眼识别可改善单通道前额脑电图在实际持续关注任务中的疲劳预测。

目的。被动的脑机接口无需明确执行控制任务即可识别其操作员的认知状态。此技术通常与消费级EEG设备结合使用，以检测疲劳，注意力，情绪唤醒或晕动病的状况。虽然很容易将传感器安装在没有被头发覆盖的额头区域，但是记录的信号却被眨眼和运动伪影严重污染，这使得获取适合分析质量的数据成为挑战，特别是在少数情况下缺乏空间信息的通道系统限制了复杂信号清除算法的适用性。在本文中，我们证明了通过结合与皮层活动和眨眼识别分析相关的功能，仅使用一个EEG传感器来设计用于注意力不集中状态预测的准确系统就变得可行。方法。如果...