论文标题

使用大脑计算机界面检测注意力模式

Attention Patterns Detection using Brain Computer Interfaces

论文作者

Hamza-Lup, Felix G., Suri, Adytia, Iacob, Ionut E., Goldbach, Ioana R., Rasheed, Lateef, Borza, Paul N.

论文摘要

人的大脑提供了一系列功能,例如表达情绪,控制呼吸速度等,并且其研究吸引了科学家的兴趣多年。随着机器学习模型变得越来越复杂,通过新的非侵入性技术变得更加容易获得生物 - 金属数据,越来越有可能访问有趣的生物识别数据,从而彻底改变了人类计算机的交互。在这项研究中,我们提出了一种评估和量化人类注意力水平及其对学习影响的方法。在我们的研究中,我们采用了能够检测脑波活性并显示相应脑电图(EEG)的大脑计算机界面(BCI)。我们训练复发性神经网络(RNN),以确定个人正在执行的活动类型。

The human brain provides a range of functions such as expressing emotions, controlling the rate of breathing, etc., and its study has attracted the interest of scientists for many years. As machine learning models become more sophisticated, and bio-metric data becomes more readily available through new non-invasive technologies, it becomes increasingly possible to gain access to interesting biometric data that could revolutionize Human-Computer Interaction. In this research, we propose a method to assess and quantify human attention levels and their effects on learning. In our study, we employ a brain computer interface (BCI) capable of detecting brain wave activity and displaying the corresponding electroencephalograms (EEG). We train recurrent neural networks (RNNS) to identify the type of activity an individual is performing.

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