This paper presents a novel approach for analysing EEG data from drivers in a
simulated driving test. We focused on the Hurst exponent, Shannon entropy, and
fractal dimension as markers of the nonlinear dynamics of the brain. The
results show significant trends: Shannon Entropy and Fractal Dimension exhibit
variations during driving condition transitions, whereas the Hurst exponent
reflects memory retention portraying learning patterns. These findings suggest
that the tools of Non-linear Dynamical (NLD) Theory as indicators of cognitive
state and driving memory changes for assessing driver performance and advancing
the understanding of non-linear dynamics of human cognition in the context of
driving and beyond. Our study reveals the potential of NLD tools to elucidate
brain state and system variances, enabling their integration into current Deep
Learning and Machine Learning models. This integration can extend beyond
driving applications and be harnessed for cognitive learning, thereby improving
overall productivity and accuracy levels.

通过分析脑电图数据，识别驾驶员认知和驾驶状态的非线性动力学特征，以优化驾驶表现并促进认知学习、提高生产力和准确性。

通过司机认知的定量评估提升对驾驶特征的理解

Enhancing Understanding of Driving Attributes through Quantitative  Assessment of Driver Cognition

When we communicate with other humans, we do not simply generate a sequence
of words. Rather, we use our cognitive state (beliefs, desires, intentions) and
our model of the audience's cognitive state to create utterances that affect
the audience's cognitive state in the intended manner. An important part of
cognitive state is the common ground, which is the content the speaker
believes, and the speaker believes the audience believes, and so on. While much
attention has been paid to common ground in cognitive science, there has not
been much work in natural language processing. In this paper, we introduce a
new annotation and corpus to capture common ground. We then describe some
initial experiments extracting propositions from dialog and tracking their
status in the common ground from the perspective of each speaker.

我们介绍了一种新的标注和语料库来捕捉共同基础，然后从每个说话者的角度描述了一些从对话中提取命题并在共同基础中追踪其状态的初步实验。

寻找共同基础：口语对话中的注释和预测共同基础

Finding Common Ground: Annotating and Predicting Common Ground in Spoken  Conversations

Driver monitoring systems (DMS) are a key component of vehicular safety and
essential for the transition from semiautonomous to fully autonomous driving. A
key task for DMS is to ascertain the cognitive state of a driver and to
determine their level of tiredness. Neuromorphic vision systems, based on event
camera technology, provide advanced sensing of facial characteristics, in
particular the behavior of a driver's eyes. This research explores the
potential to extend neuromorphic sensing techniques to analyze the entire
facial region, detecting yawning behaviors that give a complimentary indicator
of tiredness. A neuromorphic dataset is constructed from 952 video clips (481
yawns, 471 not-yawns) captured with an RGB color camera, with 37 subjects. A
total of 95200 neuromorphic image frames are generated from this video data
using a video-to-event converter. From these data 21 subjects were selected to
provide a training dataset, 8 subjects were used for validation data, and the
remaining 8 subjects were reserved for an "unseen" test dataset. An additional
12300 frames were generated from event simulations of a public dataset to test
against other methods. A CNN with self-attention and a recurrent head was
designed, trained, and tested with these data. Respective precision and recall
scores of 95.9 percent and 94.7 percent were achieved on our test set, and 89.9
percent and 91 percent on the simulated public test set, demonstrating the
feasibility to add yawn detection as a sensing component of a neuromorphic DMS.

本研究探索了将类脑视觉技术扩展到分析整个面部区域的潜力，以检测疲劳的补充指标 —— 打哈欠行为，并使用新型事件相机技术构建了基于类脑神经元的数据集，从而设计、培训和测试了一个用于检测打哈欠的具有自我关注和循环头的卷积神经网络。