Predicting and understanding the changes in cognitive performance, especially
after a longitudinal intervention, is a fundamental goal in neuroscience.
Longitudinal brain stimulation-based interventions like transcranial direct
current stimulation (tDCS) induce short-term changes in the resting membrane
potential and influence cognitive processes. However, very little research has
been conducted on predicting these changes in cognitive performance
post-intervention. In this research, we intend to address this gap in the
literature by employing different EEG-based functional connectivity analyses
and machine learning algorithms to predict changes in cognitive performance in
a complex multitasking task. Forty subjects were divided into experimental and
active-control conditions. On Day 1, all subjects executed a multitasking task
with simultaneous 32-channel EEG being acquired. From Day 2 to Day 7, subjects
in the experimental condition undertook 15 minutes of 2mA anodal tDCS
stimulation during task training. Subjects in the active-control condition
undertook 15 minutes of sham stimulation during task training. On Day 10, all
subjects again executed the multitasking task with EEG acquisition.
Source-level functional connectivity metrics, namely phase lag index and
directed transfer function, were extracted from the EEG data on Day 1 and Day
10. Various machine learning models were employed to predict changes in
cognitive performance. Results revealed that the multi-layer perceptron and
directed transfer function recorded a cross-validation training RMSE of 5.11%
and a test RMSE of 4.97%. We discuss the implications of our results in
developing real-time cognitive state assessors for accurately predicting
cognitive performance in dynamic and complex tasks post-tDCS intervention

在这项研究中，我们通过使用不同的基于 EEG 的功能连接分析和机器学习算法，来预测复杂多任务任务中认知表现的变化。结果显示，多层感知器和有向传递函数具有较高的预测准确率。

基于脑电功能连接性和机器学习方法的经纵向 tDCS 后多任务执行能力预测

Prediction of multitasking performance post-longitudinal tDCS via  EEG-based functional connectivity and machine learning methods

Physiological fatigue, a state of reduced cognitive and physical performance
resulting from prolonged mental or physical exertion, poses significant
challenges in various domains, including healthcare, aviation, transportation,
and industrial sectors. As the understanding of fatigue's impact on human
performance grows, there is a growing interest in developing effective fatigue
monitoring techniques. Among these techniques, electroencephalography (EEG) has
emerged as a promising tool for objectively assessing physiological fatigue due
to its non-invasiveness, high temporal resolution, and sensitivity to neural
activity. This paper aims to provide a comprehensive analysis of the current
state of the use of EEG for monitoring physiological fatigue.

该论文综合分析了当前使用脑电图（EEG）来监测生理疲劳的现状。

疲劳监测的脑电图

EEG for fatigue monitoring

We will soon be surrounded by artificial systems capable of cognitive
performance rivaling or exceeding a human expert in specific domains of
discourse. However, these cogs need not be capable of full general artificial
intelligence nor able to function in a stand-alone manner. Instead, cogs and
humans will work together in collaboration each compensating for the weaknesses
of the other and together achieve synthetic expertise as an ensemble. This
paper reviews the nature of expertise, the Expertise Level to describe the
skills required of an expert, and knowledge stores required by an expert. By
collaboration, cogs augment human cognitive ability in a human/cog ensemble.
This paper introduces six Levels of Cognitive Augmentation to describe the
balance of cognitive processing in the human/cog ensemble. Because these cogs
will be available to the mass market via common devices and inexpensive
applications, they will lead to the Democratization of Expertise and a new
cognitive systems era promising to change how we live, work, and play. The
future will belong to those best able to communicate, coordinate, and
collaborate with cognitive systems.

论文考察了人工系统的认知性能和专家技能所需的特定技能水平以及专业知识储备，并介绍了人工智能 /cog 合奏中认知加强的六个级别，旨在通过这种合作达到合成专业知识的目的，进而实现专业知识的民主化和新的认知系统时代的到来。

合成专业知识

Synthetic Expertise

Human cognitive performance is critical to productivity, learning, and
accident avoidance. Cognitive performance varies throughout each day and is in
part driven by intrinsic, near 24-hour circadian rhythms. Prior research on the
impact of sleep and circadian rhythms on cognitive performance has typically
been restricted to small-scale laboratory-based studies that do not capture the
variability of real-world conditions, such as environmental factors,
motivation, and sleep patterns in real-world settings. Given these limitations,
leading sleep researchers have called for larger in situ monitoring of sleep
and performance. We present the largest study to date on the impact of
objectively measured real-world sleep on performance enabled through a
reframing of everyday interactions with a web search engine as a series of
performance tasks. Our analysis includes 3 million nights of sleep and 75
million interaction tasks. We measure cognitive performance through the speed
of keystroke and click interactions on a web search engine and correlate them
to wearable device-defined sleep measures over time. We demonstrate that
real-world performance varies throughout the day and is influenced by both
circadian rhythms, chronotype (morning/evening preference), and prior sleep
duration and timing. We develop a statistical model that operationalizes a
large body of work on sleep and performance and demonstrates that our estimates
of circadian rhythms, homeostatic sleep drive, and sleep inertia align with
expectations from laboratory-based sleep studies. Further, we quantify the
impact of insufficient sleep on real-world performance and show that two
consecutive nights with less than six hours of sleep are associated with
decreases in performance which last for a period of six days. This work
demonstrates the feasibility of using online interactions for large-scale
physiological sensing.

本研究通过对 3 百万晚上的睡眠和 7,500 万互动任务的分析， 提出了有关真实世界中客观测量的睡眠对认知表现的影响的最大研究，并证明了真实世界中的表演受循环节律，钟型（早晨 / 晚上偏好）和前一次睡眠持续时间和时序的影响。我们开发了一个统计模型，证明了睡眠和表现方面的大量工作的效果，展示了缺乏睡眠对真实世界表现的影响，并表明连续两个晚上睡眠不到六个小时会导致表现下降，持续六天。