CHS: Small: Enhancing EEG-based Emotion Estimation with Transfer Learning, Priming, and Virtual Reality

CHS：小：通过迁移学习、启动和虚拟现实增强基于脑电图的情绪估计

• 批准号: 1910526
• 负责人: David Thompson
• 金额: $ 50万
• 依托单位: Kansas State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1910526&HistoricalAwards=false
• 关键词: CHS; Small; Enhancing; EEG; based

Emotions influence the decisions we make every day. However, emotions are difficult to measure. Surveys, the traditional method of emotion measurement, take significant time and cause interruptions. Further, people do not always know their own emotions -- think of the times when a person has shouted, "I'm not shouting!" Direct emotion-measuring systems, based on sensors worn on the body or head, have shown promise but are not yet ready to compete with surveys. This project includes several new techniques designed to increase the reliability and performance of these systems. The project will lay the groundwork for wearable devices that can be used outside the laboratory, in environments as varied as classrooms and theme parks. From there, this research could lead to the development of human-computer systems which adjust in real-time to maintain a person's interest. Such systems would be valuable for all levels of education, as well as entertainment and other fields. During the project, women and members of underrepresented minorities will participate, both as researchers and as research participants. This project is one of the first to combine emotion-estimating Brain-Computer Interfaces (BCIs) with three methods: transfer learning, emotion priming, and virtual reality. Transfer learning is expected to help BCI systems detect real emotions rather than setting-specific brain responses. Transfer learning techniques will be applied to data from different emotion elicitation paradigms to study how well training data from standard methods can be expected to work in new environments. An alternative training technique from psychology called "emotion priming" will be used, and the effects on transfer learning will be studied. Emotion priming is expected to increase the accuracy of emotion elicitation, increasing the quality of training data and thus system performance. Virtual reality will be used to manipulate the participants' arousal states through a larger range than is possible through pictures and videos, in order to generate more transferable results. Together, these investigations are expected to dramatically improve the performance and, critically, the cross-task reliability of these systems. Because the study of emotion impacts nearly every field of human study, the development of a system for real-time measurement of valid emotions has the potential to advance cross-disciplinary transformation. Overall, these tests will fill a knowledge gap in the scientific literature, and inform how future general-purpose emotion estimation systems are trained. In addition, the project will provide shared data that may lead to even greater advancements in the BCI field when used by future researchers.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

情绪会影响我们每天做出的决定。但是，情绪很难衡量。调查是传统的情感测量方法，需要大量时间并引起干扰。此外，人们并不总是知道自己的情绪 - 想想一个人大喊的时代：“我不大喊！” 基于身体或头部上戴的传感器的直接情感测量系统已显示出希望，但尚未准备好与调查竞争。该项目包括几种旨在提高这些系统的可靠性和性能的新技术。 该项目将为可穿戴设备奠定基础，这些设备可在实验室外，在教室和主题公园等各种环境中使用。从那里开始，这项研究可能会导致人类计算机系统的开发，这些系统实时调整以维持一个人的兴趣。这样的系统对于所有级别的教育以及娱乐和其他领域都有价值。 在项目期间，无论是研究人员还是作为研究参与者，妇女和代表性不足的少数群体的成员都将参加。该项目是最早将情绪估计脑部计算机界面（BCIS）与三种方法相结合的项目之一：转移学习，情感启动和虚拟现实。转移学习有望帮助BCI系统检测真实情绪，而不是设定特定的大脑反应。转移学习技术将应用于来自不同情绪启发范式的数据，以研究预期在新环境中可以从标准方法起作用的训练数据。将使用一种名为“情感启动”的心理学的替代培训技术，将研究转移学习的影响。预计情绪启动会提高情绪启发的准确性，从而提高训练数据的质量，从而增加系统性能。虚拟现实将通过与图片和视频相比，通过更大的范围来操纵参与者的唤醒状态，以产生更可转移的结果。总之，这些研究将大大提高性能，并批判性地提高这些系统的交叉任务可靠性。由于对情绪的研究几乎影响了人类研究的每个领域，因此实时测量有效情绪的系统的开发有可能提高跨学科转型。总体而言，这些测试将填补科学文献中的知识差距，并告知未来通用情绪估计系统的培训。此外，该项目将提供共同的数据，在未来的研究人员使用时，该数据可能会导致BCI领域的进步。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，认为值得通过评估来获得支持。

项目成果

Automated Artifact Rejection Algorithms Harm P3 Speller Brain-Computer Interface Performance.

• DOI: 10.1080/2326263x.2020.1734401
• 发表时间: 2019
• 期刊: Brain computer interfaces (Abingdon, England)
• 作者: Thompson DE;Mowla MR;Dhuyvetter KJ;Tillman JW;Huggins JE
• 通讯作者: Huggins JE

Affective brain-computer interfaces: Choosing a meaningful performance measuring metric

• DOI: 10.1016/j.compbiomed.2020.104001
• 发表时间: 2020-11-01
• 期刊: COMPUTERS IN BIOLOGY AND MEDICINE
• 影响因子: 7.7
• 作者: Mowla, Md Rakibul;Cano, Rachael, I;Thompson, David E.
• 通讯作者: Thompson, David E.

Do Automatic Artifact Removal Algorithms Reduce Accuracy By Removing Dependence on Post-Stimulus Blinks

自动伪影去除算法是否会通过消除对刺激后眨眼的依赖来降低准确性

• 发表时间: 2023
• 期刊: BCI Society Meeting 2023
• 作者: Harvey, Chase;Chan, David;Sweet, Taylor;Huggins, Jane E.;Thompson, David E.
• 通讯作者: Thompson, David E.

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation.

基于 P300 的脑机接口拼写器性能估计和基于分类器的延迟估计。

• DOI: 10.3791/64959
• 发表时间: 2023
• 期刊: Journal of visualized experiments : JoVE
• 作者: Khan,NazmunN;Sweet,Taylor;Harvey,ChaseA;Warschausky,Seth;Huggins,JaneE;Thompson,DavidE
• 通讯作者: Thompson,DavidE

Cross-Task Transfer Learning in Emotion Estimating BCI

情绪估计 BCI 中的跨任务迁移学习

• 发表时间: 2023
• 期刊: BCI Society Meeting 2023
• 作者: Khan, Nazmun;Thompson, David E.
• 通讯作者: Thompson, David E.