CHS:Small: Improved Cross-Subject Cognitive and Emotional State Classification Using Functional Near-Infrared Spectroscopy Data for Deep Learning

CHS：Small：使用深度学习的功能性近红外光谱数据改进跨主题认知和情绪状态分类

• 批准号: 1816732
• 负责人: Senem Velipasalar
• 金额: $ 49.44万
• 依托单位: Syracuse University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-15 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1816732&HistoricalAwards=false
• 关键词: CHS; Small; Improved; Cross; Subject

New advances in bio-technology suggest devices to wear and measure the brain will be available to support many different activities. Future technologies may use brain activity data to adapt or customize educational software in real-time. Activity support based on interpreted brain activity could be used to reduce mental workload, modify emotional states, or help someone with post-traumatic stress disorder. However, brain activity data is complex and difficult to interpret. This project will use deep machine learning methods to overcome the challenge of classifying and interpreting brain activity data using real-time data from participants. The objective is to harness the tremendous potential of cognitive sensors and computational methods to help individuals function more effectively. Although many early successes were achieved using machine learning on brain data, several notable challenges have arisen, which significantly limit the impacts of these early successes. The technical approach in this project has three research thrusts. The investigators will develop models specifically for use on high density functional-near infrared spectroscopy (fNIRS) data. Thrust 1 involves the development of advanced deep learning techniques that are particularly well-suited for fNIRS data, and address spatial and temporal inter-relations. Thrust 2 involves development and adaptation of algorithm transparency (AT) techniques that are well-suited to shed light on brain dynamics embedded within the deep learning model structures. This will help the research team interpret the underlying structure of the models, with respect to brain spatial and temporal dynamics at the individual and group level. Thrust 3 collates the model and AT techniques developed in the prior thrusts and evaluates them using an extensive cross-subject and cross-participant fNIRS dataset. Using this data for evaluation purposes, the research team will work together to interpret results to improve upon classifier performance and model generalizability.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.

生物技术的新进展表明，佩戴和测量大脑的设备将可用于支持许多不同的活动。未来的技术可能会使用大脑活动数据来实时调整或定制教育软件。基于解释大脑活动的活动支持可以用来减少心理工作量，改善情绪状态，或帮助患有创伤后应激障碍的人。然而，大脑活动数据复杂且难以解释。该项目将使用深度机器学习方法来克服使用参与者的实时数据对大脑活动数据进行分类和解释的挑战。目标是利用认知传感器和计算方法的巨大潜力，帮助个人更有效地发挥作用。 尽管在大脑数据上使用机器学习取得了许多早期成功，但也出现了一些值得注意的挑战，这些挑战大大限制了这些早期成功的影响。该项目的技术方法有三个研究重点。研究人员将开发专门用于高密度功能近红外光谱（fNIRS）数据的模型。重点1涉及开发特别适合fNIRS数据的高级深度学习技术，并解决空间和时间的相互关系。第二个目标涉及算法透明性（AT）技术的开发和调整，这些技术非常适合揭示嵌入深度学习模型结构中的大脑动力学。这将有助于研究团队解释模型的基本结构，在个体和群体水平上的大脑空间和时间动态。Thrust 3整理了在之前的研究中开发的模型和AT技术，并使用广泛的跨学科和跨参与者fNIRS数据集对其进行了评估。使用这些数据进行评估，研究团队将共同努力解释结果，以提高分类器的性能和模型的普适性。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

项目成果

Autonomous Human Activity Classification From Wearable Multi-Modal Sensors

• DOI: 10.1109/jsen.2019.2934678
• 发表时间: 2019-12-01
• 期刊: IEEE SENSORS JOURNAL
• 影响因子: 4.3
• 作者: Lu, Yantao;Velipasalar, Senem
• 通讯作者: Velipasalar, Senem

ViewNet: A Novel Projection-Based Backbone with View Pooling for Few-shot Point Cloud Classification

• DOI: 10.1109/cvpr52729.2023.01693
• 发表时间: 2023-06
• 期刊: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
• 作者: Jiajing Chen;Min Yang;Senem Velipasalar

Classification of fNIRS Finger Tapping Data With Multi-Labeling and Deep Learning

利用多重标记和深度学习对 fNIRS 手指敲击数据进行分类

• DOI: 10.1109/jsen.2021.3115405
• 发表时间: 2021
• 期刊: IEEE Sensors Journal
• 影响因子: 4.3
• 作者: Sommer, Natalie M.;Kakillioglu, Burak;Grant, Trevor;Velipasalar, Senem;Hirshfield, Leanne
• 通讯作者: Hirshfield, Leanne

Enhancing Cross-Task Black-Box Transferability of Adversarial Examples With Dispersion Reduction

• DOI: 10.1109/cvpr42600.2020.00102
• 发表时间: 2019-11
• 期刊: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
• 作者: Yantao Lu;Yunhan Jia;Jianyu Wang-;Bai Li;Weiheng Chai;L. Carin;Senem Velipasalar

Gaitpoint: A Gait Recognition Network Based on Point Cloud Analysis

• DOI: 10.1109/icip46576.2022.9897558
• 发表时间: 2022-10
• 期刊: 2022 IEEE International Conference on Image Processing (ICIP)
• 作者: Jiajing Chen;Huantao Ren;F. Chen;Senem Velipasalar;V. Phoha