EAGER: Adapting Multi-Modal BCI-Based Assistive Technologies for Patients with High Spinal Cord Injury

EAGER：为高位脊髓损伤患者采用基于多模式 BCI 的辅助技术

• 批准号: 1841087
• 负责人: Laleh Najafizadeh
• 金额: $ 12万
• 依托单位: Rutgers University New Brunswick
• 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: 2019-06-15 至 2022-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1841087&HistoricalAwards=false
• 关键词: EAGER; Adapting; Multi; Modal; BCI

The goal of this research is to advance the brain computer interface (BCI) technology by bringing the technology closer to the needs of patients with high spinal cord injury (SCI). While BCI-based assistive technologies hold a great promise to improve the quality of life of patients with SCI by enabling them to independently perform simple activities of their daily life, several factors have prevented their successful application in practical and clinical settings. A major contributing factor to this problem is related to the secondary health conditions (e.g. chronic pain) that are developed as a result of SCI, but have been largely ignored in BCI research. The presence of pain can negatively impact the control signals acquired from BCIs, and consequently, compromise the operation of the assistive device, thereby, adding to stress, discomfort, and depression for the patient. Using innovative approaches and experiments, this research aims to investigate how the presence of pain impacts the operation of multi-modal BCIs, thereby, promoting the progress of science. By providing an understanding of the effects that pain, the outcome of this work is expected to provide guidelines for developing effective methods for realizing robust and high-performance BCI-based assistive technologies for SCI patients. This project will also offer educational and outreach activities for the unrepresentative researches working in a STEM field.This research aims to advance the non-invasive multi-modal brain computer interface (BCI) technology by bringing the technology closer to the needs of patients with high spinal cord injury (SCI). By collecting information from the brain at two different physiological levels via electroencephalography (EEG) and functional near infrared spectroscopy (fNIRS), the number of commands required for controlling assistive devices can be increased. Using innovative approaches, the project will investigate how the presence of pain, a condition that unfortunately most SCI patients live with, will influence the operation of multi-modal BCIs. Different classes of multi-modal BCIs are considered. The results from this project are expected to provide important information and guidelines for developing effective strategies and methods for realizing robust and high-performance BCI-based assistive technologies for SCI patients who are in real need of them.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.

本研究的目的是通过使脑机接口（BCI）技术更接近高位脊髓损伤（SCI）患者的需求来推进该技术。虽然基于BCI的辅助技术通过使SCI患者能够独立地进行日常生活中的简单活动来改善SCI患者的生活质量，但有几个因素阻碍了它们在实际和临床环境中的成功应用。这个问题的一个主要影响因素与SCI导致的继发性健康状况（例如慢性疼痛）有关，但在BCI研究中基本上被忽视了。疼痛的存在可能对从BCI获取的控制信号产生负面影响，并且因此损害辅助设备的操作，从而增加患者的压力、不适和抑郁。本研究旨在通过创新的方法和实验，研究疼痛的存在如何影响多模式脑机接口的运行，从而促进科学的进步。通过了解疼痛的影响，这项工作的结果有望为开发有效的方法提供指导，为SCI患者实现强大和高性能的基于BCI的辅助技术。这项研究旨在推动无创多模态脑机接口（BCI）技术的发展，使该技术更贴近高位脊髓损伤（SCI）患者的需要。通过脑电图（EEG）和功能性近红外光谱（fNIRS）从两个不同生理水平的大脑收集信息，可以增加控制辅助设备所需的命令数量。使用创新的方法，该项目将调查疼痛的存在，不幸的是，大多数SCI患者都患有疼痛，这将如何影响多模式脑机接口的操作。考虑不同类别的多模态BCI。该项目的成果有望为制定有效的策略和方法提供重要的信息和指导，从而为真实的需要的SCI患者实现强大和高性能的基于BCI的辅助技术。该奖项反映了NSF的法定使命，并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，被认为值得支持。

项目成果

On the Effects of Pain on fNIRS Classification

疼痛对 fNIRS 分类的影响

• 发表时间: 2020
• 期刊: The OSA Biophotonics Congress
• 作者: Shamsi, F.;Najafizadeh, L.
• 通讯作者: Najafizadeh, L.

On the Reliability of Frequency-Domain Features for fNIRS BCIs in the Presence of Pain

存在疼痛时 fNIRS BCI 频域特征的可靠性

• DOI: 10.1109/spmb52430.2021.9672256
• 发表时间: 2021
• 期刊: IEEE Signal Processing in Medicine and Biology Symposium (SPMB
• 作者: Subramanian, A.;Shamsi, F.;Najafizadeh, L.
• 通讯作者: Najafizadeh, L.

Recognizing Pain in Motor Imagery EEG Recordings Using Dynamic Functional Connectivity Graphs

使用动态功能连接图识别运动想象脑电图记录中的疼痛

• DOI: 10.1109/embc44109.2020.9175627
• 发表时间: 2020
• 期刊: 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC
• 作者: Shamsi, Foroogh;Haddad, Ali;Najafizadeh, Laleh
• 通讯作者: Najafizadeh, Laleh

Multi-class Classification of Motor Execution Tasks using fNIRS

• DOI: 10.1109/spmb47826.2019.9037856
• 发表时间: 2019-12
• 期刊: 2019 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)
• 作者: F. Shamsi;L. Najafizadeh