Multimodal Intelligent Neural Interface for Early Detection and Treating of Post-stroke Dementia

用于早期检测和治疗中风后痴呆症的多模态智能神经接口

• 批准号: 2901977
• 金额: --
• 依托单位: University of Sheffield
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2901977
• 关键词: Multimodal; Intelligent; Neural; Interface; Early

The prevalence of stroke will double in 20 years. Almost a quarter of people who have stroke will develop signs of dementia after three to six months. The need for improving assessment, monitoring, and treating post-stroke cognitive impairment and dementia is listed 2nd in the top 10 priorities for stroke rehabilitation by The UK Stroke Association.This timely project aims to address this growing global challenge by improving the early detection and treatment of post-stroke dementia. This project has two main objectives:- Improve early diagnosis of dementia in stroke survivors by developing a multimodal, wearable, and intelligent assessment tool, consisting of computerised cognitive tests, brain recordings from a low-density EEG system, and CognoSpeaK [1] (our newly developed intelligent tool that automatically measures cognitive function by analysing speech)- Explore the feasibility, acceptability and usability of our newly developed P300-based brain-computer interface game [2] for improving cognitive performance in stroke survivors with and without mild cognitive impairment.To achieve the above-mentioned objectives, you will require to design and conduct experimental research, collect longitudinal data from stroke patients, process the collected data, and develop machine learning algorithms to identify robust biomarkers that could accurately monitor cognitive function and predict its failure in advance.You will work in Brain-computer Interface group at University of Sheffield. Our group is uniquely multidisciplinary and diverse, integrating exceptional research programs that span bioengineering, data science and clinical, experimental and computational neuroscience. The research vision of our group is to develop brain-directed therapies and tools for improving human's cognitive and physical performance. You will participate in a highly interdisciplinary project and interact and collaborate with system engineers, computer scientists, neuroscientist and medical doctors. You will receive broad range of training in our team, including EEG data recording and processing, statistical analysis, machine learning on biological data, ethics in research and data management, as well as scientific writing and presentation. You also have the opportunity to gain unique skills on commercialising biomedical devices.

中风的患病率将在20年内翻一番。几乎四分之一的中风患者会在三到六个月后出现痴呆症的迹象。改善中风后认知障碍和痴呆症的评估、监测和治疗的需求被英国中风协会列为中风康复十大优先事项中的第二位。这个及时的项目旨在通过改善早期发现和治疗来应对这一日益增长的全球挑战。中风后痴呆症。该项目有两个主要目标：- 通过开发多模式、可穿戴和智能评估工具，包括计算机化认知测试、低密度EEG系统的大脑记录和CognoSpeaK，改善中风幸存者痴呆症的早期诊断[1]（我们新开发的智能工具，通过分析语音自动测量认知功能）-探索我们新开发的基于P300的脑机接口游戏[2]的可行性，可接受性和可用性，以改善有和没有轻度认知障碍的中风幸存者的认知表现。为了实现上述目标，你需要设计和进行实验研究，从中风患者中收集纵向数据，处理收集的数据，并开发机器学习算法，以识别能够准确监测认知功能并提前预测其失败的强大生物标志物。您将在谢菲尔德大学的脑机接口小组工作。我们的团队是独特的多学科和多样化的，整合了跨越生物工程，数据科学和临床，实验和计算神经科学的卓越研究计划。我们团队的研究愿景是开发脑导向疗法和工具，以改善人类的认知和身体表现。您将参与一个高度跨学科的项目，并与系统工程师，计算机科学家，神经科学家和医生进行互动和合作。您将在我们的团队中接受广泛的培训，包括EEG数据记录和处理，统计分析，生物数据的机器学习，研究和数据管理中的伦理，以及科学写作和演示。您也有机会获得商业化生物医学设备的独特技能。