MRI: Acquisition of an EEG-NIRs Bimodal Neural Signal Recording System

摘要：这项主要研究仪器（MRI）资助项目支持获取一种神经记录系统，该系统将脑电图（EEG）和近红外光谱（NIRs）集成在一起，提供双峰数据收集和分析。该系统的获得使生物医学和电气工程、计算机科学和神经心理学领域的研究人员能够共同合作，开展多学科研究和教育活动。提出了多个研究项目，以探讨脑电图-近红外双峰信号采集的基本挑战及其在医疗保健，医疗仪器和脑机接口中的广泛应用。研究生和本科生，包括少数民族，将参与拟议的研究项目。威德纳大学位于宾夕法尼亚州切斯特，那里有大量来自代表性不足的群体的人口，在过去的几十年里经历了严重的经济萧条。研究人员将与当地的两个教育项目合作，让一群超过95%的中学生参与其中，这些学生来自代表性不足的群体，激励他们在科学、技术、工程和数学（STEM）领域接受大学教育。该授权的目标是获得BrainVision EEG-NIRs双峰神经记录系统，该系统将32通道BrainVision有源通道放大器EEG和32通道NIRScout NIRs集成在一起。EEG被广泛用于神经活动的测量，其时间分辨率高，但空间特异性差。近红外光谱（NIRs）是一种非侵入性光学成像技术，用于测量人类皮层浅层大脑活动期间脱氧血红蛋白和含氧血红蛋白的浓度变化。近红外光谱可以测量与神经活动相关的代谢变化，其空间分辨率高于脑电图，这促使人们将近红外光谱与脑电图结合起来，探索彼此的互补信息。该双峰系统将用于开发双峰脑机接口、Chiari畸形手术后症状复发的计算机辅助诊断和治疗、运动相关脑震荡的早期诊断。近红外光谱装置还将用于定量评估一种用于靶向脑低温的新型冷却装置，并开发新的技术来测量一种新型血液透析机的血流分布。该系统和相关研究将促进我们对脑电图和近红外光谱结合在不同应用领域的潜在好处的理解。对单个模态和组合模态下的数据特征的研究将产生数据复杂性、特征平稳性和模态间干扰的新发现。将开发新的数据处理方法来执行工件去除、信息融合、自适应学习和分类、功能网络映射和动态建模。该研究项目将产生定量结果来证明预期的改进，并为未来多模态神经成像系统的发展提供一个可扩展的平台。

Abstract:This Major Research Instrumentation (MRI) grant supports the acquisition of a neural recording system that integrates electroencephalography (EEG) and near-infrared spectroscopy (NIRs) together to provide bimodal data collection and analysis. The acquisition of this system enables faculty researchers across Biomedical & Electrical Engineering, Computer Science, and Neuropsychology fields to work together and develop multidisciplinary research and educational activities. Multiple research projects are proposed to investigate fundamental challenges in EEG-NIRs bimodal signal acquisition and its widespread applications in healthcare, medical instrument, and brain computer interface. Graduate and undergraduate students, including minorities, will participate in the proposed research projects. Widener University is located in Chester, Pennsylvania, which has a large population from underrepresented groups and undergoing significant economic depression in the last decades. The researchers will work with two local educational programs to involve a group of middle/high school students over 95% of whom are from underrepresented groups, inspiring them to pursue college education in science, technology, engineering, and mathematics (STEM) fields.The goal of this grant is to acquire a BrainVision EEG-NIRs bimodal neural recording system that integrates a 32-channel BrainVision active channel amplifier EEG and a 32-channel NIRScout NIRs together. EEG is widely used to measure neural activity with a high temporal resolution but poor spatial specificity. NIRs is a noninvasive optical imaging technique that measures concentration changes of deoxygenated hemoglobin and oxygenated hemoglobin during brain activity in superficial layers of the human cortex. NIRs can measure metabolic changes associated with the neural activity with a higher spatial resolution than EEG, and this has prompted the effort to combine NIRs and EEG together to explore complementary information to each other. The bimodal system will be use for the development of a bimodal brain computer interface, computer-aided diagnosis and treatment of symptom recurrence after Chiari malformation surgery, and early diagnosis of sports-related concussion. The NIRs device will also be used to quantitatively evaluate a novel cooling device for targeted brain hypothermia, and to develop novel techniques to measure the flow distribution of a new hemodialysis machine. The system and associated research will advance our understanding of potential benefits from the combination of EEG and NIRs on different application fields. The study of data characteristics under the individual and combined modalities will generate new findings of data complexity, feature stationarity, and inter-modality interference. Novel data processing methods will be developed to perform artifacts removal, information fusion, adaptive learning and classification, and functional network mapping and dynamic modeling. The research projects will generate quantitative results to justify the expected improvement, and provide an extensible platform for future development of multimodal neuroimaging systems.