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.
