Decoding human mental states by whole-head EEG+fNIRS during category fluency task performance

Objective: Concurrent scalp electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), which we refer to as EEG+fNIRS, promises greater accuracy than the individual modalities while remaining nearly as convenient as EEG. We sought to quantify the hybrid system's ability to decode mental states and compare it with unimodal systems. Approach: We recorded from healthy volunteers taking the category fluency test and applied machine learning techniques to the data. Main results: EEG+fNIRS's decoding accuracy was greater than that of its subsystems, partly due to the new type of neurovascular features made available by hybrid data. Significance: Availability of an accurate and practical decoding method has potential implications for medical diagnosis, brain-computer interface design, and neuroergonomics

Blood Banking in Living Droplets

Blood banking has a broad public health impact influencing millions of lives daily. It could potentially benefit from emerging biopreservation technologies. However, although vitrification has shown advantages over traditional cryopreservation techniques, it has not been incorporated into transfusion medicine mainly due to throughput challenges. Here, we present a scalable method that can vitrify red blood cells in microdroplets. This approach enables the vitrification of large volumes of blood in a short amount of time, and makes it a viable and scalable biotechnology tool for blood cryopreservation.National Institutes of Health (U.S.) (NIH R21 EB007707)Wallace H. Coulter FoundationUnited States. Army Medical Research and Materiel Command (Acquisition Activity Cooperative Agreement RO1 A1081534)Center for Integration of Medicine and Innovative TechnologyUnited States. Army Medical Research and Materiel Command (Acquisition Activity Cooperative Agreement R21 AI087107)United States. Army. Telemedicine & Advanced Technology Research Cente

Investigation of Neurovascular Coupling with Multimodal Imaging System: a fNIRS-EEG Study

Technological advances in functional neuroimaging contributed to understanding of the neurovascular coupling in humans over the years, however, the temporal and spatial relationship between large-scale neural oscillations and hemodynamic changes is still not completely understood during the different states and different disease states of the human cortex. It is also still unclear that what type of large-scale neural oscillations that mostly drive to the hemodynamic signal. There has been a need for novel tools and methods in neuroimaging to study neurovascular coupling. This thesis focused on the development of simultaneous Functional Near-Infrared Spectroscopy and Electroencephalography system (simultaneous fNIRS+EEG) that can be used for the investigation of neurovascular coupling over the whole head. The simultaneous fNIRS+EEG system is then applied to the resting state studies in healthy adult subjects. The results of these resting state experiments are presented. Our finding shows that the EEG signals at various frequencies tend to drive hemoglobin concentration changes with a typical time delay during the resting states. As side studies, the simultaneous fNIRS+EEG is applied to the cognitive task and artifact experiments to evaluate the suitability of the developed system. The results of these task and artifacts experiments are also presented. Our goal was to characterize the basic phenomena through practical, noninvasive methods in order to facilitate the study of diseases known to affect neurovascular coupling, such as traumatic brain injury.Biomedical Engineering, Department o

Hybrid EEG-fNIRS Asynchronous Brain-Computer Interface for Multiple Motor Tasks.

Non-invasive Brain-Computer Interfaces (BCI) have demonstrated great promise for neuroprosthetics and assistive devices. Here we aim to investigate methods to combine Electroencephalography (EEG) and functional Near-Infrared Spectroscopy (fNIRS) in an asynchronous Sensory Motor rhythm (SMR)-based BCI. We attempted to classify 4 different executed movements, namely, Right-Arm-Left-Arm-Right-Hand-Left-Hand tasks. Previous studies demonstrated the benefit of EEG-fNIRS combination. However, since normally fNIRS hemodynamic response shows a long delay, we investigated new features, involving slope indicators, in order to immediately detect changes in the signals. Moreover, Common Spatial Patterns (CSPs) have been applied to both EEG and fNIRS signals. 15 healthy subjects took part in the experiments and since 25 trials per class were available, CSPs have been regularized with information from the entire population of participants and optimized using genetic algorithms. The different features have been compared in terms of performance and the dynamic accuracy over trials shows that the introduced methods diminish the fNIRS delay in the detection of changes

EEG, fNIRS, and HYB Rest-Task classification accuracy [%] for a 1 s moving window with 50% overlap (top: EEG, middle: fNIRS, bottom: HYB).

<p>The colored lines represent the different subjects and the black thick line is the average accuracy. The first black vertical line (at time 0 s) is the beginning of the task, while the second one (at time 6 s) is the end of it.</p

<i>μ</i> power, HbO averages, and HbO slopes (top: EEG, middle: HbO-average, bottom: HbO-slope) scalp plots along the trial (values are averaged over all subjects).

<p>The values are computed every 1 s with 50% overlap and averaged over the time interval shown on top (e.g. [−3, −1]: values averaged between −3 s and −1 s). The task, as shown by the light blue rectangle at the bottom, starts at 0 s and ends at 6 s.</p

EEG, fNIRS, and HYB Right-Left classification accuracy [%] for a 1 s moving window with 50% overlap (top: EEG, middle: fNIRS, bottom: HYB).

<p>The colored lines represent the different subjects and the black thick line is the average accuracy. The first black vertical line (at time 0 s) is the beginning of the task, while the second one (at time 6 s) is the end of it.</p

EEG, fNIRS, and HYB Arm-Hand classification accuracy [%] for a 1 s moving window with 50% overlap (top: EEG, middle: fNIRS, bottom: HYB).

<p>The colored lines represent the different subjects and the black thick line is the average accuracy. The first black vertical line (at time 0 s) is the beginning of the task, while the second one (at time 6 s) is the end of it.</p

Lensless imaging for point-of-care testing

We show a platform that merges a microfluidic chip with lensless imaging for CD4[superscript +] T-lymphocyte counting at resource-limited settings. To capture CD4[superscript +] T lymphocytes, anti-CD4[superscript +] antibody was immobilized on a microfluidic chip. The captured cells were detected by a charge coupled device (CCD) sensor using lensless shadow imaging techniques. Gray scale shadow images of captured cells on the chip (24 mm times 4 mm times 50 mum) were enumerated in three seconds using an automatic cell counting software. The device achieved 70.2 plusmn 6.5% capture efficiency, 88.8 plusmn 5.4% capture specificity for CD4[superscript +] T-lymphocytes, 96 plusmn 1.6% CCD efficiency, and 83.5 plusmn 2.4% overall platform performance (n = 3D 9 devices). This integrated platform has potential for point-of-care testing (POCT) to rapidly capture, image and count specific cell types from unprocessed whole blood.National Institute of General Medical Sciences (U.S.) (grants RO1 AI081534, RR016482, and AI060354