Iyad Obeid, Ph.D. - US grants

I. Obeid
0846351


Brain Machine Interfaces are an emerging technology whose purpose is to allow amputees and spinal cord injury patients to control a prosthetic limb using signals derived from the brain. The proposed work will create the means for investigating how the natural plasticity of the human brain can be exploited to innovate more efficient (and thus more easily made portable) Brain Machine Interface instrumentation. This will be achieved through the development of a new simulator that simultaneously models neural adaptation in reaching tasks, a prosthetic limb, and Brain Machine Interface hardware that connects the two. A key element of this simulator will be the ability to use real-time visual and proprioceptive feedback from the modeled arm to train the virtual brain cells and thus, over time, improve the accuracy with which the brain can control the prosthesis.

The proposed work will be accomplished using three Research Aims (1) Design and implement a simulation platform that models adaptive motor control of a human limb in three-space. (2) Design and implement an instrumentation testbed capable of realizing entire families of Brain Machine Interface data acquisition subsystems and systematically manipulating their parameters. The system will handle up to 50 x 50 channels and will collect performance statistics that quantify how and where information is lost or altered in the data pathway. (3) Quantify how BMI performance can be expected to degrade in response to errors in spike detection, spike sorting, and wireless neural data transmission.

The project will guide the development of next generation Brain Machine Interface systems, especially the implantable and wireless systems that remain an obstacle to Brain Machine Interfaces becoming realistic therapeutic devices. The project will provide training to students to conduct neural engineering research and also for developing new hands-on instructional materials for teaching neural engineering at the graduate level. The research will advance techniques for modeling functional ensembles of cortical neurons while also creating pedagogical tools for conducting neural engineering education and research at Temple University and beyond.

1202430, Obeid

Intellectual Merit
Rapid advances in engineering are facilitating an enhanced understanding of biology and providing novel transformative solutions to biomedical problems. The NEBEC will provide a multidisciplinary forum for dissemination of these advances in biomedical engineering at all levels, and will facilitate the sharing of knowledge across interdisciplinary barriers. The conference serves as an open forum for discussion of new directions, ideas and approaches in research and education. Furthermore, the conference provides a setting to reinforce existing contacts and to establishing new collaborations.
To support these goals, sessions will be organized covering a broad range of topics related (but not limited) to:
- Stem Cells
- Instrumentation
- Imaging
- Biomedical Design
- Biomechanics
The NEBEC has traditionally served as a platform for students to present their research and to obtain constructive feedback. This conference also provides an opportunity for students to discuss potential career choices with individuals from academia, government, and industry.


Broader Impact
The 38th NEBEC will have a broad impact for the Northeast bioengineering community and for engineering education. The knowledge shared at the conference by researchers from over 40 institutions in the Northeast will be captured electronically and broadly disseminated via abstract books, USB thumb drives, and the IEEE website. The conference?s emphasis on students, with multiple opportunities for them to present their work and discuss current obstacles to progress with leading researchers in an intimate setting, will better prepare them for careers in both research and industry. One of the main goals of the conference is to nurture exceptional students and to help develop an environment to support future leaders in the field. In addition, the exposure to research in diverse fields will help spawn innovations and create ground breaking bioengineering technologies and bench benefit the society at large.

Despite significant progress in Neural Engineering in recent years, overall progress in the field does not appear to have been commensurate with the scope of investment. It has become evident that the field can benefit from a new paradigm in research and development that focuses on robust algorithm development. The purpose of this planning grant is to develop community support for the creation of the Neural Engineering Data Consortium (NEDC) at Temple University. The NEDC is focusing the attention of the research community on a progression of research questions as well as generating and curating massive data sets to be used in addressing those questions. Under this award, we are conducting an extensive series of workshops and site visits to build consensus amongst various research groups from academia and industry. Guided by this site research, we are developing an operational model for the NEDC that is simultaneously relevant to academic research, industry R&D, and government funding agencies. The NEDC is structured to significantly accelerate research progress and provide resources that promote the development of more robust technology. We are also broadening participation in neural engineering research by making data available to research groups who have significant signal processing expertise but who lack capacity for data generation. The impact of funded research is thus improved by enticing more participation in focused evaluation tasks. Finally, the NEDC is accelerating the transfer of technology to the healthcare marketplace, where it can directly enhance quality of life for a variety of patients.

Electroencephalograms (EEGs) are the most pervasive neural diagnostic tool; they require a highly trained neurologist to analyze them. Long-term EEG monitoring, used to diagnose rare events such as epileptic seizures, is difficult or impossible to scan manually without decision software support. Development of portable standalone diagnostic tools, which can address emerging markets such as contact sports, is highly difficult; and smaller or stand-alone medical practices often lack expertise to conduct diagnostics on-site and accordingly lose revenue. At present, innovation in commercial clinical decision support tools is minimal, whereas the global market for rapidly diagnosing brain-related injury and disease is growing. The proposed AutoEEGTM is a software tool that enhances productivity by auto-scanning EEG signals and flagging sections of the signal that need further review by a clinician. The proposed tool reduces the amount of data needing manual review by two orders of magnitude, offering substantial productivity gains in a clinical setting.

The proposed clinical decision support tool is based on proven, advanced, deep learning technology. It reduces time to diagnosis, reduces error and is sufficiently lightweight to run on portable standalone platforms. This technology is able to identify EEG events in the signal and subsequently to provide a report that summarizes its findings based on the event detected. The transcribed EEG signals can be viewed from any portable computing device. It also has the ability to learn from data, helping in future decision making, providing real-time feedback to aid in diagnosis, and, for patients undergoing long-term monitoring, creating an alert when abnormal signals are identified. This market-leading product will (1) Enable clinical neurologists employing a volume-based business mode to decrease the time spent analyzing an EEG and thereby increase billing; (2) Allow pharmas to assess changes quantitatively in neural activation during clinical trials; (3) Allow neurologists to order and bill for substantially more long-term monitoring tests based on this proven decision support tool; and (4) Add value to the commodity EEG headsets currently entering the market by providing meaningful, real-time signal analysis. This research project has two key components: (1) a detailed analysis of the market to understand various opportunities such as licensing to equipment manufacturers and off-line analysis for contract research organizations; and (2) usability design and engineering to understand the analytics and user interface issues that bring most value to potential users such as clinicians and primary care physicians. The outcomes of this research will be used to harden the technology and guide integration into existing EEG products.

This project, developing a digital imaging system, aims to automatically characterize an enormous archive of digital images from a pathology lab. Based on open source programming language packages and using deep learning technologies, the effort entails developing software that will automatically annotate and classify these images. Thus the system consists of an annotated archive tool for high performance digital pathology involving digital images from pathology slides produced in clinical operations. This tool, presently unavailable, enables observation, annotation, and classification of images from tissues in pathology slides in order to create a very large data base that may be analyzed with algorithms that are designed to process and interpret the image data. By applying state of the art machine learning, the effort is expected to generate a sustainable facility to rapidly collect large amounts of data automatically. This facility enables deep learning systems to systematically address many operational challenges, such as ingestion of large, complex images.

Broader Impacts:
The instrumentation provides a useful technology capability. The work builds on the researchers' history of providing unencumbered resources for fields including human language technology and neuroscience. Several large, comprehensive databases of pathology slides will be released in an unencumbered manner; no comparable databases currently exist in terms of the quantity of data proposed. The urban setting of the project, as well as the diverse nature of the institution's client population, make it ideal for collecting this type of clinical data. A new generation of healthcare professionals will be trained using these resources to validate their knowledge in the longer term.

The broader impact/commercial potential of this PFI project is that it will lead to improved clinical outcomes for neurological patients in intensive care units (ICUs). Although the data acquired using continuous electroencephalography (EEG) in the ICU is inexpensive to record and a rich source of information for guiding clinical decision making, it is often not used because it takes too long to be analyzed manually. The proposed technology will be capable of evaluating EEGs in real-time in order to alert doctors when clinically relevant events such as seizures occur. This will improve patient outcomes by allowing doctors to intervene with medications in a timelier and more precise fashion. This work will also have the broader impact of improving science's understanding of the fundamentals of how machine learning can be applied specifically to neural signal processing, which is currently a poorly understood area.

The proposed project will enable and accelerate the commercialization of software technology that detects seizures and abnormal brain activity in Intensive Care Unit patients. This will be accomplished with three main tasks. In the first task, the existing seizure detection software, which currently works offline, will be converted to work in real-time with a target latency of 20 seconds to detect a seizure. This will be accomplished through intelligent memory handling and by developing a low-latency, highly optimized post-processing algorithm. The second task will strengthen the existing seizure detection code to operate at clinically acceptable levels of sensitivity and false alarm rates. This will be achieved by retraining our algorithms on a significantly more diverse and complex EEG database in order to expose the software to as many variations of seizure presentation as possible. In the third and final task, extensive software testing will be conducted in order to optimize the machine learning configuration that maximizes the gains achieved in Tasks 1 and 2.

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.

This project is concerned with research on very large digital images. Although image recognition tools are now commonplace for standard definition images, there is a lack of tools and techniques for analyzing very high resolution images (hundreds of millions of pixels per image). There are many important applications that would be well-served by the ability to automatically search such images, especially in cases where the properties of the search target aren't even fully known. Research of this nature is complicated by the fact that images of this size do not lend themselves well to the type of software tools that have been successful in standard-resolution image processing techniques such as handwriting or facial recognition. Data quantity alone is a major limitation; training a robust image recognition tool may require tens of thousands of images; at 100 million pixels per image, the sheer quantity of data becomes an issue for storing, sharing, and processing. New innovations will be required in machine learning, data provenance and warehousing, cloud computing, and image compression, all of which will serve the national interest.

The specific purpose of this project is to (a) build a community of stakeholders who have vested interests in innovations in high resolution image processing and (b) create a roadmap for future research. Specifically, the researchers will reach out to relevant groups across academia, industry, and not-for-profit consortia with the goal of building a robust community with diverse expectations. This community will engage in a series of workshops to define the goals and scope of a high definition image processing consortium. The workshops will seek to define data standards, image processing goals, standardized data sets, and best practices for sharing and computing images at the petabyte scale. These findings will be leveraged into a future project whose goal will be to actually build the tools and perform the research that has been road-mapped by this project.

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.