The Neural Engineering Data Consortium (NEDC) launched to focus the research community on high-impact, common-interest neural engineering research questions. The NEDC is a central community resource with the goals of determining key research problems as well as being an independent arbiter for algorithm testing and evaluation. Critically, the NEDC will also generate and curate massive data sets to support statistically significant data-driven solutions to those problems. The meticulous and robust creation of these freely available datasets is what makes NEDC unique.
Building the largest set of widely available clinical electroencephalography (EEG) data also created the core of future software developments to be used in the medical field. The EEG is an exquisite temporal sensitive electrophysiological technique that medical professionals use to record brain activity. Furthermore, the main utility of EEG is in the evaluation of dynamic cerebral functioning. Our organization has utilized hospital data to create software that detects seizures, artifact events, and brain disorders. This software, among other projects, is developed by Temple students.
NEDC’s mission is to cultivate an environment where all students can perform leading-edge research in order to grow to become disciplined and self-motivated engineers and scientists. Our lab is a place where students come together to learn applicable engineering skills such as software development, teamwork, and communication. If you’d like to know more about our organization, don’t hesitate to contact us.
NEDC's first dataset is the Temple University Hospital EEG Corpus (TUH EEG), which is the world's largest publicly available database of clinical EEG data. Our approach has been successfully applied in other data-intensive fields such as pathology and clinical signal processing. Current active projects include:
- TUH EEG | The corpus consists of over 20,000 EEGs dating back to 2002. In addition to the raw signal data, metadata about the subjects is available, including medical conditions and treatments. Physician interpretations of the data are also included making this an invaluable resource for machine learning experiments.
- NSF DPATH | The goal of this project is to build a large open-source database of pathology slides that can be used to train high performance deep learning models. The outcome will be a sustainable facility to rapidly collect large amounts of automatically annotated whole slide images.
- NSF CCRI DPATH | This proposal supports a community planning effort for digital pathology focused on planning the data and evaluation resources that will enable high performance, automated interpretation of pathology images using machine learning. We have created a diverse community of digital pathology researchers that contributes data, software tools, and expertise with the communal goal of improving healthcare outcomes through enhanced analysis of biological tissue.
- NSF PFI TT | In this project, we have developed a real-time version of our state of the art automated seizure detection software. We have also improved performance of the system. The overall goal is to develop technology that is marketable.
- IEEE SPMB | Envisioned to be a regional forum that would bring together researchers from the disciplines of signal processing, bioengeering and medicine to collaborate on problems of scale in bioengineering. The NEDC has hosted the IEEE Signal Processing in Medicine and Biology Symposium at Temple University since 2014.
- NSF STEM DIVE Challenge | The NEDC participated in the NSF STEM DIVE Challenge to highlight the promotion of STEM education as well as the professional development of our students.
- Neureka 2020 Epilepsy Challenge | As part of the 10th anniversary of IEEE SPMB, the NEDC, in collaboration with Novela Neurotech and NeuroTechX, hosted the Neureka 2020 Epilepsy Challenge.
- Women in Engineering Seminar | Katherine Degerberg visited Temple on January 24, 2020 to present a seminar on engineering career management. Career development issues in the current engineering field was discussed in depth.