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Improving the Acquisition of EEG Signals
ECE Assistant Professor Aatmesh Shrivastava & Associate Professor Marvin Onabajo were awarded $500K from NSF's Computer Systems Research program to develop an analog computing based feature extraction system-on-a-chip with dry skin-electrode contact interface circuits for wireless EEG sensing.
Abstract Source: NSF
Electroencephalography (EEG) is used for the analysis of many neurological disorders such as epilepsy, sleep disorders, encephalopathy, and coma. Perpetual monitoring and processing of EEG signals helps the treatment inside and outside of the hospital environment. However, the power consumption involved in signal acquisition, processing, and communication has remained high for wearable wireless EEG devices. This research will develop an ultra-low power (ULP) EEG acquisition and processing system-on-a-chip (SoC) using a new analog computing technique instead of conventional digital processing system. This SoC will be able to identify a seizure event in the analog domain, incorporating learning and continuous signal processing.
The analog processing and feature extraction capability will be realized with precise amplifier and filter design techniques to achieve stabilities down to 10s of parts-per-million (ppm)/degree for gains and filter cutoff frequencies. The feature extraction method will measure the power levels in various EEG spectral bands by utilizing these precise analog amplifiers and filters to detect the onset of seizures. Power level threshold setting and simple vector model based training methods will be implemented on-chip for seizure characterization and detection. A capacitance cancellation scheme with online calibration will be devised to acquire EEG signals with higher input impedance for brain-computer interfaces requiring long-term monitoring.
The results from this research will improve the acquisition of EEG signals for predicting the onset of seizures with small portable devices, which impacts 2% of the world's population. The proposed SoC will be particularly beneficial in future miniaturized wearable devices for continuous EEG signal monitoring outside of hospital environments. Knowledge obtained from this project will be integrated into graduate and undergraduate education; results from the project will be disseminated through journal articles and conference presentations. Undergraduate researchers and high school interns will be involved and trained in the project.
Publicly shared data collected as part of this research will be deposited into Northeastern University's Digital Repository Service (DRS), which is a digital archive developed and maintained by the library (https://repository.library.northeastern.edu). It provides security for the files it stores, as well as access management controls and support for various metadata standards to help ensure that data is as accessible and usable in the present and the future. All project participants will have access to a project management database stored on local servers with design and simulation data. The project data will be maintained for at least 3 years after the conclusion of the 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.