Allerton 2015 Paper Abstract

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Paper ThA4.5

Grover, Pulkit (Carnegie Mellon University), Weldon, Jeffrey (Carnegie Mellon University), Kelly, Shawn (Carnegie Mellon University), Venkatesh, Praveen (Carnegie Mellon University), Jeong, Haewon (Carnegie Mellon University)

An information theoretic technique for harnessing attenuation of high spatial frequencies to design ultra-high-density EEG

Scheduled for presentation during the Regular Session "Sensor Networks I" (ThA4), Thursday, October 1, 2015, 09:50−10:10, Pine

53rd Annual Allerton Conference on Communication, Control, and Computing, Sept 29-Oct 2, 2015, Allerton Park and Retreat Center, Monticello, IL, USA

This information is tentative and subject to change. Compiled on November 19, 2019

Keywords Information Theory, Cyber-Physical Systems, Sensor Networks in Communications

Abstract

It is widely believed in the clinical and biosciences community that Electroencephalography (EEG) is fundamentally limited in the spatial resolution achieved using a few hundred electrodes. This belief rests on the well known decay of high-spatial frequencies as the signal passes from the brain surface to the scalp surface. These high spatial frequencies carry high spatial resolution information about the source. However, recent experimental work as well as our theoretical and numerical analyses strongly suggest that EEG's resolution could be improved significantly through increased electrode density despite this decay. Somewhat counterintuitively, instead of viewing this decay of spatial frequencies as a detriment to signal quality (which it is), in this work we propose an information-theoretic strategy to harness this decay to reduce circuit area and energy needed for high-resolution signal acquisition. This is made possible by the observation that this spatial-low-pass filtering of the signal as it passes from the brain to the scalp induces large spatial correlations that can be exploited information-theoretically. The proposed techniques are shown in idealized head models to reduce requirements on energy required for sensing by 3x. These results are being applied towards an ongoing project on developing the "Neural Web," a 10,000 electrode portable EEG system at CMU.

 

 

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