Event
CCSP Seminar: Sina Miran, Dec. 7 (Thursday, 5 PM)
Thursday, December 7, 2017
5:00 p.m.-6:30 p.m.
AVW 2168 (ISR Conference Room)
Ajaykrishnan Nageswaran
301 405 3661
ajayk@umd.edu
http://www.ece.umd.edu/seminars/ccsp/
In this talk, we will develop an algorithmic pipeline for real-time decoding of the attentional state. Our proposed framework consists of three main modules: 1) Real-time and robust estimation of encoding or decoding coefficients, achieved by sparse adaptive filtering, 2) Extracting reliable markers of the attentional state, and thereby generalizing the widely-used correlation-based measures thereof, and 3) Devising a near real-time state-space estimator that translates the noisy and variable attention markers to robust and reliable estimates of the attentional state with minimal delay. Our proposed algorithms integrate various techniques including forgetting factor-based adaptive filtering, L1-regularization, forward-backward splitting algorithms, fixed-lag smoothing, and expectation maximization. We validate the performance of our proposed framework using comprehensive simulations as well as application to experimentally acquired M/EEG data. Our results reveal that the proposed real-time algorithms perform nearly as accurate as the existing state-of-the-art offline techniques, while providing a significant degree of adaptivity, statistical robustness, and computational savings.