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Considering that human RT is the reflection of brain dynamics preference (BDP) rather than a single regression output of EEG signals, we propose a novel channel-reliability-aware ranking (CArank) model for the multichannel ranking problem. CArank learns from BDPs using EEG data robustly and aims at preserving the ordering corresponding to RTs. In particular, we introduce a transition matrix to characterize the reliability of each channel used in the EEG data, which helps in learning with BDPs only from informative EEG channels. To handle large-scale EEG signals, we propose a stochastic-generalized expectation maximum (SGEM) algorithm to update CArank in an online fashion. Comprehensive empirical analysis on EEG signals from 40 participants shows that our CArank achieves substantial improvements in reliability while simultaneously detecting noisy or less informative EEG channels.<\/jats:p>","DOI":"10.1162\/neco_a_01293","type":"journal-article","created":{"date-parts":[[2020,6,10]],"date-time":"2020-06-10T22:41:18Z","timestamp":1591828878000},"page":"1499-1530","source":"Crossref","is-referenced-by-count":4,"title":["Stochastic Multichannel Ranking with Brain Dynamics Preferences"],"prefix":"10.1162","volume":"32","author":[{"given":"Yuangang","family":"Pan","sequence":"first","affiliation":[{"name":"Centre for Artificial Intelligence, University of Technology Sydney, Sydney 2007, Australia"}]},{"given":"Ivor W.","family":"Tsang","sequence":"additional","affiliation":[{"name":"Centre for Artificial Intelligence, University of Technology Sydney, Sydney 2007, Australia"}]},{"given":"Avinash K.","family":"Singh","sequence":"additional","affiliation":[{"name":"Centre for Artificial Intelligence, University of Technology Sydney, Sydney 2007, Australia"}]},{"given":"Chin-Teng","family":"Lin","sequence":"additional","affiliation":[{"name":"Centre for Artificial Intelligence, University of Technology Sydney, Sydney 2007, Australia"}]},{"given":"Masashi","family":"Sugiyama","sequence":"additional","affiliation":[{"name":"Center for Advanced Intelligence Project, RIKEN, Tokyo 103-0027, and Graduate School of Frontier Sciences, University of Tokyo, Tokyo 2777-8563, Japan"}]}],"member":"281","reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1016\/j.sleh.2016.11.005"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2017.07.011"},{"issue":"10","key":"B3","volume":"11","author":"Basak D.","year":"2007","journal-title":"Neural Information Processing\u2013Letters and Reviews"},{"key":"B4","unstructured":"Becirovic, E. 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