首页|Reports from Queen’s University Provide New Insights into Computational Intelligence (Spatio-temporal Eeg Representation Learning On Riemannian Manifold and Euclidean Space)
Reports from Queen’s University Provide New Insights into Computational Intelligence (Spatio-temporal Eeg Representation Learning On Riemannian Manifold and Euclidean Space)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - A new study on Machine Learning - Computational Intelligence is now available.According to news reporting originating from Kingston, Canada, by NewsRx correspondents, researchstated, “We present a novel deep neural architecture for learning electroencephalogram (EEG). To learnthe spatial information, our model first obtains the Riemannian mean and distance from spatial covariancematrices (SCMs) on a Riemannian manifold.”
KingstonCanadaNorth and Central AmericaComputational IntelligenceMachine LearningQueen’s University
NETL
NSTL
