Inequalty constraints are introduced to a normalized minimum-L1-norm estimator, which gives a sparse solution of the biomagnetic inverse problem.

Inequality constraints are introduced to a normalized minimum-L/sub 1/-norm estimator, which gives a sparse solution of the biomagnetic inverse problem.

Inequality constraints are introduced to a normalized minimum-L/sub 1/-norm estimator, which gives a sparse solution of the biomagnetic inverse problem.

Computer simulation and phantom-data analysis show how the solution is improved by the constraints with a moderate tolerance of the biomagnetic inverse ...

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A robust reconstruction of sparse biomagnetic sources ; 巻: 44 ; 号: 8 ; 開始ページ: 720 ; 終了ページ: 726 ; 記述言語: 英語 ...

Jul 31, 2019 · We introduce a novel robust empirical Bayesian algorithm that enables better reconstruction of distributed brain source activity with two key ideas.

Yoichi Okabe's 48 research works with 696 citations, including: A robust reconstruction of sparse biomagnetic sources.

We examine the performance of “Space-Time Spar- sity” (STS) penalized reconstruction of brain activity from magneto-/electroencephalographic (MEG/EEG) ...

We introduced the Champagne algorithm, a novel Bayesian inference algorithm that has shown tremendous success in M/EEG source reconstruction.