Orthogonal extended infomax algorithm

IF 3.7 3区 医学 Q2 ENGINEERING, BIOMEDICAL Journal of neural engineering Pub Date : 2024-04-09 DOI:10.1088/1741-2552/ad38db
Nicole Ille
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Abstract

Objective. The extended infomax algorithm for independent component analysis (ICA) can separate sub- and super-Gaussian signals but converges slowly as it uses stochastic gradient optimization. In this paper, an improved extended infomax algorithm is presented that converges much faster. Approach. Accelerated convergence is achieved by replacing the natural gradient learning rule of extended infomax by a fully-multiplicative orthogonal-group based update scheme of the ICA unmixing matrix, leading to an orthogonal extended infomax algorithm (OgExtInf). The computational performance of OgExtInf was compared with original extended infomax and with two fast ICA algorithms: the popular FastICA and Picard, a preconditioned limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) algorithm belonging to the family of quasi-Newton methods. Main results. OgExtInf converges much faster than original extended infomax. For small-size electroencephalogram (EEG) data segments, as used for example in online EEG processing, OgExtInf is also faster than FastICA and Picard. Significance. OgExtInf may be useful for fast and reliable ICA, e.g. in online systems for epileptic spike and seizure detection or brain-computer interfaces.
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正交扩展 infomax 算法
目的。用于独立分量分析(ICA)的扩展 infomax 算法可以分离亚高斯和超高斯信号,但由于使用随机梯度优化,收敛速度较慢。本文提出了一种改进的扩展 infomax 算法,收敛速度更快。方法。通过用基于正交组的 ICA 非混合矩阵全乘法更新方案取代扩展 infomax 的自然梯度学习规则,从而实现加速收敛,这就是正交扩展 infomax 算法(OgExtInf)。OgExtInf 的计算性能与原始扩展 infomax 算法和两种快速 ICA 算法进行了比较:流行的 FastICA 算法和 Picard 算法,后者是一种属于准牛顿方法系列的预条件有限内存 Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) 算法。主要结果OgExtInf 的收敛速度比原始扩展 infomax 快得多。对于在线脑电图处理中使用的小尺寸脑电图(EEG)数据段,OgExtInf 也比 FastICA 和 Picard 更快。意义重大。OgExtInf 可用于快速可靠的 ICA,例如用于癫痫尖峰和癫痫发作检测或脑机接口的在线系统。
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来源期刊
Journal of neural engineering
Journal of neural engineering 工程技术-工程:生物医学
CiteScore
7.80
自引率
12.50%
发文量
319
审稿时长
4.2 months
期刊介绍: The goal of Journal of Neural Engineering (JNE) is to act as a forum for the interdisciplinary field of neural engineering where neuroscientists, neurobiologists and engineers can publish their work in one periodical that bridges the gap between neuroscience and engineering. The journal publishes articles in the field of neural engineering at the molecular, cellular and systems levels. The scope of the journal encompasses experimental, computational, theoretical, clinical and applied aspects of: Innovative neurotechnology; Brain-machine (computer) interface; Neural interfacing; Bioelectronic medicines; Neuromodulation; Neural prostheses; Neural control; Neuro-rehabilitation; Neurorobotics; Optical neural engineering; Neural circuits: artificial & biological; Neuromorphic engineering; Neural tissue regeneration; Neural signal processing; Theoretical and computational neuroscience; Systems neuroscience; Translational neuroscience; Neuroimaging.
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