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. 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 unmixing matrix leading to an orthogonal extended infomax algorithm (OgExtInf). Computational performance of OgExtInf is compared with two fast ICA algorithms: the popular FastICA and Picard, a L-BFGS algorithm belonging to the family of quasi-Newton methods. Our results demonstrate superior performance of the proposed method on small-size EEG data sets as used for example in online EEG processing systems, such as brain-computer interfaces or clinical systems for spike and seizure detection.

翻译：独立成分分析（ICA）中的扩展Infomax算法能够分离亚高斯与超高斯信号，但由于采用随机梯度优化，收敛速度较慢。本文提出一种改进的扩展Infomax算法，其收敛速度显著加快。通过将扩展Infomax的自然梯度学习规则替换为基于全乘法正交群的解混矩阵更新方案，从而构建正交扩展Infomax算法（OgExtInf），实现了加速收敛。我们将OgExtInf的计算性能与两种快速ICA算法——流行的FastICA算法及属于拟牛顿法族的L-BFGS算法Picard——进行了比较。结果表明，在脑机接口或用于尖波与癫痫发作检测的临床系统等在线脑电图处理系统中使用的小型脑电图数据集上，所提方法展现出优越的性能。