This paper is focused on the experimental approach to explore the potential of independent component analysis (ICA) in the context of motor imagery (MI)-based brain-computer interface (BCI). We presented a simple and efficient algorithmic framework of ICA-based MI BCI (ICA-MIBCI) for the evaluation of four classical ICA algorithms (Infomax, FastICA, Jade, and Sobi) as well as a simplified Infomax (sInfomax). Two novel performance indexes, self-test accuracy and the number of invalid ICA filters, were employed to assess the performance of MIBCI based on different ICA variants. As a reference method, common spatial pattern (CSP), a commonly-used spatial filtering method, was employed for the comparative study between ICA-MIBCI and CSP-MIBCI. The experimental results showed that sInfomax-based spatial filters exhibited significantly better transferability in session to session and subject to subject transfer as compared to CSP-based spatial filters. The online experiment was also introduced to demonstrate the practicability and feasibility of sInfomax-based MIBCI. However, four classical ICA variants, especially FastICA, Jade, and Sobi, performed much worse as compared to sInfomax and CSP in terms of classification accuracy and stability. We consider that conventional ICA-based spatial filtering methods tend to be overfitting while applied to real-life electroencephalogram data. Nevertheless, the sInfomax-based experimental results indicate that ICA methods have a great space for improvement in the application of MIBCI. We believe that this paper could bring forth new ideas for the practical implementation of ICA-MIBCI.

中文翻译：

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探索运动图像脑机接口中独立成分分析的潜力

本文致力于实验方法，以探索在基于运动图像（MI）的脑机接口（BCI）的背景下独立成分分析（ICA）的潜力。我们提出了一种基于ICA的MI BCI（ICA-MIBCI）的简单高效的算法框架，用于评估四种经典ICA算法（Infomax，FastICA，Jade和Sobi）以及简化的Infomax（sInfomax）。两种新颖的性能指标，即自检准确性和无效ICA过滤器的数量，被用来评估基于不同ICA变体的MIBCI的性能。作为参考方法，常用的空间模式（CSP）是一种常用的空间滤波方法，用于ICA-MIBCI和CSP-MIBCI的比较研究。实验结果表明，与基于CSP的空间过滤器相比，基于sInfomax的空间过滤器在会话之间和对象转移方面表现出明显更好的可传递性。还介绍了在线实验，以证明基于sInfomax的MIBCI的实用性和可行性。但是，在分类准确性和稳定性方面，四个经典ICA变体，尤其是FastICA，Jade和Sobi，与sInfomax和CSP相比，表现要差得多。我们认为，传统的基于ICA的空间滤波方法在应用于现实的脑电图数据时倾向于过度拟合。尽管如此，基于sInfomax的实验结果表明ICA方法在MIBCI的应用中仍有很大的改进空间。