Faster Self-Organizing Fuzzy Neural Network Training and a Hyperparameter Analysis for a BrainComputer Interface
Faster Self-Organizing Fuzzy Neural Network Training and a Hyperparameter Analysis for a BrainComputer Interface
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DOI：
10.1109/tsmcb.2009.2018469
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发表时间：
2009-12-01
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通讯作者：
McGinnity, Thomas Martin
中科院分区：
文献类型：
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作者：
Coyle, Damien;Prasad, Girijesh;McGinnity, Thomas Martin
本文介绍了一些修改的自组织模糊神经网络（SOFNN）的学习算法，以提高计算效率。结果表明，修改后的SOFNN相比，其他不断发展的模糊系统的精度和结构复杂性。SOFNN的有效性分析时，应用在脑电图（EEG）为基础的脑-机接口（BCI）涉及神经时间序列预测预处理（NTSPP）的框架，其中的SOFNN超参数的灵敏度分析（SA）进行使用记录的EEG数据从三个主题在左/右运动图像为基础的BCI实验。这种一次性SA的目的是消除需要为SOFNN选择主题和信号特定的超参数，从而将NTSPP框架中的SOFNN应用为EEG预处理的无参数自组织框架。结果表明，通过SA选择的NTSPP参数的一般集合在BCI系统中测试时提供最佳结果。因此，利用这一通用的SOFNN参数集及其自组织结构，结合无参数特征提取和线性判别式分类，可以实现完全无参数的BCI，该BCI适合于自主适应。
This paper introduces a number of modifications to the learning algorithm of the self-organizing fuzzy neural network (SOFNN) to improve computational efficiency. It is shown that the modified SOFNN favorably compares to other evolving fuzzy systems in terms of accuracy and structural complexity. An analysis of the SOFNN's effectiveness when applied in an electroencephalogram (EEG)-based brain-computer interface (BCI) involving the neural-time-series-prediction-preprocessing (NTSPP) framework is also presented, where a sensitivity analysis (SA) of the SOFNN hyperparameters was performed using EEG data recorded from three subjects during left/right-motor-imagery-based BCI experiments. The aim of this one-time SA was to eliminate the need to choose subject- and signal-specific hyperparameters for the SOFNN and thus apply the SOFNN in the NTSPP framework as a parameterless self-organizing framework for EEG preprocessing. The results indicate that a general set of NTSPP parameters chosen via the SA provide the best results when tested in a BCI system. Therefore, with this general set of SOFNN parameters and its self-organizing structure, in conjunction with parameterless feature extraction and linear discriminant classification, a fully parameterless BCI that lends itself well to autonomous adaptation is realizable.