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首页> 外文期刊>American journal of applied sciences >Comparative Evaluation of Adaptive Filter and Neuro-Fuzzy Filter in Artifacts Removal From Electroencephalogram Signal
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Comparative Evaluation of Adaptive Filter and Neuro-Fuzzy Filter in Artifacts Removal From Electroencephalogram Signal

机译：自适应滤波器和神经模糊滤波器在脑电信号去除伪像方面的比较评估

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Problem statement: This study presents an effective method for removing mixed artifacts (EOG-Electro-ocular gram, ECG-Electrocardiogram, EMG-Electromyogram) from the EEG-Electroencephalogram records. The noise sources increases the difficulty in analyzing the EEG and obtaining clinical information. EEG signals are multidimensional, non-stationary (i.e., statistical properties are not invariant in time), time domain biological signals, which are not reproducible. It is supposed to contain information about what is going on in the ensemble of excitatory pyramidal neuron level, at millisecond temporal resolution scale. Since scalp EEG contains considerable amount of noise and artifacts and exactly where it is coming from is poorly determined, extracting information from it is extremely challenging. For this reason it is necessary to design specific filters to decrease such artifacts in EEG records. Approach: Some of the other methods that are really appealing are artifact removal through Independent Component Analysis (ICA), Wavelet Transforms, Linear filtering and Artificial Neural Networks. ICA method could be used in situations, where large numbers of noises need to be distinguished, but it is not suitable for on-line real time application like Brain Computer Interface (BCI). Wavelet transforms are suitable for real-time application, but there all success lies in the selection of the threshold function. Linear filtering is best when; the frequency of noises does not interfere or overlap with each other. In this study we proposed adaptive filtering and neuro-fuzzy filtering method to remove artifacts from EEG. Adaptive filter performs linear filtering. Neuro-fuzzy approaches are very promising for non-linear filtering of noisy image. The multiple-output structure is based on recursive processing. It is able to adapt the filtering action to different kinds of corrupting noise. Fuzzy reasoning embedded into the network structure aims at reducing errors when fine details are processed. Results: The computational result shows that the artifacts from the EEG are removed to a great extent and this has led to the accurate analysis and diagnosis of the EEG related diseases. Conclusion: Experimental results show that the proposed neuro-fuzzy technique is very effective and performs significantly better. The fidelity of the reconstructed EEG signal is assessed quantitatively using parameters such as Signal to Noise Ratio (SNR) and Power Spectral Density (PSD). In addition we have also compared the performance of adaptive filter and neuro-fuzzy filter based on the above parameters.
机译：问题陈述：这项研究提出了一种从脑电图脑电图记录中去除混合伪影（EOG-眼电图，ECG-心电图，EMG-心电图）的有效方法。噪声源增加了分析脑电图和获得临床信息的难度。 EEG信号是多维的，非平稳的（即统计属性在时间上不是不变的），时域生物学信号，无法再现。它应该包含有关毫秒级时间分辨率范围内的兴奋性锥体神经元水平集合中正在发生的事情的信息。由于头皮脑电图包含大量噪声和伪影，并且确切地确定其来源，因此从中提取信息非常困难。因此，有必要设计特定的过滤器以减少EEG记录中的此类伪像。方法：其他一些真正吸引人的方法是通过独立分量分析（ICA），小波变换，线性滤波和人工神经网络去除伪影。 ICA方法可用于需要区分大量噪声的情况，但不适用于诸如Brain Computer Interface（BCI）之类的在线实时应用。小波变换适合于实时应用，但是所有成功都在于选择阈值函数。线性过滤的最佳时机；噪声的频率不会相互干扰或重叠。在这项研究中，我们提出了自适应滤波和神经模糊滤波方法，以从脑电图中去除伪像。自适应滤波器执行线性滤波。对于模糊图像的非线性滤波，神经模糊方法非常有前途。多输出结构基于递归处理。它能够使过滤动作适应各种噪声干扰。嵌入到网络结构中的模糊推理旨在减少处理精细细节时的错误。结果：计算结果表明，脑电信号中的伪影已得到很大程度的去除，从而导致对脑电相关疾病的准确分析和诊断。结论：实验结果表明，所提出的神经模糊技术非常有效，并且效果明显更好。使用诸如信噪比（SNR）和功率谱密度（PSD）之类的参数定量评估重建的EEG信号的保真度。此外，我们还根据上述参数比较了自适应滤波器和神经模糊滤波器的性能。

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