Ahmadieh Hajar, Ghassemi Farnaz, Moradi Mohammad Hassan
Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran.
Brain Topogr. 2025 Jan 6;38(2):20. doi: 10.1007/s10548-024-01080-0.
By gaining insights into how brain activity is encoded and decoded, we enhance our understanding of brain function. This study introduces a method for classifying EEG signals related to visual objects, employing a combination of an LSTM network and nonlinear interval type-2 fuzzy regression (NIT2FR). Here, ResNet is utilized for feature extraction from images, the LSTM network for feature extraction from EEG signals, and NIT2FR for mapping image features to EEG signal features. The application of type-2 fuzzy logic addresses uncertainties arising from EEG signal nonlinearity, noise, limited data sample size, and diverse mental states among participants. The Stanford database was used for implementation, evaluating effectiveness through metrics like classification accuracy, precision, recall, and F1 score. According to the findings, the LSTM network achieved an accuracy of 55.83% in categorizing images using raw EEG data. When compared to other methods like linear type-2, linear/nonlinear type-1 fuzzy, neural network, and polynomial regression, NIT2FR coupled with an SVM classifier outperformed with a 68.05% accuracy. Thus, NIT2FR demonstrates superiority in handling high uncertainty environments. Moreover, the 6.03% improvement in accuracy over the best previous study using the same dataset underscores its effectiveness. Precision, recall, and F1 score results for NIT2FR were 68.93%, 68.08%, and 68.49% respectively, surpassing outcomes from linear type-2, linear/nonlinear type-1 fuzzy regression methods.
通过深入了解大脑活动是如何被编码和解码的，我们增进了对大脑功能的理解。本研究介绍了一种用于对与视觉对象相关的脑电图（EEG）信号进行分类的方法，该方法采用了长短期记忆（LSTM）网络和非线性区间二型模糊回归（NIT2FR）相结合的方式。在这里，残差网络（ResNet）用于从图像中提取特征，LSTM网络用于从EEG信号中提取特征，而NIT2FR用于将图像特征映射到EEG信号特征。二型模糊逻辑的应用解决了由于EEG信号的非线性、噪声、有限的数据样本大小以及参与者之间不同的心理状态而产生的不确定性。本研究使用斯坦福数据库进行实施，并通过分类准确率、精确率、召回率和F1分数等指标评估有效性。根据研究结果，LSTM网络在使用原始EEG数据对图像进行分类时，准确率达到了55.83%。与线性二型、线性/非线性一型模糊、神经网络和多项式回归等其他方法相比，结合支持向量机（SVM）分类器的NIT2FR表现更优，准确率达到了68.05%。因此，NIT2FR在处理高不确定性环境方面表现出优越性。此外，与使用相同数据集的之前最佳研究相比，准确率提高了6.03%，突出了其有效性。NIT2FR的精确率、召回率和F1分数结果分别为68.93%、68.08%和68.49%，超过了线性二型、线性/非线性一型模糊回归方法的结果。
