EEG Signals Classification Related to Visual Objects Using Long Short-Term Memory Network and Nonlinear Interval Type-2 Fuzzy Regression

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.