LSTM (Long Short-Term Memory) is commonly used for sequential data, including time-series datasets like EEG signals, as well as in Natural Language Processing (NLP) and speech analysis, because these types of data are treated as consecutive sequences. For image classification, deep learning models are among the most widely used techniques. Pre-trained models such as VGG19 or ResNet can be applied directly or combined to create ensemble models, which use multiple pre-trained models together. Integrating an attention mechanism with a deep learning model can further improve the feature extraction process.