This project explores the integration of convolutional neural networks (CNNs) and long short-term memory (LSTM) networks with attention mechanisms for time-series classification tasks, focusing on electroencephalography (EEG) and electrocorticography (ECoG) data. CNNs extract spatial features, LSTMs capture temporal dependencies, and attention mechanisms enhance the model's focus on relevant features. This hybrid approach enables advanced applications like seizure prediction and brain state classification. While CNNs and LSTMs are independently used in neuroimaging, their combined potential in seizure modeling is underexplored. Intraoperative neuromonitoring, a growing technique in neurosurgery, highlights the value of analyzing brain signals. 

The methodology involves preprocessing EEG/ECoG data to standardize signal quality, followed by applying CNN layers to extract spatial features. These features are then fed into LSTM layers to capture temporal patterns. Attention mechanisms are applied to focus on the most relevant temporal or spatial features. The model is trained using labeled seizure datasets and validated on unseen data. Performance is evaluated using measures of classification accuracy, prediction latency, and computational efficiency. The model's hyperparameters, including those for CNN, LSTM, and attention mechanisms, are tuned to optimize performance. 

The hybrid CNN-LSTM model with attention mechanisms achieved an accuracy rate of 62.5% in detecting seizures based on the training dataset. While this result indicates the model's potential to capture spatial-temporal dependencies, it highlights the need for further improvements. Enhancements in data preprocessing, model architecture, and feature representation are essential to utilize the hybrid model's capabilities and achieve higher classification performance. 

The hybrid CNN-LSTM model with attention mechanisms is expected to outperform single-network models in capturing spatial-temporal patterns in EEG/ECoG data. It should achieve higher classification accuracy, particularly in seizure detection, with reduced prediction latency. The hybrid model is anticipated to consistently outperform standalone CNNs and LSTMs, with attention mechanisms enhancing focus on key features.