Precision Forecasting of Brain Activity Using Reservoir Computing, Attention Networks, and Long Short-term Memory...

Rapid identification of trauma-induced brain activity is critical for mitigating posttraumatic stress disorder (PTSD), especially in military personnel exposed to high-stress environments. This work proposes a novel framework that integrates reservoir computing (RC) with deep learning to classify individuals with PTSD and healthy controls based on EEG data. RC, particularly echo state networks (ESN), efficiently processes temporal data by projecting inputs into a high-dimensional space, making it ideal for complex datasets. To further enhance accuracy, we incorporate long short-term memory (LSTM) and attention-based models for focused feature learning and error correction. EEG-derived features were extracted and used to train three deep learning architectures: dense neural networks (DNNs), LSTM, and attention-based models. Results show that DNN achieved the highest classification accuracy at 92.16%, while LSTM and attention models yielded 84.31% and 74.51%, respectively. These findings demonstrate the potential of the RC-deep learning hybrid framework for both EEG signal prediction and PTSD classification. Future work will focus on real-time implementation in wearable neural technologies for continuous monitoring and early diagnosis of PTSD in high-stress environments.