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

A Hybrid Deep Learning Architecture with Retrieval-Augmented Explanation Generation for Interpretable EEG-Based Stress Classification Across Multiple Paradigms

This paper presents a hybrid deep learning architecture integrating convolutional neural networks, bidirectional long short-term memory networks, self-attention, and retrieval-augmented generation (RAG) for accurate and interpretable electroencephalography (EEG)-based stress detection. The compact EEG encoder contains...

Attention-augmented CNN-LSTM framework for EEG-based spatiotemporal emotion recognition

EEG emotion recognition offers a direct path to the analysis of affective brain states. This work presents an attention-enhanced CNN-LSTM model for emotion recognition from EEG in the SEED benchmark dataset (three classes: positive, neutral, negative). The suggested method combines Power Spectral Density (PSD), Differe...

Predicting Worry Mental States using Long Short-Term Memory (LSTM) Recurrent Deep Neural Networks

BH-CMA: A cross-modal attention network based on brain-heart collaboration for stress cognitive state decoding.

Accurate recognition of stress states under different cognitive loads is important for adaptive human computer interaction and physiological stress monitoring. However, unimodal measurements provide incomplete central or autonomic information, while indiscriminate multimodal fusion may obscure the distinct temporal and...

A Deep Learning-Based Retrospective Evaluation Prediction System for Emotional Experiences: Temporal Dynamic Feature Extraction and ERP Neural Mechanisms of the Peak-End Effect

Retrospective evaluations of emotional experiences are significantly influenced by the peak-end effect, yet existing electroencephalography (EEG)-based emotion recognition studies have focused on classifying immediate emotional states, leaving integration of the peak-end effect into predictive modeling of retrospective...

Deep Learning for Scalp-Level Nonlinear Source Separation in Electroencephalogram: A Comparative Evaluation of Spatiotemporal Architectures

Electroencephalography (EEG) is a common technique to measure field potentials of various brain regions, and event-related potentials (ERPs) reflect stimulus- or response-locked brain responses that are spatially distributed across the scalp due to volume conduction. However, accurately capturing stimulus specific ERPs...