ERP Insights and Truncated SVD in conjunction with Dual-Tree Complex Wavelet Transform and Multi-View Hypergraph Neural Networks for Cognitive Distortion Analysis

Multi-modal EEG data analysis requires sophisticated methods for accurate prediction in the critical area of cognitive depression study in neuroscience. With the help of Multi-View Hypergraph Neural Networks (MV-HGNN) and Dual-Tree Complex Wavelet Transform (DT-CWT), a novel framework for enhancing cognitive distortion analysis is provided today. The initial stage of the procedure, DT-CWT, captures EEG signals and extracts the crucial frequency characteristics (gamma, delta, theta, beta, and alpha). Truncated singular value decomposition, or SVD, thereby reduces noise while preserving significant features. To identify task-related cognitive responses, Event-Related Potential (ERP) is used. The data is arranged into a multi-view framework following processing, which records multiple perspectives such as task-specific responses, frequency patterns, and temporal trends. To enable MV-HGNN to recognize complex cognitive patterns, a hypergraph is then constructed to mimic the complex relationships between various viewpoints. The final category predicts cognitive distortion. According on experimental data, the proposed method outperforms traditional deep learning models and delivers improved accuracy. This work shows that integrating multi-resolution feature extraction, dimensionality reduction, and hypergraph learning is effective for EEG-based cognitive distortion analysis.