Epilepsy is characterized by recurring seizures that result from abnormal electrical activity in the brain. These seizures manifest as various symptoms including muscle contractions and loss of consciousness. The challenging task of detecting epileptic seizures involves classifying electroencephalography (EEG) signals into ictal (seizure) and interictal (non-seizure) classes. This classification is crucial because it distinguishes between the states of seizure and seizure-free periods in patients with epilepsy. Our study presents an innovative approach for detecting seizures and neurological diseases using EEG signals by leveraging graph neural networks. This method effectively addresses EEG data processing challenges. We construct a graph representation of EEG signals by extracting features such as frequency-based, statistical-based, and Daubechies wavelet transform features. This graph representation allows for potential differentiation between seizure and non-seizure signals through visual inspection of the extracted features. To enhance seizure detection accuracy, we employ two models: one combining a graph convolutional network (GCN) with long short-term memory (LSTM) and the other combining a GCN with balanced random forest (BRF). Our experimental results reveal that both models significantly improve seizure detection accuracy, surpassing previous methods. Despite simplifying our approach by reducing channels, our research reveals a consistent performance, showing a significant advancement in neurodegenerative disease detection. Our models accurately identify seizures in EEG signals, underscoring the potential of graph neural networks. The streamlined method not only maintains effectiveness with fewer channels but also offers a visually distinguishable approach for discerning seizure classes. This research opens avenues for EEG analysis, emphasizing the impact of graph representations in advancing our understanding of neurodegenerative diseases.

Tinnitus is the perception of sounds like ringing or buzzing in the ears without any external source, varying in intensity and potentially becoming chronic. This study aims to enhance the understanding and treatment of tinnitus by analyzing a dataset related to tinnitus therapy, focusing on electroencephalography (EEG) signals from patients undergoing treatment. The objectives of the study include applying various preprocessing techniques to ensure data quality, such as noise elimination and standardization of sampling rates, and extracting essential features from EEG signals, including power spectral density and statistical measures. The novelty of this research lies in its innovative approach to representing different channels of EEG signals as new graph network representations without losing any information. This transformation allows for the use of Graph Neural Networks (GNNs), specifically Graph Convolutional Networks (GCNs) combined with Long Short-Term Memory (LSTM) networks, to model intricate relationships and temporal dependencies within the EEG data. This method enables a comprehensive analysis of the complex interactions between EEG channels. The study reports an impressive accuracy rate of 99.41%, demonstrating the potential of this novel approach. By integrating graph representation and deep learning, this research introduces a new methodology for analyzing tinnitus therapy data, aiming to contribute to more effective treatment strategies for tinnitus sufferers.

Semiconductor wafer defect detection faces a critical challenge: traditional supervised methods require extensive labeled datasets that are costly to obtain. This paper presents a novel self-supervised approach combining Graph Neural Networks (GNNs) with contrastive learning for defect detection that minimizes reliance on labeled training data. Our method introduces a theoretically-grounded graph construction using 8-connectivity patterns that preserves spatial locality, with edge weights capturing geometric and semantic relationships. The core innovation is a multi-criteria contrastive learning framework incorporating spatial pattern similarity, defect density proximity, and production lot relationships to define positive-negative pairs using domain-driven heuristics rather than manual annotations. We implement this using a Graph Isomorphism Network (GIN) with jumping knowledge connections for multi-scale defect patterns. Evaluation on the WM-811K dataset (811,457 wafer maps from 46,293 lots) demonstrates that unsupervised clustering on learned embeddings achieves ARI of 0.89 and NMI of 0.87, while supervised fine-tuning on these embeddings yields 98.6% classification accuracy (ARI 0.98, NMI 0.96), maintaining 95.7% accuracy with only 25% labeled data. Our approach handles class imbalance (91.5% normal vs. 8.5% defective) across 9 defect patterns. Deployment analysis shows 8.3ms inference time, 387MB memory footprint, and 78% cost reduction versus manual inspection, with robustness to sensor noise and an 89.3% out-of-distribution detection rate. This provides the first contrastive learning-GNN application for wafer defect detection, offering scalable industrial quality control where labeled data is scarce.