[dataset] MLSPred-Bench: Reference EEG Benchmark for Prediction of Epileptic Seizures

[Method Development] Predicting Epileptic Seizures

1. Systems and methods for patient-specific epileptic seizure prediction

2. MLSPred-Bench: Transforming Electroencephalography (EEG) Datasets into Machine Learning-Ready Seizure Prediction Benchmarks

3. TA‐RNN: an Attention‐based Time‐aware Recurrent Neural Network Architecture to Predict Progression of Alzheimer’s Disease

4. Robustness of ML-Based Seizure Prediction Using Noisy EEG Data From Limited Channels

5. Lightweight Transformer exhibits comparable performance to LLMs for Seizure Prediction: A case for light-weight models for EEG data

6. Utilizing Pretrained Vision Transfomers and Large Language Models for Epileptic Seizure Prediction

7. Heterogeneity Aware Distributed Machine Learning at the Wireless Edge for Health IoT Applications: An EEG Data Case Study

8. Communication Evaluation of a Wireless 4-Channel Wearable EEG for Brain-Computer Interface (BCI) and Healthcare Applications

9. PPAD: a deep learning architecture to predict progression of Alzheimer’s disease

10. Energy Efficient AI/ML based Continuous Monitoring at the Edge: ECG and EEG Case Study

11. SPERTL: Epileptic Seizure Prediction using EEG with ResNets and Transfer Learning

12. Simulation Testbed for Evaluating Distributed Querying and Searching of Mass Spectrometry Big Data in a Network-based Infrastructure

13. Search feasibility in distributed MS-proteomics big data