This document provides an overview of key papers related to EEG analysis using machine learning techniques. It includes authors, key takeaways, and the current status of each paper. This table is automatically updated from a Google Sheet.
| 📄 Paper | 👨🔬 Author(s) / Institution | 🔑 Key Takeaways | 📅 Status |
|---|---|---|---|
| ML Techniques for EEG based on brain-computer interface: A systematic review | Pawan | "Two paths: 1: Signal acquisition → Pre-processing + features extraction → Classification 2: Signal Acquisition → Deep learning model (unsupervised?)" | |
| Review on mathematical modelling of (EEGs) | Darbas, Lohrengel | N/A | |
| Empowering CS Students in EEG Analysis: A Review of ML Algorithms for EEG Datasets | Murungi, Dai, Pham, Qu | Begin with supervised learning, classification tasks | |
| Machine learning of brain-specific biomarkers from EEG | Roche Innovation Center (Switzerland): Bomatter, Paillard, Garces, Hipp, Engemann | ||
| Epileptic seizure detection using CHB-MIT dataset: The overlooked perspectives | Ali, Angelova, Karmakar | ||
| Frequency of a false positive diagnosis of epilepsy: A systematic review of observational studies | False positive diagnoses are thought to result from various issues, from visual hallucinations to tingling. | ||
| Review of ML and Deep Learning Techniques in Epileptic Seizure Detection using Physiological Signals and Sentiment Analysis | |||
| Epileptic Seizure Detection and Prediction based on EEG Signal Analysis | |||
| Computer vision for automated seizure detection and classification: A systematic review | |||
| Machine Intelligence-Based Epileptic Seizure Forecasting | 2020 | Overview |