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Found 10 papers, 2 papers with code
Augmenting Replay in World Models for Continual Reinforcement Learning
Also, the concept of replay comes from biological inspiration, where evidence suggests that replay is applied to a world model, which implies model-based RL -- and model-based RL should have benefits for continual RL, where it is possible to exploit knowledge independent of the policy.
Left/Right Brain, human motor control and the implications for robotics
The models were trained and tested on two tasks common in the human motor control literature: the random reach task, suited to the dominant system, a model with better coordination, and the hold position task, suited to the non-dominant system, a model with more stable movement.
Coherent False Seizure Prediction in Epilepsy, Coincidence or Providence?
Here, we examine false and missing alarms of two algorithms on long-term datasets to show that the limitations are less related to classifiers or features, but rather to intrinsic changes in the data.
A Generative Model to Synthesize EEG Data for Epileptic Seizure Prediction
One of the roadblocks for accurate seizure prediction is scarcity of epileptic seizure data.
Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review
Here we provide a comprehensive review of state-of-the-art ML techniques in early prediction of seizures using EEG signals.
Human Intracranial EEG Quantitative Analysis and Automatic Feature Learning for Epileptic Seizure Prediction
2) The traditional PCA is not a reliable method for iEEG data reduction in seizure prediction.
Convolutional Neural Networks for Epileptic Seizure Prediction
In this contribution, we present and discuss a novel methodology for the classification of intracranial electroencephalography (iEEG) for seizure prediction.
Semi-supervised Seizure Prediction with Generative Adversarial Networks
In this article, we propose an approach that can make use of not only labeled EEG signals but also the unlabeled ones which is more accessible.
A Generalised Seizure Prediction with Convolutional Neural Networks for Intracranial and Scalp Electroencephalogram Data Analysis
The proposed approach achieves sensitivity of 81. 4%, 81. 2%, 82. 3% and false prediction rate (FPR) of 0. 06/h, 0. 16/h, 0. 22/h on Freiburg Hospital intracranial EEG (iEEG) dataset, Children's Hospital of Boston-MIT scalp EEG (sEEG) dataset, and Kaggle American Epilepsy Society Seizure Prediction Challenge's dataset, respectively.
Supervised Learning in Automatic Channel Selection for Epileptic Seizure Detection
We present a novel method for automatic seizure detection based on iEEG data that outperforms current state-of-the-art seizure detection methods in terms of computational efficiency while maintaining the accuracy.
