1 code implementation • 30 Jan 2024 • Luke Yang, Levin Kuhlmann, Gideon Kowadlo
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.
1 code implementation • 25 Jan 2024 • Jarrad Rinaldo, Levin Kuhlmann, Jason Friedman, Gideon Kowadlo
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.
no code implementations • 26 Oct 2021 • Jens Müller, Hongliu Yang, Matthias Eberlein, Georg Leonhardt, Ortrud Uckermann, Levin Kuhlmann, Ronald Tetzlaff
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.
no code implementations • 1 Dec 2020 • Khansa Rasheed, Junaid Qadir, Terence J. O'Brien, Levin Kuhlmann, Adeel Razi
One of the roadblocks for accurate seizure prediction is scarcity of epileptic seizure data.
no code implementations • 4 Feb 2020 • Khansa Rasheed, Adnan Qayyum, Junaid Qadir, Shobi Sivathamboo, Patrick Kwan, Levin Kuhlmann, Terence O'Brien, Adeel Razi
Here we provide a comprehensive review of state-of-the-art ML techniques in early prediction of seizures using EEG signals.
no code implementations • 7 Apr 2019 • Ramy Hussein, Mohamed Osama Ahmed, Rabab Ward, Z. Jane Wang, Levin Kuhlmann, Yi Guo
2) The traditional PCA is not a reliable method for iEEG data reduction in seizure prediction.
no code implementations • 2 Nov 2018 • Matthias Eberlein, Raphael Hildebrand, Ronald Tetzlaff, Nico Hoffmann, Levin Kuhlmann, Benjamin Brinkmann, Jens Müller
In this contribution, we present and discuss a novel methodology for the classification of intracranial electroencephalography (iEEG) for seizure prediction.
no code implementations • 20 Jun 2018 • Nhan Duy Truong, Levin Kuhlmann, Mohammad Reza Bonyadi, Omid Kavehei
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.
no code implementations • 6 Jul 2017 • Nhan Duy Truong, Anh Duy Nguyen, Levin Kuhlmann, Mohammad Reza Bonyadi, Jiawei Yang, Omid Kavehei
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.
no code implementations • 31 Jan 2017 • Nhan Truong, Levin Kuhlmann, Mohammad Reza Bonyadi, Jiawei Yang, Andrew Faulks, Omid Kavehei
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.