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Dive into the research topics where Pieter Buteneers is active.

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Featured researches published by Pieter Buteneers.


Epilepsy Research | 2013

Real-time detection of epileptic seizures in animal models using reservoir computing.

Pieter Buteneers; David Verstraeten; Bregt Van Nieuwenhuyse; Dirk Stroobandt; Robrecht Raedt; Kristl Vonck; Paul Boon; Benjamin Schrauwen

In recent years, an increasing number of studies have investigated the effects of closed-loop anti-epileptic treatments. Most of the current research still is very labour intensive: real-time treatment is manually triggered and conclusions can only be drawn after multiple days of manual review and annotation of the electroencephalogram (EEG). In this paper we propose a technique based on reservoir computing (RC) to automatically and in real-time detect epileptic seizures in the intra-cranial EEG (iEEG) of epileptic rats in order to immediately trigger seizure treatment. The performance of the system is evaluated in two different seizure types: absence seizures from genetic absence epilepsy rats from Strasbourg (GAERS) and limbic seizures from post status epilepticus (PSE) rats. The dataset consists of 452 hours iEEG from 23 GAERS and 2083 hours iEEG from 22 PSE rats. In the default set-up the system detects 0.09 and 0.13 false positives per seizure and misses 0.07 and 0.005 events per seizure for GAERS and PSE rats respectively. It achieves an average detection delay below 1s in GAERS and less than 10s in the PSE data. This detection delay and the number of missed seizures can be further decreased when a higher false positive rate is allowed. Our method outperforms state-of-the-art detection techniques and only a few parameters require optimization on a limited training set. It is therefore suited for automatic seizure detection based on iEEG and may serve as a useful tool for epilepsy researchers. The technique avoids the time-consuming manual review and annotation of EEG and can be incorporated in a closed-loop treatment strategy.


Artificial Intelligence in Medicine | 2011

Automatic detection of epileptic seizures on the intra-cranial electroencephalogram of rats using reservoir computing

Pieter Buteneers; David Verstraeten; Pieter van Mierlo; Tine Wyckhuys; Dirk Stroobandt; Robrecht Raedt; Hans Hallez; Benjamin Schrauwen

INTRODUCTION In this paper we propose a technique based on reservoir computing (RC) to mark epileptic seizures on the intra-cranial electroencephalogram (EEG) of rats. RC is a recurrent neural networks training technique which has been shown to possess good generalization properties with limited training. MATERIALS The system is evaluated on data containing two different seizure types: absence seizures from genetic absence epilepsy rats from Strasbourg (GAERS) and tonic-clonic seizures from kainate-induced temporal-lobe epilepsy rats. The dataset consists of 452hours from 23 GAERS and 982hours from 15 kainate-induced temporal-lobe epilepsy rats. METHODS During the preprocessing stage, several features are extracted from the EEG. A feature selection algorithm selects the best features, which are then presented as input to the RC-based classification algorithm. To classify the output of this algorithm a two-threshold technique is used. This technique is compared with other state-of-the-art techniques. RESULTS A balanced error rate (BER) of 3.7% and 3.5% was achieved on the data from GAERS and kainate rats, respectively. This resulted in a sensitivity of 96% and 94% and a specificity of 96% and 99% respectively. The state-of-the-art technique for GAERS achieved a BER of 4%, whereas the best technique to detect tonic-clonic seizures achieved a BER of 16%. CONCLUSION Our method outperforms up-to-date techniques and only a few parameters need to be optimized on a limited training set. It is therefore suited as an automatic aid for epilepsy researchers and is able to eliminate the tedious manual review and annotation of EEG.


international conference on neural information processing | 2008

Real-time epileptic seizure detection on intra-cranial rat data using reservoir computing

Pieter Buteneers; Benjamin Schrauwen; David Verstraeten; Dirk Stroobandt

In this paper it is shown that Reservoir Computing can be successfully applied to perform real-time detection of epileptic seizures in Electroencephalograms (EEGs). Absence and tonic-clonic seizures are detected on intracranial EEG coming from rats. This resulted in an area under the Receiver Operating Characteristics (ROC) curve of about 0.99 on the data that was used. For absences an average detection delay of 0.3s was noted, for tonic-clonic seizures this was 1.5s. Since it was possible to process 15h of data on an average computer in 14.5 minutes all conditions are met for a fast and reliable real-time detection system.


Neural Processing Letters | 2013

Optimized Parameter Search for Large Datasets of the Regularization Parameter and Feature Selection for Ridge Regression

Pieter Buteneers; Ken Caluwaerts; Joni Dambre; David Verstraeten; Benjamin Schrauwen

In this paper we propose mathematical optimizations to select the optimal regularization parameter for ridge regression using cross-validation. The resulting algorithm is suited for large datasets and the computational cost does not depend on the size of the training set. We extend this algorithm to forward or backward feature selection in which the optimal regularization parameter is selected for each possible feature set. These feature selection algorithms yield solutions with a sparse weight matrix using a quadratic cost on the norm of the weights. A naive approach to optimizing the ridge regression parameter has a computational complexity of the order


Journal of Neural Engineering | 2015

Towards a symbiotic brain–computer interface: exploring the application–decoder interaction

Thibault Verhoeven; Pieter Buteneers; Jan R. Wiersema; Joni Dambre; Pieter-Jan Kindermans


Proceedings of the 6th International Brain-Computer Interface Conference 2014 | 2014

Switching characters between stimuli improves P300 speller accuracy

Thibault Verhoeven; Pieter-Jan Kindermans; Pieter Buteneers; Benjamin Schrauwen

O(R K N^{2} M)


Journal of Machine Learning Research | 2012

Oger: modular learning architectures for large-scale sequential processing

David Verstraeten; Benjamin Schrauwen; Sander Dieleman; Philemon Brakel; Pieter Buteneers; Dejan Pecevski


5th international brain-computer interface conference, Proceedings | 2011

How do you like your P300 speller: adaptive, accurate and simple?

Pieter-Jan Kindermans; David Verstraeten; Pieter Buteneers; Benjamin Schrauwen

with


neural information processing systems | 2010

An uncued brain-computer interface using reservoir computing

Pieter-Jan Kindermans; Pieter Buteneers; David Verstraeten; Benjamin Schrauwen


Proceedings of the 19th Annual Workshop on Circuits, Systems and Signal Processing | 2008

Epileptic seizure detection using Reservoir Computing

Pieter Buteneers; Benjamin Schrauwen; David Verstraeten; Dirk Stroobandt

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Kristl Vonck

Ghent University Hospital

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Paul Boon

Ghent University Hospital

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