Publications
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Augmenting interictal mapping with neurovascular coupling biomarkers by structured factorization of epileptic EEG and fMRI data. KU Leuven
EEG-correlated fMRI analysis is widely used to detect regional BOLD fluctuations that are synchronized to interictal epileptic discharges, which can provide evidence for localizing the ictal onset zone. However, the typical, asymmetrical and mass-univariate approach cannot capture the inherent, higher order structure in the EEG data, nor multivariate relations in the fMRI data, and it is nontrivial to accurately handle varying neurovascular ...
Semi-automated EEG Enhancement Improves Localization of Ictal Onset Zone With EEG-Correlated fMRI KU Leuven
Objective: To improve the accuracy of detecting the ictal onset zone, we propose to enhance the epilepsy-related activity present in the EEG signals, before mapping their BOLD correlates through EEG-correlated fMRI analysis. Methods: Based solely on a segmentation of interictal epileptic discharges (IEDs) on the EEG, we train multi-channel Wiener filters (MWF) which enhance IED-like waveforms, and suppress background activity and noisy ...
Unsupervised respiratory signal extraction from ungated cardiac magnetic resonance imaging at rest and during exercise KU Leuven
We propose and evaluate a method to estimate a respiratory signal from ungated cardiac magnetic resonance (CMR) images. Ungated CMR images were acquired in five subjects who performed exercise at different intensity levels under different physiological conditions while breathing freely. The respiratory motion was estimated by applying principal components analysis (PCA). A sign correction procedure was developed to correctly define inspiration ...
Single-channel EEG classification by multi-channel tensor subspace learning and regression KU Leuven
© 2018 IEEE. The classification of brain states using neural recordings such as electroencephalography (EEG) finds applications in both medical and non-medical contexts, such as detecting epileptic seizures or discriminating mental states in brain-computer interfaces, respectively. Although this endeavor is well-established, existing solutions are typically restricted to lab or hospital conditions because they operate on recordings from a set of ...
EEG-based attention-driven speech enhancement for noisy speech mixtures using N-fold multi-channel Wiener filters KU Leuven
© 2017 EURASIP. Hearing prostheses have built-in algorithms to perform acoustic noise reduction and improve speech intelligibility. However, in a multi-speaker scenario the noise reduction algorithm has to determine which speaker the listener is focusing on, in order to enhance it while suppressing the other interfering sources. Recently, it has been demonstrated that it is possible to detect auditory attention using electroencephalography ...
Flexible Fusion of Electroencephalography and Functional Magnetic Resonance Imaging: Revealing Neural-Hemodynamic Coupling Through Structured Matrix-Tensor Factorization KU Leuven
© EURASIP 2017. Simultaneous recording of electroencephalographic (EEG) signals and functional magnetic resonance images (fMRI) has gained wide interest in brain research, thanks to the highly complementary spatiotemporal nature of both modalities. We propose a novel technique to extract sources of neural activity from the multimodal measurements, which relies on a structured form of coupled matrix-tensor factorization (CMTF). In a datasymmetric ...
EEG-informed attended speaker extraction from recorded speech mixtures with application in neuro-steered hearing prostheses KU Leuven
OBJECTIVE: We aim to extract and denoise the attended speaker in a noisy two-speaker acoustic scenario, relying on microphone array recordings from a binaural hearing aid, which are complemented with electroencephalography (EEG) recordings to infer the speaker of interest. METHODS: In this study, we propose a modular processing flow that first extracts the two speech envelopes from the microphone recordings, then selects the attended speech ...
Adaptive attention-driven speech enhancement for EEG-informed hearing prostheses KU Leuven
State-of-the-art hearing prostheses are equipped with acoustic noise reduction algorithms to improve speech intelligibility. Currently, one of the major challenges is to perform acoustic noise reduction in so-called cocktail party scenarios with multiple speakers, in particular because it is difficult-if not impossible-for the algorithm to determine which are the target speaker(s) that should be enhanced, and which speaker(s) should be treated ...