Denis A. Engemann
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View article: Hierarchical Variable Importance with Statistical Control for Medical Data-Based Prediction
Hierarchical Variable Importance with Statistical Control for Medical Data-Based Prediction Open
View article: Emotion recognition systems with electrodermal activity: From affective science to affective computing
Emotion recognition systems with electrodermal activity: From affective science to affective computing Open
View article: Assessing the robustness of deep learning based brain age prediction models across multiple EEG datasets
Assessing the robustness of deep learning based brain age prediction models across multiple EEG datasets Open
The increasing availability of large electroencephalography (EEG) datasets enhances the potential clinical utility of deep learning (DL) for cognitive and pathological decoding. However, dataset shifts due to variations in the population a…
View article: Neurologically altered brain activity may not look like aged brain activity: Implications for brain-age modeling and biomarker strategies
Neurologically altered brain activity may not look like aged brain activity: Implications for brain-age modeling and biomarker strategies Open
Background Brain-age gap (BAG), the difference between predicted age and chronological age, is studied as a biomarker for the natural progression of neurodegeneration. The BAG captures brain atrophy as measured with structural Magnetic Res…
View article: Exploring the neuromagnetic signatures of cognitive decline from mild cognitive impairment to Alzheimer's disease dementia
Exploring the neuromagnetic signatures of cognitive decline from mild cognitive impairment to Alzheimer's disease dementia Open
This work was supported by: Fondation pour la Recherche Médicale (grant FDM202106013579).
View article: GREEN: A lightweight architecture using learnable wavelets and Riemannian geometry for biomarker exploration with EEG signals
GREEN: A lightweight architecture using learnable wavelets and Riemannian geometry for biomarker exploration with EEG signals Open
Spectral analysis using wavelets is widely used for identifying biomarkers in EEG signals. Recently, Riemannian geometry has provided an effective mathematical framework for predicting biomedical outcomes from multichannel electroencephalo…
View article: Measuring Variable Importance in Heterogeneous Treatment Effects with Confidence
Measuring Variable Importance in Heterogeneous Treatment Effects with Confidence Open
Causal machine learning holds promise for estimating individual treatment effects from complex data. For successful real-world applications of machine learning methods, it is of paramount importance to obtain reliable insights into which v…
View article: Machine learning of brain-specific biomarkers from EEG
Machine learning of brain-specific biomarkers from EEG Open
View article: Exploring the neuromagnetic signatures of cognitive decline from mild cognitive impairment to Alzheimer’s disease dementia
Exploring the neuromagnetic signatures of cognitive decline from mild cognitive impairment to Alzheimer’s disease dementia Open
Introduction Alzheimer’s disease (AD) is the most common cause of dementia. Non-invasive, affordable, and largely available biomarkers that are able to identify patients at a prodromal stage of AD are becoming essential, especially in the …
View article: Geodesic Optimization for Predictive Shift Adaptation on EEG data
Geodesic Optimization for Predictive Shift Adaptation on EEG data Open
Electroencephalography (EEG) data is often collected from diverse contexts involving different populations and EEG devices. This variability can induce distribution shifts in the data $X$ and in the biomedical variables of interest $y$, th…
View article: Multimodal assessment improves neuroprognosis performance in clinically unresponsive critical-care patients with brain injury
Multimodal assessment improves neuroprognosis performance in clinically unresponsive critical-care patients with brain injury Open
View article: GREEN: a lightweight architecture using learnable wavelets and Riemannian geometry for biomarker exploration
GREEN: a lightweight architecture using learnable wavelets and Riemannian geometry for biomarker exploration Open
Spectral analysis using wavelets is widely used for identifying biomarkers in EEG signals. At the same time, Riemannian geometry enabled theoretically grounded machine learning models with high performance for predicting biomedical outcome…
View article: Variable Importance in High-Dimensional Settings Requires Grouping
Variable Importance in High-Dimensional Settings Requires Grouping Open
Explaining the decision process of machine learning algorithms is nowadays crucial for both model’s performance enhancement and human comprehension. This can be achieved by assessing the variable importance of single variables, even for hi…
View article: Physics-informed and Unsupervised Riemannian Domain Adaptation for Machine Learning on Heterogeneous EEG Datasets
Physics-informed and Unsupervised Riemannian Domain Adaptation for Machine Learning on Heterogeneous EEG Datasets Open
Combining electroencephalogram (EEG) datasets for supervised machine learning (ML) is challenging due to session, subject, and device variability. ML algorithms typically require identical features at train and test time, complicating anal…
View article: Do try this at home: Age prediction from sleep and meditation with large-scale low-cost mobile EEG
Do try this at home: Age prediction from sleep and meditation with large-scale low-cost mobile EEG Open
Electroencephalography (EEG) is an established method for quantifying large-scale neuronal dynamics which enables diverse real-world biomedical applications, including brain-computer interfaces, epilepsy monitoring, and sleep staging. Adva…
View article: Emotion Recognition Systems with Electrodermal Activity: From Affective Science to Affective Computing
Emotion Recognition Systems with Electrodermal Activity: From Affective Science to Affective Computing Open
View article: Machine learning of brain-specific biomarkers from EEG
Machine learning of brain-specific biomarkers from EEG Open
Electroencephalography (EEG) has a long history as a clinical tool to study brain function, and its potential to derive biomarkers for various applications is far from exhausted. Machine learning (ML) can guide future innovation by harness…
View article: Exploring the Underlying Emotional Models in Emotion Recognition Systems with Electrodermal Activity
Exploring the Underlying Emotional Models in Emotion Recognition Systems with Electrodermal Activity Open
Affective computing is an interdisciplinary field that aims to automatically recognize and interpret emotions. Recent research has focused on using physiological signals (e.g., electrodermal activity) to improve emotion recognition. Howeve…
View article: Event-related modulation of alpha rhythm explains the auditory P300-evoked response in EEG
Event-related modulation of alpha rhythm explains the auditory P300-evoked response in EEG Open
Evoked responses and oscillations represent two major electrophysiological phenomena in the human brain yet the link between them remains rather obscure. Here we show how most frequently studied EEG signals: the P300-evoked response and al…
View article: Author Response: Event-related modulation of alpha rhythm explains the auditory P300-evoked response in EEG
Author Response: Event-related modulation of alpha rhythm explains the auditory P300-evoked response in EEG Open
View article: Harmonizing and aligning M/EEG datasets with covariance-based techniques to enhance predictive regression modeling
Harmonizing and aligning M/EEG datasets with covariance-based techniques to enhance predictive regression modeling Open
Neuroscience studies face challenges in gathering large datasets, which limits the use of machine learning (ML) approaches. One possible solution is to incorporate additional data from large public datasets; however, data collected in diff…
View article: Event-related modulation of alpha rhythm explains the auditory P300 evoked response in EEG
Event-related modulation of alpha rhythm explains the auditory P300 evoked response in EEG Open
Evoked responses and ongoing oscillations represent two major electrophysiological phenomena in the human brain yet the link between them remains rather obscure. Here we show how these two types of brain activity can be mechanistically lin…
View article: Reviewer #1 (Public Review): Event-related modulation of alpha rhythm explains the auditory P300 evoked response in EEG
Reviewer #1 (Public Review): Event-related modulation of alpha rhythm explains the auditory P300 evoked response in EEG Open
Evoked responses and ongoing oscillations represent two major electrophysiological phenomena in the human brain yet the link between them remains rather obscure. Here we show how these two types of brain activity can be mechanistically lin…
View article: Author Response: Event-related modulation of alpha rhythm explains the auditory P300 evoked response in EEG
Author Response: Event-related modulation of alpha rhythm explains the auditory P300 evoked response in EEG Open
Evoked responses and ongoing oscillations represent two major electrophysiological phenomena in the human brain yet the link between them remains rather obscure. Here we show how these two types of brain activity can be mechanistically lin…
View article: Author response: Event-related modulation of alpha rhythm explains the auditory P300 evoked response in EEG
Author response: Event-related modulation of alpha rhythm explains the auditory P300 evoked response in EEG Open
Evoked responses and ongoing oscillations represent two major electrophysiological phenomena in the human brain yet the link between them remains rather obscure. Here we show how these two types of brain activity can be mechanistically lin…
View article: Statistically Valid Variable Importance Assessment through Conditional Permutations
Statistically Valid Variable Importance Assessment through Conditional Permutations Open
Variable importance assessment has become a crucial step in machine-learning applications when using complex learners, such as deep neural networks, on large-scale data. Removal-based importance assessment is currently the reference approa…
View article: Event-related modulation of alpha rhythm explains the auditory P300 evoked response in EEG
Event-related modulation of alpha rhythm explains the auditory P300 evoked response in EEG Open
Evoked responses and ongoing oscillations represent two major electrophysiological phenomena in the human brain yet the link between them remains rather obscure. Here we show how these two types of brain activity can be mechanistically lin…
View article: Author Response: Event-related modulation of alpha rhythm explains the auditory P300 evoked response in EEG
Author Response: Event-related modulation of alpha rhythm explains the auditory P300 evoked response in EEG Open
Evoked responses and ongoing oscillations represent two major electrophysiological phenomena in the human brain yet the link between them remains rather obscure. Here we show how these two types of brain activity can be mechanistically lin…
View article: Event-related modulation of alpha rhythm explains the auditory P300-evoked response in EEG
Event-related modulation of alpha rhythm explains the auditory P300-evoked response in EEG Open
Evoked responses and oscillations represent two major electrophysiological phenomena in the human brain yet the link between them remains rather obscure. Here we show how most frequently studied EEG signals: the P300-evoked response and al…
View article: Repurposing electroencephalogram monitoring of general anaesthesia for building biomarkers of brain ageing: an exploratory study
Repurposing electroencephalogram monitoring of general anaesthesia for building biomarkers of brain ageing: an exploratory study Open
Although EEG from general anaesthesia may enable state-of-the-art age prediction, differences between anaesthetic drugs can impact the effectiveness and validity of brain-age models. To unleash the dormant potential of EEG monitoring for c…