Giulia Lioi
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View article: Unsupervised Adaptive Deep Learning Method for BCI Motor Imagery Decoding
Unsupervised Adaptive Deep Learning Method for BCI Motor Imagery Decoding Open
International audience
View article: LLM meets Vision-Language Models for Zero-Shot One-Class Classification
LLM meets Vision-Language Models for Zero-Shot One-Class Classification Open
We consider the problem of zero-shot one-class visual classification, extending traditional one-class classification to scenarios where only the label of the target class is available. This method aims to discriminate between positive and …
View article: Inferring Latent Class Statistics from Text for Robust Visual Few-Shot Learning
Inferring Latent Class Statistics from Text for Robust Visual Few-Shot Learning Open
In the realm of few-shot learning, foundation models like CLIP have proven effective but exhibit limitations in cross-domain robustness especially in few-shot settings. Recent works add text as an extra modality to enhance the performance …
View article: Disambiguation of One-Shot Visual Classification Tasks: A Simplex-Based Approach
Disambiguation of One-Shot Visual Classification Tasks: A Simplex-Based Approach Open
The field of visual few-shot classification aims at transferring the state-of-the-art performance of deep learning visual systems onto tasks where only a very limited number of training samples are available. The main solution consists in …
View article: Spatial Graph Signal Interpolation with an Application for Merging BCI Datasets with Various Dimensionalities
Spatial Graph Signal Interpolation with an Application for Merging BCI Datasets with Various Dimensionalities Open
BCI Motor Imagery datasets usually are small and have different electrodes setups. When training a Deep Neural Network, one may want to capitalize on all these datasets to increase the amount of data available and hence obtain good general…
View article: Easy—Ensemble Augmented-Shot-Y-Shaped Learning: State-of-the-Art Few-Shot Classification with Simple Components
Easy—Ensemble Augmented-Shot-Y-Shaped Learning: State-of-the-Art Few-Shot Classification with Simple Components Open
Few-shot classification aims at leveraging knowledge learned in a deep learning model, in order to obtain good classification performance on new problems, where only a few labeled samples per class are available. Recent years have seen a f…
View article: EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients
EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple Ingredients Open
Few-shot learning aims at leveraging knowledge learned by one or more deep learning models, in order to obtain good classification performance on new problems, where only a few labeled samples per class are available. Recent years have see…
View article: The impact of neurofeedback on effective connectivity networks in chronic stroke patients: an exploratory study
The impact of neurofeedback on effective connectivity networks in chronic stroke patients: an exploratory study Open
Objective. In this study, we assessed the impact of electroencephalography-functional magnetic resonance imaging (EEG-fMRI) neurofeedback (NF) on connectivity strength and direction in bilateral motor cortices in chronic stroke patients. M…
View article: Few-Shot Decoding of Brain Activation Maps
Few-Shot Decoding of Brain Activation Maps Open
Few-shot learning addresses problems for which a limited number of training examples are available. So far, the field has been mostly driven by applications in computer vision. Here, we are interested in adapting recently introduced few-sh…
View article: A Survey on the Use of Haptic Feedback for Brain-Computer Interfaces and Neurofeedback
A Survey on the Use of Haptic Feedback for Brain-Computer Interfaces and Neurofeedback Open
Neurofeedback (NF) and brain-computer interface (BCI) applications rely on the registration and real-time feedback of individual patterns of brain activity with the aim of achieving self-regulation of specific neural substrates or control …
View article: Learning 2-in-1: Towards Integrated EEG-fMRI-Neurofeedback
Learning 2-in-1: Towards Integrated EEG-fMRI-Neurofeedback Open
Neurofeedback (NF) allows to exert self-regulation over specific aspects of one's own brain activity by returning information extracted in real-time from brain activity measures. These measures are usually acquired from a single modality, …
View article: A Multi-Target Motor Imagery Training Using Bimodal EEG-fMRI Neurofeedback: A Pilot Study in Chronic Stroke Patients
A Multi-Target Motor Imagery Training Using Bimodal EEG-fMRI Neurofeedback: A Pilot Study in Chronic Stroke Patients Open
Traditional rehabilitation techniques present limitations and the majority of patients show poor 1-year post-stroke recovery. Thus, Neurofeedback (NF) or Brain-Computer-Interface applications for stroke rehabilitation purposes are gaining …
View article: Bimodal EEG-fMRI Neurofeedback for upper motor limb rehabilitation: a pilot study on chronic patients
Bimodal EEG-fMRI Neurofeedback for upper motor limb rehabilitation: a pilot study on chronic patients Open
International audience
View article: A multi-target motor imagery training using EEG-fMRI Neurofeedback: an exploratory study on stroke
A multi-target motor imagery training using EEG-fMRI Neurofeedback: an exploratory study on stroke Open
International audience
View article: Efficacy of EEG-fMRI Neurofeedback in stroke in relation to the DTI structural damage: a pilot study
Efficacy of EEG-fMRI Neurofeedback in stroke in relation to the DTI structural damage: a pilot study Open
International audience
View article: Measuring depth of anaesthesia using changes in directional connectivity: a comparison with auditory middle latency response and estimated bispectral index during propofol anaesthesia
Measuring depth of anaesthesia using changes in directional connectivity: a comparison with auditory middle latency response and estimated bispectral index during propofol anaesthesia Open
Summary General anaesthesia is associated with changes in connectivity between different regions of the brain, the assessment of which has the potential to provide a novel marker of anaesthetic effect. We propose an index that quantifies t…
View article: Learning 2-in-1: Towards Integrated EEG-fMRI-Neurofeedback
Learning 2-in-1: Towards Integrated EEG-fMRI-Neurofeedback Open
Neurofeedback (NF) allows to exert self-regulation over specific aspects of one’s own brain activity by returning information extracted in real-time from brain activity measures. These measures are usually acquired from a single modality, …
View article: EEG connectivity measures and their application to assess the depth of anaesthesia and sleep
EEG connectivity measures and their application to assess the depth of anaesthesia and sleep Open
General anaesthesia has been used for more than two centuries to guarantee unconsciousness, analgesia and immobility during surgery, yet our ability to evaluate the level of anaesthesia of the patient remains insufficient. This contributes o…