Publications de Delphine Ribes Lemay

Advances on Real Time M/EEG Neural Feature Extraction

Nicolas Henchoz, Dimitri Nestor Alice Van De Ville, Delphine Ribes Lemay, Andreas Sonderegger, Emily Clare Groves, Patrick Karl Alois Neff

This paper introduces MNE-RT, a Python package designed for real-time neural feature extraction from magne-toencephalography (MEG) and electroencephalography (EEG) signals in Brain-Computer Interface (BCI) systems. The package incorporates efficient algori ...

IEEE2025

ANT - Advancing Neurofeedback (In Tinnitus)

Nicolas Henchoz, Delphine Ribes Lemay, Emily Clare Groves

The Advancing Neurofeedback in Tinnitus (ANT) project aims to develop improved neurofeedback protocols and BCI technology by systematically designing engaging feedback stimuli and optimizing neural targets. General design principles for audiovisual feedbac ...

IEEE2025

Designing Feedback Stimuli in Neurofeedback: Preliminary Requirements from Experts and Users

Nicolas Henchoz, Delphine Ribes Lemay, Nicolas Gninenko, Andreas Sonderegger, Emily Clare Groves, Patrick Karl Alois Neff

This study presents the first phase of a transdisciplinary research project aimed at improving the design of visual feedback stimuli in neurofeedback (NFB) applications. While current NFB research has focused extensively on signal processing and feature ex ...

IEEE2025

Explainable AI and Trust, Design Methodologies to Explore Patients' Perspective

Nicolas Henchoz, Delphine Ribes Lemay, Margherita Motta

This study investigates patient's perspective on the use of AI in healthcare and the role of Explainable AI in this context. Through a co-creative workshop with six participants from diverse disciplines, we investigated the impact of transparency on trust. ...

IEEE2025

Auditory Phantom Perceptions (Tinnitus) and Neurofeedback Training ‘In the Wild’: A Feasibility Study on Home Treatment

Delphine Ribes Lemay, Andreas Sonderegger, Patrick Karl Alois Neff

Tinnitus (TI) is a disease of the brain with high prevalence and often severe consequences for which no causal therapy approach has been established so far. Neurofeedback Training (NFT) is considered a promising approach to treat TI based on studies applyi ...

IEEE2025

Designing for Intimate Wellbeing: Aidee, a Qualitative Approach To Urine Home Monitoring

Nicolas Henchoz, Delphine Ribes Lemay, Emily Clare Groves, Margherita Motta

Despite the widespread use of self-tracking technologies for promoting personal wellbeing, there is limited research on the monitoring of intimate data, particularly urine. To shed light on the design possibilities within this unexplored domain we designed ...

DRS Digital Library2024

Toward Automatic Typography Analysis: Serif Classification and Font Similarities

Mathieu Salzmann, Nicolas Henchoz, Delphine Ribes Lemay, Romain Simon Collaud, Syed Talal Wasim

Whether a document is of historical or contemporary significance, typography plays a crucial role in its composition. From the early days of modern printing, typographic techniques have evolved and transformed, resulting in changes to the features of typog ...

2024

Designing self-tracking experiences: A qualitative study of the perceptions of barriers and facilitators to adopting digital health technology for automatic urine analysis at home

Nicolas Henchoz, Delphine Ribes Lemay, Emily Clare Groves, Andrea Regula Schneider, Margherita Motta

Self-tracking technologies open new doors to previously unimaginable scenarios. The diagnosis of diseases years in advance, or supporting the health of astronauts on missions to Mars are just some of many example applications. During the COVID-19 pandemic, ...

2023

Poster World. Bespoke AI Meets Curator Expertise for Public Engagement

Mathieu Salzmann, Nicolas Henchoz, Delphine Ribes Lemay, Emily Clare Groves, Andrea Regula Schneider

Though museums are digitising their archives, online consultations remain low. New forms of engagement bring these digital memories to life and can support museums in maintaining and developing digital resources. Artificial Intelligence presents opportunit ...

2022

Trust Indicators and Explainable AI: A Study on User Perceptions

Nicolas Henchoz, Daniel Gatica-Perez, Delphine Ribes Lemay, Thanh Trung Phan, Andreas Sonderegger

Nowadays, search engines, social media or news aggregators are the preferred services for news access. Aggregation is mostly based on artificial intelligence technologies raising a new challenge: Trust has been ranked as the most important factor for media ...

Springer2021

Ming Shan Digital Experience

Nicolas Henchoz, Delphine Ribes Lemay, Cédric Duchene, Andreas Sonderegger, Emily Clare Groves, Margaux Charvolin

The Ming Shan Digital Experience is an immersive installation designed to support meditation in the context of a new Taoist center. Its creation confronted current academic literature on digital technology for meditation with the practical and cultural req ...

2021

Automatic Content Curation of Visual Heritage

Mathieu Salzmann, Nicolas Henchoz, Delphine Ribes Lemay, Frédéric Kaplan, Valentine Bernasconi

Digitization and preservation of large heritage induce high maintenance costs to keep up with the technical standards and ensure sustainable access. Creating impactful usage is instrumental to justify the resources for long-term preservation. The Museum fü ...

2021

Food Talks: visual and interaction principles for representing environmental and nutritional food information in augmented reality

Nicolas Henchoz, Delphine Ribes Lemay, Andreas Sonderegger, Emily Clare Groves

This user-centered design research project aimed to investigate visual and interaction principles for augmented reality (AR) in the context of environmental and nutritional food labelling. Nutritional information on existing food labels is often misunderst ...

2019

An evaluation of volume-based morphometry for prediction of mild cognitive impairment and Alzheimer's disease

Meritxell Bach Cuadra, Reto Meuli, Bénédicte Marie Maréchal, Delphine Ribes Lemay, Daniel Andreas Schmitter, Gunnar Krüger, Ahmed Abdulkadir, Cristina Granziera, Alessandro Daducci, Alexis Roche

Voxel-based morphometry from conventional T1-weighted images has proved effective to quantify Alzheimer’s disease (AD) related brain atrophy and to enable fairly accurate automated classification of AD patients, mild cognitive impaired patients (MCI) and e ...

Elsevier2015

Volume-based vs. voxel-based brain morphometry in Alzheimer's disease prediction

Meritxell Bach Cuadra, Reto Meuli, Bénédicte Marie Maréchal, Delphine Ribes Lemay, Daniel Andreas Schmitter, Gunnar Krüger, Ahmed Abdulkadir, Cristina Granziera, Alessandro Daducci, Alexis Roche

Voxel-based morphometry from conventional T1-weighted images has proved effective to quantify Alzheimer's disease (AD) related brain atrophy and to enable accurate automated classification of AD patients, mild cognitive impaired (MCI) patients and elderly ...

Proceedings of 23rd International Society for Magnetic Resonance in Medicine (ISMRM) Conference2014

MP2RAGE multiple sclerosis magnetic resonance imaging at 3 T

Reto Meuli, Rolf Gruetter, Delphine Ribes Lemay, Gunnar Krüger, Tobias Kober, Cristina Granziera

Objectives: Lesion detection and characterization in multiple sclerosis (MS) are an essential part of its clinical diagnosis and an important research field. In this pilot study, we applied the recently introduced two inversion-contrast magnetization-prepa ...

2012

JULIDE: a software tool for 3D reconstruction and statistical analysis of autoradiographic mouse brain sections

Jean-Philippe Thiran, Pierre Magistretti, Rémi Charrier, Delphine Ribes Lemay

In this article we introduce JULIDE, a software toolkit developed to perform the 3D reconstruction, intensity normalization, volume standardization by 3D image registration and voxel-wise statistical analysis of autoradiographs of mouse brain sections. Thi ...

Public Library of Science2011

On the Convergence of EM-Like Algorithms for Image Segmentation using Markov Random Fields

Meritxell Bach Cuadra, Delphine Ribes Lemay, Gunnar Krüger, Alexis Roche

Inference of Markov random field images segmentation models is usually performed using iterative methods which adapt the well-known expectation-maximization (EM) algorithm for independent mixture models. However, some of these adaptations are ad-hoc and ma ...

2011

Who said fat is bad? Skull-stripping benefits from additional fat image

Reto Meuli, Delphine Ribes Lemay, Gunnar Krüger, Tobias Kober

Being a preliminary step for many clinical applications and analyses, accurate skull-stripping is a key challenge in MR brain imaging. One of its major difficulties arises from the contrast similarities at brain/non-brain tissue interfaces, in particular b ...

2011