RESEARCHER in BRAIN
SIGNAL AND IMAGE 
PROCESSING



WELCOME!
I am a researcher in neuroimage and signal processing with a focus  on multimodality and brain connectivity.                                                                                                                              My current research project focuses on using Graph Signal Processing and Machine Learning to learn relevant brain representations. During my thesis I focused on multivariate causal measures of brain connectivity from EEG recordings; in my Post-Doctoral experience I extended my expertise to a range of MRI neuroimaging techniques. My research is interdisciplinary: the methodologies I propose are motivated by medical applications or neuroscience oriented questions such as monitoring the depth of anaesthesia or adapt brain computer interfaces to neurorehabilitation.  
E-mail: lioi.giulia@gmail.com

Address: Batiment K02 - IMT Atlantique

Campus de Brest
655 Avenue du Technopôle, 29280 Plouzané
Github: https://github.com/glioi 
Twitter: https://twitter.com/giulia_lioi


ABOUT

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SHORT BIO

Post-Doc IMT Atlantique (FRANCE) on  Graph Signal Processing and Machine Learning for multimodal neuroimaging.

Post-Doc INRIA Rennes (FRANCE) on EEG-fMRI Integration for Neurofeedback

PhD University of Southampton (UK) on multivariate causal estimators of brain connectivity to monitor anaesthesia

Research Internship Santa Lucia European Centre for Brain Research, Rome (ITALY)

Master Degree Biomedical Engineering , La Sapienza University, Rome (ITALY)

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RESEARCH INTERESTS

I am fascinated by the complex architecture and function of the brain and the wide range of methods that can help us understanding it:

  • Neuroimaging: EEG, fMRI, DTI 
  • Graph Signal Processing
  • Machine Learning
  • Brain Computer Interface and Neurofeedback
  • Network Neuroscience


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NEWS

1) PAPER ACCEPTED in Annals of Physical and Rehabilitation Medicine!

2) NEW PREPRINT! The Impact of Neurofeedback on Effective Connectivity Networks in Chronic Stroke Patients (medrxiv link).

2) Simultaneous MRI-EEG during a motor imagery neurofeedback task: an open access brain imaging dataset for multi-modal data integration ACCEPTED FOR PUBLICATION in SCIENTIFIC DATA-NATURE   !! (link to preprint) 

3) A Multi-Target Motor Imagery Training Using Bimodal EEG-fMRI Neurofeedback: A Pilot Study in Chronic Stroke Patients PUBLISHED in FRONTIERS IN NEUROSCIENCE  (link)

4) We have released two open access datasets of simultaneous EEG and MRI data during neurofeedback on OpenNeuro.org


Research

Bimodal EEG-fMRI NEUROFEEDBACK

Neurofeedback consists in training self-regulation of a specific brain function by providing a subject real-time information about his own brain activity. It is therefore thought to impact on the related pathological condition and a promising neurorehabilitation technique. 

In the Empenn team (INRIA, Rennes) we aim at making full and enhanced use of the neurofeedback paradigm by integrating EEG and fMRI in a unique experimental platform. We are currently testing the efficacity of bimodal neurofeedback for stroke rehabilitation and treadment of resistant depression.

My presentation for the OHBM Symposium (2019) on  EEG-fMRI Neurofeedback Integration for Stroke Rehabilitation https://www.pathlms.com/ohbm/courses/12238/sections/15843/video_presentations/137855

 The experimental platform for bimodal neurofeedback


https://www.incr.fr/la-plateforme-neurinfo/


University Hospital of Rennes, Neurinfo 
EEG effective connectivity in sleep and anaesthesia

Analysis of brain connectivity is crucial to understand the complex behaviour of the brain . During my thesis I concentrated on multivariate causal measures of brain connectivity from EEG recordings. In particular I was interested in assessing the impact of instantaneous causality on causal measures of  effective connectivity to identify robust measures to apply in the clinical context (for instance to monitor the depth of anaesthesia and sleep).

TEACHING

BIMODAL EEG-fMRI NEUROFEEDBACK TRAINING

24 -25 February 2020,  CHU Pontchaillou Rennes

Cours ESIR-3 Imagerie Médicale 2020

Neurofeedback et Interfaces Cerveau-Ordinateur: une Introduction: ppt

PUBLICATIONS

Reviewed International Journals

 1. Lioi G, Butet S, Fleury M, Bannier E, Lécuyer A, Bonan I, Barillot C. A multi-target motor imagery training using bimodal EEG-fMRI 1 Neurofeedback: a pilot studyon chronic stroke patients. Frontiers in Human Neuroscience (2020) 14 (February), 1–13  https://www.frontiersin.org/articles/10.3389/fnhum.2020.00037/full 

 2.  Lioi G, Bell SL, Smith DC, Simpson DM. Measuring depth of anaesthesia using changes in directional connectivity: a comparison with auditory middle latency response and estimated bispectral index during propofol anaesthesia. Anaesthesia (2018) pp: anae.14535 https://doi.org/10.1111/anae.14535 Impact Factor: 5.87

3. Lioi G, Bell SL, Smith DC, Simpson DM. Directional Connectivity in the EEG is able to discriminate wakefulness from NREM sleep. Physiol. Meas. 38 (2017) 1802 1820 https://iopscience.iop.org/article/10.1088/1361-6579/aa81b5 Impact Factor: 2.24

4. Butet S*, Lioi G, Fleury M, Bannier E, Lécuyer A, Barillot C, Bonan I. Bimodal EEG - fMRI Neurofeedback training to induce cerebral reorganization and enhance upper limb motricity after chronic stroke: a proof of concept study (Accepted at Annals of Physical and Rehabilitation Medicine!) Impact Factor: 4.19  

5. Lioi G, Cury C, Perronnet L., Mano M., Lécuyer A, Barillot C, Bonan I. Simultaneous MRI-EEG during a motor imagery neurofeedback task: an open access brain imaging dataset for multi-modal data integration, biorXiv 2019, https://doi.org/10.1101/862375 (Accepted for publication in Nature Scientific Data!)                                                                                                                                                                                                                                                                                     6. Perronnet L, Anatole L, Mano M, Fleury M, Lioi G, Cury C, ... Barillot C. Learning2-in-1 : Towards Integrated EEG-fMRI Neurofeedback. BioRxiv (2020) 1–30 (Under review in Frontiers in Neuroscience)   https://www.biorxiv.org/content/10.1101/397729v2 

Reviewed International Conferences With Proceedings

1. Cury C, Lioi G, Perronnet L., Lécuyer A, Maurel P, Barillot C. Impact of 1D and 2D visualisation on EEG-fMRI Neurofeedback training during a motor imagery task, IEEE International Symposium on Biomedical Imaging, 2020 (Accepted for publication)

2. Lioi G, Fleury M, Butet S, Lécuyer A, Barillot C, Bonan I. Bimodal EEG-fMRI Neurofeedback for Stroke Rehabilitation: a Case Report ISPRM, Paris, France. Annals of Physical and Rehabilitation Medicine Volume 61, Supplement, July 2018, Pages e482-e483 https://doi.org/10.1016/j.rehab.2018.05.1127

3. Lioi G, Bell S L and Simpson D M 2016. Changes in Functional Brain Connectivity in the Transition from Wakefulness to Sleep in different EEG bands. In: XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016. IFMBE Proceedings vol 57,pp 3–8 https://doi.org/10.1007/978-3-319-32703-7_1 (Best Student Paper Competition: Second Prize)

4. Lioi G, Bell S L, Smith D and Simpson D M 2016. The use of the middle latency response as an indicator of anesthetic depth: an investigation using slow induction of propofol anesthesia. XXV International Evoked Response Audimetry Study Group (IERASG) Biennal Sympo-sium, Warsaw, Poland, May 2017. http://dx.doi.org/10.13140/RG.2.2.19480.14081

5. Lioi G, Butet S, Fleury M, Lécuyer A, Bonan I, Barillot C Efficacy of EEG-fMRI Neurofeedback in stroke in relation to the DTI structural damage: a pilot study. Organization for Human Brain Mapping (OHBM), Rome, Italy, June 2019 https://hal.inria.fr/hal-02265495v1

6. Butet S, Lioi G, Fleury M, Lécuyer A, Barillot C, Bonan I. A multi-target motor imagery training using EEG-fMRI Neurofeedback: an exploratory study on stroke. Organization for Human Brain Mapping (OHBM), Rome, Italy, June 2019 https://hal.inria.fr/hal-02265496v1

7. Cury C, Maurel P, Lioi G, Gribonval R., Barillot C. Learning bi-modal EEG-fMRI neurofeedback to improve neurofeedback in EEG only. Real-Time functional Imaging and Neurofeedback (rtFIN), Maastricht, Netherlands, December 2019 https://hal.inria.fr/inserm-02368720v1

8. Lioi G, Butet S, Fleury M, Cury C, Elise B, et al. Bimodal EEG-fMRI Neurofeedback for upper motor limb rehabilitation: a pilot study on chronic patients. rtFIN 2019 - Real Time Functional Imaging and Neurofeedback, Dec 2019, Maastricht, Netherlands. pp.1-rtFIN 2019 - Real Time Functional Imaging and Neurofeedback, Dec 2019, Maastricht, Netherlands http://hal.archives-ouvertes.fr/hal-02383532

9. Fleury M, Lioi G, Barillot C, Anatole L. The use of haptic feedback in Brain-Computer Interfaces and Neurofeedback. rtFIN 2019 - Real Time Functional Imaging and Neurofeedback, Dec 2019, Maastricht, Netherlands https://hal.archives-ouvertes.fr/hal-02387400  

Public Dataset Release

1. Lioi G, Cury C, Perronnet L., Mano M., Lécuyer A, Barillot C.A multi-modal human neuroimaging dataset for data integration: simultaneous EEG and MRI acquisition during a motor imagery neurofeedback task: XP1. OpenNeuro dataset Repository. https://doi.org/10.18112/openneuro.ds002338.v1.0.1 

2. Lioi G, Cury C, Perronnet L., Mano M., Lécuyer A, Barillot C.A multi-modal human neuroimaging dataset for data integration: simultaneous EEG and MRI acquisition during a motor imagery neurofeedback task: XP2. OpenNeuro dataset Repository. https://doi.org/10.18112/openneuro.ds002336.v1.0.1

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