My Resume

Irene Vigué-Guix

Ph.D. Student of the Information and Communication Technologies (TIC) program at Pompeu Fabra University (UPF), with a Master Degree in Cognitive Neuroscience and a Bachelor Degree in Biomedical Engineering.



Ph.D. in Communication and Information Technologies

Universitat Pompeu Fabra (UPF), Mar 2018 – Expected: Mar 2021

Department of Information and Communication Technologies (DTIC)

Center for Brain and Cognition (CBC) – Multisensory Research Group (MRG)

MS in Brain and Cognition

 Universitat Pompeu Fabra (UPF), Sep 2016 – Sep 2017

THESIS PROJECT: “A closed loop Bran Computer Interface (BCI) on EEG alpha activity”

BS in Biomedical Engineering
  • Universitat Politecnica de  Catalunya (UPC)Sep 2011 – Jun 2015
  • Politecnico di Torino (Thesis project), Sep 2015 – Feb 2016

THESIS PROJECT: “Brain-Computer Interfaces (BCI) based on Movement-Related Cortical Potentials (MRCP)”



Neuroscientist Resident

OPENBCI, INC,  Brooklyn, New York, United States / Apr 2016 – Aug 2016.

  • OpenBCI stands for “an open-source brain-computer interface (BCI)“. The OpenBCI Board is a versatile and affordable bio-sensing microcontroller that can be used to sample brain electrical activity (EEG), muscle activity (EMG), heart rate (EKG), and more. It is compatible with almost any type of electrode and is supported by an ever-growing, open-source framework of signal processing tools & applications.
  • My primary goal is comparing motor imagery with motor execution such as different studies have been doing during the last decade, as well as differentiating between right and left-hand movements. To accomplish this, I’ll base my research on narrowing my frequency range into alpha (mu) and beta waves.
Junior Biomedical Engineer Intern

FICOSA (FICOMIRRORS, S.A.),  Barcelona, Catalonia / Mar 2015 – Jun 2015.

  • FICOSA is a multinational corporation devoted to the research, development, production, and commercialization of systems and parts for the automobile, as well as for both commercial and industrial vehicles.
  • Worked on the Healing Drop Project, which consists of developing medical multiparametric processing algorithms and biomedical data and user behavior relevant to the detection of excessive sleepiness during driving.
  • Validated the multiparametric processing algorithms applied to all the data.



Research volunteer at the National Center for Adaptative Neurotechnologies (NCAN)

WADSWORTH CENTER, Albany, New York, United States / Mar 2016 – Apr 2016

  • Studies include sensorimotor rhythms (SMR)
  • EEG Signal processing in MATLAB and EEGLAB.
  • Data analysis of EEG datasets in R program.
  • A system for comprehensive kinematic/EMG/EEG analyses of locomotion and other motor actions in normal volunteers and people with spinal cord injuries, cerebral palsy, or other chronic neuromuscular disorders.
Research Assistant

Biomedical Engineering Research Centre (CREB),  Barcelona, Catalonia / Oct 2014 – Mar 2015

Worked with Prof Xavier Rosell and Prof Mireia Fernandez in the Biomedical Instrumentation group to create a shoe-embedded piezoelectric energy harvester for wearable sensors in walking race environments. The project is still going forward.



Computational Methods for Data Analysis

The University of Washington (by Nathan Kutz), Jan 2016 – Mar 2016

Exploratory and objective data analysis methods applied to the physical, engineering, and biological sciences. A brief review of statistical methods and their computational implementation for studying time series analysis, spectral analysis, filtering methods, principal component analysis, orthogonal mode decomposition, and image processing and compression.

Medical Neuroscience

Duke University (by Leonard E. White), Aug 2015 – Dec 2015

Medical Neuroscience explores the organization and physiology of the human central nervous system. The course provides students an understanding of the essential principles of neurological function, from cellular and molecular mechanisms of neural signaling and plasticity to the organization and function of sensory and motor systems.

Machine Learning

Standford University (by Andrew Ng), Jun 2016 – Present

This course provides a broad introduction to machine learning, data mining, and statistical pattern recognition. Topics include supervised learning, unsupervised learning, and best practices in machine learning.



  • 1st place award of the IEEE Brain Initiative Brain Hackathon, Budapest, 8-9 October 2016


You can download my resume here.