Victoria Peterson

PhD - Research in Brain-Computer Interfaces (BCI)

Victoria Peterson

Faculty Researcher @ IMAL-CONICET (Santa Fe, Argentina)

Associate Professor @ FIQ-UNL (Santa Fe, Argentina)

Collaborator @ Brain Modulation Lab (Boston, MGH-Harvard, US).


  • [Nov 2022] New publication. "Movement decoding using spatio-spectral features of cortical and subcortical local field potentials" @Experimental Neurology. Free access here.

  • [April 2022] Starting new position @IMAL-CONICET-UNL

  • [Sep 2021] BCI award 2021 nomination. Our BCI research has been nominated for the BCI Award 2021

  • [Aug 2021] New publication. "Transfer learning based on Optimal Transport for Motor Imagery Brain-Computer Interfaces" @TBME. Check it out here

  • [Jun 2021] MI-BCI low-cost dataset release. It is in BIDs format! You can download it @OpenNeuro

About me

I am currently a CONICET Faculty Researcher at the Instituto de Matemática Aplicada del Litoral, IMAL-UNL-CONICET, Argentina, where I lead a group on Computational Neuroengineering. We investigate decoding models for invasive and non-invasive neuroscillations with application to Brain-Computer Interfaces.

From 2021 to 2022, I worked as a Postdoc research fellow at the Brain Modulation Lab, Massachusetts General Hospital, Boston, US, and a Research Affiliate of the Harvard Medical School, under the supervision of PhD MD Mark Richardson. Before that (2019-2021) I was a Postdoc at the Instituto de Matemática Aplicada del Litoral, IMAL-UNL-CONICET, Argentina, under the supervision of Prof. Ruben Spies. I did my PhD (2014-2019) at the Instituto de Investigación en señales, sistemas e inteligencia computacional, sinc(i)-UNL-CONICET, Argetina.

I work in the development of computational solutions for brain signal decoding with application to Brain-Computer Interface (BCIs) for neurophysiological decoding. I was introduced to the BCI word research when I was doing my graduate degree thesis. I have since been deeply fascinated by BCIs, with a desire to understand the depth and beauty of the underlying mathematical models, and using the power of advanced signal processing and machine learning tools to make this technology more robust and applicable.

In 2014, driven by the desire to deepen my knowledge in machine learning and BCIs, I started my Ph.D. studies in the field of BCIs, supported by a five-year competitive fellowship awarded to me by the National Council of Scientific and Technical Research of Argentina, (CONICET). In 2017 I visited the Rehabilitation Engineering Laboratory at ETH Zurich thanks to the Doctoral Exchange Grant awarded to me by the Zeno Karl Schindler Foundation (ZKS, Switzerland).

My main research goal is to help toward the development of computer-based technologies that improve the quality of life of people with neurological disorders.


  • PhD in Engineering, 2018

Universidad Nacional del Litoral, Argentina
  • Bioengineer, 2013

Universidad Nacional de Entre Ríos, Argentina


  • Machine learning methods for brain signal decoding

  • Invasive and non-invasive neurophysiology