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Low-cost BCI Systems for Motor Rehabilitation

Versión en español disponible aquí.

This is an update from the OpenBCI Discovery Program. Click here for information on how to apply.

At OpenBCI we are proud to partner with the Instituto de Matemática Aplicada del Litoral, IMAL-UNL-CONICET, in Santa Fe, Argentina, to help develop a motor rehabilitation system based on a closed-loop, low-cost Brain Computer Interface (BCI).

We interviewed Dr. Victoria Peterson, project director and postdoctoral fellow at the Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), to learn more about this incredible project. In this article we present a summary of the interview.

Dr. Victoria Peterson with the Ultracortex “Mark III” EEG Headset.

What is the project about?

Nowadays, strokes are one of the leading causes of physical disability worldwide and despite rehabilitation efforts, 50% of survivors live with chronic motor and/or cognitive disabilities. Our team of researchers in Santa Fe, Argentina, integrated by Victoria Peterson, Ruben D. Spies, Diego H. Milone, Catalina M. Galván, Nicolás Nieto, Lilian Celeste Alarcón Segovia e Ignacio Rintoul, is investigating the feasibility of using low-cost Brain Computer Interfaces (BCIs) as an alternative rehabilitation tool for stroke survivors. 

We are looking to validate the concept and offer this tool as a complement to traditional therapy that patients can use at home in a safe and systematic way. The system would offer patients the ability to command an exoskeleton, robotic arm, or other structure through their brain activity. The BCI would detect the patient’s intention to do an action and would get the robotic structure to execute it, offering both visual and somatosensory feedback to the patient about the correct recognition of the intention. By combining traditional therapies with this system, the patient could participate both passively and actively in their rehabilitation process, leading to a more effective therapy.

Creating such a system will require the development of robust and adaptive algorithms to face known problems in the field of BCIs such as the high variability in human data and the noise present in brain activity signals obtained non-invasively. We want to create a low cost co-adaptive BCI in which both the system and the patient learn from each other across sessions.

Experimentation Protocol Diagram.

How will you use OpenBCI’s equipment?

Once the simulations have proven the correct functioning of the models, we will use OpenBCI equipment to record brain activity data as well as to perform tests in real time. The equipment used will be the Cyton + Daisy boards, the Ganglion board, the WiFi Shield, and the EEG Electrode Cap Kit.

Obtaining equipment for research in this field is difficult in Argentina and having access to the WiFi Shield together with the EEG Electrode Cap Kit will allow us to increase the sampling frequency and to provide study participants with a headset that has a chin strap, which contributes to the reduction of noise. In addition, the Ganglion board will allow us to obtain electromyography (EMG) data to ensure the patient does not make any movement when they are not supposed to.

The system will first be tested on people without any motor dysfunction, who will be asked to imagine a movement using a technique known as motor imagery or mental movement practice. For each experiment, participants will be required to attend the laboratory at least 4 different days. The first day will be devoted to data acquisition to calibrate the model. During the following sessions they will collect feedback to recalibrate the system and train the users.

Use of OpenBCI technologies at IMAL laboratories.

What is the impact of this work?

The long-term vision is to develop a tool that is complementary to traditional rehabilitation therapy and can easily be used by stroke patients at home to support and enhance their rehabilitation process. There are currently no clinically validated systems of this nature. The objective of this project is to improve the practicality of BCI tools and contribute to the advancement of the field towards the approval and validation of systems like this one.

We are confident that this project will have a positive impact in the field of neurotechnology both in Argentina and in other countries in the region that present difficulties in acquiring equipment for research like this.

Who is involved in this project?

Members of the research group. From left to right, Ignacio Rintoul, Lilian Alarcón, Rubén Spies, Victoria Peterson, Diego Milone, Catalina Galván and Nicolás Nieto.

Our team is integrated by:

Dr. Victoria Peterson: Postdoctoral fellow at the CONICET at the Instituto de Matemática Aplicada del Litoral. In 2013 she obtained her B.S degree in Bioengineering from the Universidad de Entre Ríos, Argentina, and in 2018 she obtained the Ph.D degree from the Universidad Nacional del Litoral (UNL, Argentina).

Prof. Dr. Ruben D. Spies: Principal Investigator of CONICET and Full Professor at the Universidad Nacional del Litoral (UNL, Argentina). He obtained his B.S degree in Applied Mathematics from the UNL, and his M.S and Ph.D degrees from the Virginia Polytechnic Institute and State University, Virginia, USA, in 1989 and 1992, respectively.

Prof. Dr. Diego H. Milone: ​​He received his Bioengineering degree (Hons.) from the Universidad Nacional de Entre Ríos in 1998 and his Ph.D in Microelectronics and Computer Architectures from the Universidad de Granada, Spain in 2003. He is Full Professor at the Universidad Nacional del Litoral (UNL , Argentina) and Principal Investigator of the CONICET. He is currently the Director of the Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional, sync (i)-UNL-CONICET, Argentina.

Eng. Nicolás Nieto: Postdoctoral fellow at the CONICET at the Universidad Nacional del Litoral (UNL, Argentina) with the work carried out at the Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional, sinc(i)-UNL-CONICET. Obtained his Biomedical Engineering degree from the Universidad Nacional de Córdoba. 

Bioeng. Catalina M. Galván: Doctoral student at the Universidad Nacional del Litoral (UNL, Argentina). She currently has a Ph.D scholarship awarded by the CONICET  at the Instituto de Matemática Aplicada del Litoral. She obtained her B.S degree in Bioengineering from the Universidad Nacional de Entre Ríos.

Dr. Lilian Celeste Alarcón Segovia: Postdoctoral fellow at the CONICET at the Instituto de Matemática Aplicada del Litoral. She is an Industrial Engineer from the Universidad Católica Nuestra Señora de la Asunción, Paraguay. She completed her Ph.D in Biological Sciences at the Facultad de Bioquímica y Ciencias Biológicas at the  Universidad Nacional del Litoral (UNL, Argentina), with the work carried out at the Instituto de Desarrollo Tecnológico para la Industria Química. She worked as a visiting predoctoral fellow at the Center for Bio-Integrated Electronics at Northwestern University, Illinois, USA.

Prof. Dr. Ignacio Rintoul: Doctor of Science from the Swiss Federal Institute of Technology Lausanne, Switzerland. Materials Engineering Diploma from the Instituto Sabato de la Universidad Nacional de San Martín and the National Committee for Atomic Energy CNEA. Independent researcher at the Group of Advanced Materials and Energy of the Institute for Research and Development of the Chemical Industry – CONICET, Argentina, and Associate Professor at the Faculty of Chemical Engineering – UNL, Argentina.

What is the timeline for the next few months?

We are currently developing adaptive algorithms. The next step will be to collect simulated data with variability and perform tests to determine the validity of the hypothesis proposed. Once it has been proven, it will be tested on healthy people before testing on patients. Based on the results, we will design studies to improve the model’s adaptability.

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