Ouroboros Neurotechnologies

We develop

Data Science, Machine Learning, and Deep Learning

We apply data science, machine learning, and deep learning tools to physiological and neurological (fMRI, EEG) data, in order to develop biofeedback and neurofeedback software.

Open Source Projects

For our open source projects Ouroboros fMRI, Ouroboros EEG-fMRI, Ouroboros EEG-fMRI NF, and Neuropolis, we use open access brain data from NeuroVault and OpenNeuro.

Neuroscience Expertise

We build on our neuroscience expertise in the fields of decision-making, learning, reasoning, planning, and strategy, as demonstrated through a high-level publication in the journal Science. Our technology is also based on the practical knowledge in biofeedback, neurofeedback, and cognitive training acquired after founding and directing the Institut Lémanique du Cerveau (Lemanic Brain Institute).

Technology Stack

We mostly use Python and its data science, machine learning, and deep learning libraries, in particular NumPy, Pandas, Scikit-Learn and TensorFlow. For the API, we rely on Django.

Ouroboros fMRI

This project uses data science (NumPy, Pandas, Matplotlib), machine learning (Scikit-Learn) and deep learning (TensorFlow) tools on statistical maps of the human brain obtained with functional Magnetic Resonance Imaging (fMRI). Our objective is to train machine learning models to recognize and predict brain activity, using a dataset from the open data repository NeuroVault. Specifically, we use machine learning models to classify brain images, and to predict the values of voxels inside these brain images. Our results show that both classification and regression are possible, suggesting that the statistical maps obtained in this experimental setup contain relevant and generalizable knowledge about the brain activity.

Ouroboros EEG-fMRI

This project uses data science (NumPy, Pandas, Matplotlib) and machine learning (Scikit-Learn) tools on bandpowers obtained with Electroencephalography (EEG) and brain images obtained with functional Magnetic Resonance Imaging (fMRI). Our objective is to train machine learning models to predict EEG activity from fMRI activity, and vice versa, using a dataset from the open data repository OpenNeuro. Specifically, we use machine learning models with EEG predictors to predict the value of the fMRI BOLD signal, and machine learning models with fMRI predictors to predict the value of the EEG bandpowers. Our results show that both tasks are possible, suggesting that each technique provides some insight on processes that are, traditionally, in the realm of the other technique.

Ouroboros EEG-fMRI NF

This project uses data science (NumPy, Pandas, Matplotlib), machine learning (Scikit-Learn) and deep learning (TensorFlow) tools on bandpowers obtained with Electroencephalography (EEG), brain images obtained with functional Magnetic Resonance Imaging (fMRI), and Neurofeedback (NF) scores computed using both techniques. Our objective is to explore several ways to apply machine learning models to EEG-fMRI NF data, using a dataset from the open data repository OpenNeuro. Specifically, we use machine learning models to classify brain images, to predict the value of the fMRI BOLD signal and fMRI NF scores, and to predict the value of the EEG bandpowers and EEG NF scores. Our results show that some of these tasks are possible, suggesting that machine learning can be used to extract subtle patterns from EEG and fMRI data in the context of NF training.

Neuropolis

This project uses data science (NumPy, Pandas, Matplotlib), machine learning (Scikit-Learn), and deep learning (TensorFlow, Keras) tools on brain data obtained with Electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI). Our objective is to build an artificial intelligence system for human brain activity prediction, using EEG data to predict fMRI data, whether across experimental conditions or across subjects. For this regression task, we use a variety of machine learning and deep learning models, including linear regression, k-nearest neighbors, decision trees, random forests, support vector machines, fully connected neural networks, convolutional neural networks, recurrent neural networks, and transformers. Our results show that using EEG data to predict fMRI data is possible to a certain extent. Nevertheless, for our voxels of interest, the regression task seems too challenging, at least for relatively simple machine learning and deep learning models, even if the deep learning models perform well on a classification task. The methods developed in this project could provide useful insights for advancing toward a multimodal foundation model for neuroscience, and could help to improve promising brain technologies, such as neurofeedback systems and brain-computer interfaces.

Ouroboros Neurofeedback API

This project builds an API (Django, Docker) around Neuropolis. Our objective is to compute neurofeedback (NF) scores, whether directly on the Electroencephalography (EEG) activity, or indirectly on the predicted functional Magnetic Resonance Imaging (fMRI) activity. For this task, we use a series of machine learning models trained in the Neuropolis project, including linear regression, k-nearest neighbors, decision trees, random forests, and support vector machines.

Institut Lémanique du Cerveau

We build on the practical knowledge in biofeedback, neurofeedback and cognitive training acquired after founding and directing the Institut Lémanique du Cerveau (Lemanic Brain Institute), a company established in Lausanne in 2017. During several years, this institute has provided biofeedback, neurofeedback and cognitive training consultations. It has been replaced by the Institut Lémanique du Cerveau et du Système Nerveux, a competence center of the Policlinique Ostéopathique de Lausanne, managed in collaboration with Ouroboros Neurotechnologies.

Maël Donoso

Ph.D. in Cognitive and Computational Neuroscience from Université Pierre et Marie Curie, today Sorbonne Université (Paris)

Certified in Data Science and Machine Learning, and in Web Application Development, from École Polytechnique Fédérale de Lausanne

Address

Ouroboros Neurotechnologies
Place de la Riponne 5
1005 Lausanne

Email

contact@ouroboros-neurotechnologies.com

Contact

You are welcome to contact us by email or using this form:

11 + 12 =