Ouroboros Neurotechnologies

We develop biofeedback, neurofeedback and cognitive training software, using data science, machine learning, deep learning and artificial intelligence. We build on our neuroscience expertise, our practical knowledge in biofeedback, neurofeedback and cognitive training, and a modern technology stack. Our objective: finding original solutions to complex problems related to human brain data, and contribute to a responsible and socially useful development of artificial intelligence.

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Data Science, Machine Learning and Deep Learning

We apply data science, machine learning and deep learning tools to physiological (heart rate, breath, galvanic skin response, EMG), neurological (fMRI, EEG) and psychometric data, in order to develop biofeedback, neurofeedback and cognitive training software.

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Open Source Projects

Our open source projects Ouroboros fMRI, Ouroboros EEG-fMRI and Ouroboros EEG-fMRI NF, described below, are available on GitHub. For these projects, we use open access brain data in standard format, published in platforms such as NeuroVault and OpenNeuro.

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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).

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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 Web application development part, 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.

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.