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.

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.

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.

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.

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.

contact@ouroboros-neurotechnologies.com

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