Detection of Healthy and Unhealthy Brain States from Local Field Potentials Using Machine Learning

Neural signals are the recordings of the electrical activity individual or groups of neurons, and they are used for disease staging, brain-computer interface control and understanding the neural processes. When carrying out a functional connectivity study in rodents, processing must be done to eliminate disturbance in the data in order to have the most faithful representation of the neural activity. This step mainly includes filtering and artefact removal, where the latter can be approached by diverse methods. Furthermore, it is important to identify when the rodent is stressed, as the local field potentials can be coupled to theta oscillations. To this end, we set out to develop a machine learning-based model for the detection of stress in rodents with multi-modal recordings, namely local field potentials, respiration and electrocardiography. We explore subject-specific and cross-subject models, as well as employing an artefact detection model as a generic anomaly detector. Results show that subject-specific models can achieve a good performance, but the variability is significant across all three signals among rodents of the same age, gender and species.