BERT Learns From Electroencephalograms About Parkinson's Disease: Transformer-Based Models for Aid Diagnosis.

Medicine is a complex field with highly trained specialists with extensive knowledge that continuously needs updating. Among them all, those who study the brain can perform complex tasks due to the structure of this organ. There are neurological diseases such as degenerative ones whose diagnoses are essential in very early stages. Parkinson’s disease is one of them, usually having a confirmed diagnosis when it is already very developed. Some physicians have proposed using electroencephalograms as a non-invasive method for a prompt diagnosis. The problem with these tests is that data analysis relies on the clinical eye of a very experienced professional, which entails situations that escape human perception. This research proposes the use of deep learning techniques in combination with electroencephalograms to develop a non-invasive method for Parkinson’s disease diagnosis. These models have demonstrated their good performance in managing massive amounts of data. Our main contribution is to apply models from the field of Natural Language Processing, particularly an adaptation of BERT models, for being the last milestone in the area. This model choice is due to the similarity between texts and electroencephalograms that can be processed as data sequences. Results show that the best model uses electroencephalograms of 64 channels from people without resting states and finger-tapping tasks. In terms of metrics, the model has values around 86%.post-print1134 K

Neural network analysis of electroencephalograms based on their graphical representation

The article is devoted to the problem of recognition of motor imagery based on electroencephalogram (EEG) signals, which is associated with many difficulties, such as the physical and mental state of a person, measurement accuracy, etc. Artificial neural networks are a good tool in solving this class of problems. Electroencephalograms are time signals, Gramian Angular Fields (GAF) and Markov Transition Field (MTF) transformations are used to represent time series as images. The paper shows the possibility of using GAF and MTF EEG signal transforms for recognizing motor patterns, which is further applicable, for example, in building a brain-computer interface

Self-Organized Criticality model for Brain Plasticity

Networks of living neurons exhibit an avalanche mode of activity, experimentally found in organotypic cultures. Here we present a model based on self-organized criticality and taking into account brain plasticity, which is able to reproduce the spectrum of electroencephalograms (EEG). The model consists in an electrical network with threshold firing and activity-dependent synapse strenghts. The system exhibits an avalanche activity power law distributed. The analysis of the power spectra of the electrical signal reproduces very robustly the power law behaviour with the exponent 0.8, experimentally measured in EEG spectra. The same value of the exponent is found on small-world lattices and for leaky neurons, indicating that universality holds for a wide class of brain models.Comment: 4 pages, 3 figure