The use of Multi-domain Electroencephalogram Representations in the building of Models based on Convolutional and Recurrent Neural Networks for Epilepsy Detection

This important role has led researchers to develop various methods for gathering information about brain activity, resulting in significant advancements in medical signal and image acquisition systems [2]. Among these advancements are functional neuroimaging techniques, such as functional magnetic resonance imaging, magnetoencephalography (MEG), positron emission tomography (PET), and electroencephalography [2]. Among these techniques, electroencephalography stands out due to three key advantages: it is a non-invasive method that allows data generation from any individual, has excellent temporal resolution--effectively capturing events occurring within milliseconds--and is relatively cost-effective compared to other examinations [3]. Electroencephalography monitors the brain's electrical activity through electrodes placed on the scalp, and the resulting data, known as the electroencephalogram (EEG), consists of a time series of electrical potentials that reflect neurological activity [4]. The EEG signal is widely used in the field of neuroscience and has the potential to advance brain-computer interfaces [5], facilitate emotion detection [6], enable classification of sleep stages [7] and help clinicians and researchers in identifying brain diseases, including but not limited to Alzheimer's disease [8], dyslexia [9], schizophrenia [10], Creutzfeldt-Jakob disease [11] and cognitive impairment [12]. Epilepsy, for example, is a neurological disorder characterized by abnormal brain activity that can lead to seizures, unusual behaviors, or even loss of consciousness.