The rhythmic oscillations embedded in the spontaneous or evoked electroencephalographic (EEG) activity are closely related to the subtle cognitive processes of the brain. This review is aimed to provide a comprehensive overview of the studies investigating brain oscillations across various frequency ranges in migraine, using EEG and evoked potentials.
The need for automatic detection and classification of high-frequency oscillations (HFOs) as biomarkers of the epileptogenic tissue is strongly felt in the clinical field. In this context, the employment of artificial intelligence methods could be the missing piece to achieve this goal. This work proposed a double-step procedure based on machine learning algorithms and tested it on an intracranial electroencephalogram (iEEG) dataset available online.
© Università degli Studi di Roma "La Sapienza" - Piazzale Aldo Moro 5, 00185 Roma
