Applying machine learning EEG signal classification to emotion‑related brain anticipatory activity.

ABSTRACT: Machine learning approaches have been fruitfully applied to several neurophysiological signal classification problems. Considering the relevance of emotion in human cognition and behaviour, an important application of machine learning has been found in the field of emotion identification based on neurophysiological activity. Nonetheless, there is high variability in results in the literature depending on the neuronal activity measurement, the signal features and the classifier type. The present work aims to provide new methodological insight into machine learning applied to emotion identification based on electrophysiological brain activity. For this reason, we analysed previously recorded EEG activity measured while emotional stimuli, high and low arousal (auditory and visual) were provid

SUBMITTER: Bilucaglia M 

PROVIDER: S-EPMC10603316 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature