A Deep Learning Model for Correlation Analysis between Electroencephalography Signal and Speech Stimuli.

ABSTRACT: In recent years, the use of electroencephalography (EEG) has grown as a tool for diagnostic and brain function monitoring, being a simple and non-invasive method compared with other procedures like histological sampling. Typically, in order to extract functional brain responses from EEG signals, prolonged and repeated stimuli are needed because of the artifacts generated in recordings which adversely impact the stimulus-response analysis. To mitigate the artifact effect, correlation analysis (CA) methods are applied in the literature, where the predominant approaches focus on enhancing stimulus-response correlations through the use of linear analysis methods like canonical correlation analysis (CCA). This paper introduces a novel CA framework based on a neural network with a loss function

SUBMITTER: Alessandrini M 

PROVIDER: S-EPMC10575037 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature