Decoding Brain Signals from Rapid-Event EEG for Visual Analysis Using Deep Learning.

ABSTRACT: The perception and recognition of objects around us empower environmental interaction. Harnessing the brain's signals to achieve this objective has consistently posed difficulties. Researchers are exploring whether the poor accuracy in this field is a result of the design of the temporal stimulation (block versus rapid event) or the inherent complexity of electroencephalogram (EEG) signals. Decoding perceptive signal responses in subjects has become increasingly complex due to high noise levels and the complex nature of brain activities. EEG signals have high temporal resolution and are non-stationary signals, i.e., their mean and variance vary overtime. This study aims to develop a deep learning model for the decoding of subjects' responses to rapid-event visual stimuli and highlights the

SUBMITTER: Rehman M 

PROVIDER: S-EPMC11548637 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature