A Comparison of Regularization Methods in Forward and Backward Models for Auditory Attention Decoding.

ABSTRACT: The decoding of selective auditory attention from noninvasive electroencephalogram (EEG) data is of interest in brain computer interface and auditory perception research. The current state-of-the-art approaches for decoding the attentional selection of listeners are based on linear mappings between features of sound streams and EEG responses (forward model), or vice versa (backward model). It has been shown that when the envelope of attended speech and EEG responses are used to derive such mapping functions, the model estimates can be used to discriminate between attended and unattended talkers. However, the predictive/reconstructive performance of the models is dependent on how the model parameters are estimated. There exist a number of model estimation methods that have been published, a

SUBMITTER: Wong DDE 

PROVIDER: S-EPMC6090837 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature