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The human brain can easily focus on one speaker and suppress others inscenarios such as a cocktail party. Recently, researchers found that auditoryattention can be decoded from the electroencephalogram (EEG) data. However,most existing deep learning methods are difficult to use prior knowledge ofdifferent views (that is attended speech and EEG are task-related views) andextract an unsatisfactory representation. Inspired by Broadbent's filter model,we decode auditory attention in a multi-view paradigm and extract the mostrelevant and important information utilizing the missing view. Specifically, wepropose an auditory attention decoding (AAD) method based on multi-view VAEwith task-related multi-view contrastive (TMC) learning. Employing TMC learningin multi-view VAE can utilize the missing view to accumulate prior knowledge ofdifferent views into the fusion of representation, and extract the approximatetask-related representation. We examine our method on two popular AAD datasets,and demonstrate the superiority of our method by comparing it to thestate-of-the-art method.

Auditory Attention Decoding with Task-Related Multi-View Contrastive Learning