Conversational AI has seen a growing interest among government, researchers, and
industrialists. This comprehensive survey paper provides an in-depth analysis of large …

The dynamic uncertain relationship among each brain region is a necessary factor that limits
EEG-based emotion recognition. It is a thought-provoking problem to availably employ time …

Objective: The key principle of motor imagery (MI) decoding for electroencephalogram
(EEG)-based Brain-Computer Interface (BCI) is to extract task-discriminative features from …

Humans show a remarkable ability in solving the cocktail party problem. Decoding auditory
attention from the brain signals is a major step toward the development of bionic ears …

Objective: Despite recent advances, the decoding of auditory attention from brain signals
remains a challenge. A key solution is the extraction of discriminative features from high …

Objective. Spatial auditory attention decoding (Sp-AAD) refers to the task of identifying the
direction of the speaker to which a person is attending in a multi-talker setting, based on the …

Humans possess the remarkable ability to selectively attend to a single speaker amidst
competing voices and background noise, known as selective auditory attention. Recent …

Auditory spatial attention detection (ASAD) aims to decode the attended spatial location with
EEG in a multiple-speaker setting. ASAD methods are inspired by the brain lateralization of …

Auditory attention decoding (AAD) aims to recognize the attended speaker based on
electroencephalography (EEG) signals in multi-talker environments. Most AAD methods only …

Decoding auditory attention from brain activities, such as electroencephalography (EEG),
sheds light on solving the machine cocktail party problem. However, effective representation …