Auditory Attention

Auditory attention research focuses on understanding how the brain selectively processes sounds in complex environments and decoding this selective attention from brain activity, primarily using electroencephalography (EEG). Current research heavily utilizes deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs like LSTMs), and attention mechanisms, often integrated into hybrid architectures (e.g., CNN-SNN) to improve accuracy and efficiency in identifying attended speakers or sound sources from EEG signals. This field is significant for advancing our understanding of auditory processing and has direct implications for developing improved hearing aids, brain-computer interfaces, and other assistive technologies that can enhance selective listening capabilities.