Electroencephalography Based Auditory Attention

Electroencephalography (EEG)-based auditory attention decoding aims to identify which sound source a person is focusing on by analyzing their brainwaves. Recent research heavily utilizes deep learning models, including convolutional neural networks (CNNs), temporal attention networks (TANets), and hybrid CNN-spiking neural network architectures, to improve the accuracy and speed of attention detection, often focusing on minimizing the required decision window. This work is significant for its potential to improve assistive technologies like smart hearing aids by enabling more effective noise cancellation and personalized sound amplification based on the user's attentional focus. Furthermore, these studies are advancing our understanding of the neural mechanisms underlying auditory attention.