A DenseNet-based method for decoding auditory spatial attention with EEG
This work addresses auditory attention decoding for brain-computer interfaces, but it is incremental as it builds on existing methods with a novel data transformation and network architecture.
The paper tackled decoding auditory spatial attention from EEG in multi-speaker settings by transforming EEG channels into a 2D spatial map and using a 3D DenseNet, achieving 94.3% accuracy compared to the previous SOTA of 90.6% on the KUL dataset with a 1-second window.
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 cortical neural responses during the processing of auditory spatial attention, and show promising performance for the task of auditory attention decoding (AAD) with neural recordings. In the previous ASAD methods, the spatial distribution of EEG electrodes is not fully exploited, which may limit the performance of these methods. In the present work, by transforming the original EEG channels into a two-dimensional (2D) spatial topological map, the EEG data is transformed into a three-dimensional (3D) arrangement containing spatial-temporal information. And then a 3D deep convolutional neural network (DenseNet-3D) is used to extract temporal and spatial features of the neural representation for the attended locations. The results show that the proposed method achieves higher decoding accuracy than the state-of-the-art (SOTA) method (94.3% compared to XANet's 90.6%) with 1-second decision window for the widely used KULeuven (KUL) dataset, and the code to implement our work is available on Github: https://github.com/xuxiran/ASAD_DenseNet