@inproceedings{discovery10205071,
           month = {January},
            year = {2025},
         address = {Macao},
       booktitle = {Proceedings of 2024 IEEE Spoken Language Technology Workshop, SLT 2024},
            note = {This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.},
           title = {SWIM: Short-Window CNN Integrated With Mamba for EEG-Based Auditory Spatial Attention Decoding},
           pages = {1031--1038},
       publisher = {IEEE},
         journal = {Proceedings of 2024 IEEE Spoken Language Technology Workshop, SLT 2024},
        abstract = {In complex auditory environments, the human auditory system possesses the remarkable ability to focus on a specific speaker while disregarding others. In this study, a new model named SWIM, a short-window convolution neural network (CNN) integrated with Mamba, is proposed for identifying the locus of auditory attention (left or right) from electroencephalography (EEG) signals without relying on speech envelopes. SWIM consists of two parts. The first is a short-window CNN (SWCNN), which acts as a short-term EEG feature extractor and achieves a final accuracy of 84.9\% in the leave-one-speaker-out setup on the widely used KUL dataset. This improvement is due to the use of an improved CNN structure, data augmentation, multitask training, and model combination. The second part, Mamba, is a sequence model first applied to auditory spatial attention decoding to leverage the long-term dependency from previous SWCNN time steps. By joint training SWCNN and Mamba, the proposed SWIM structure uses both short-term and long-term information and achieves an accuracy of 86.2\%, which reduces the classification errors by a relative 31.0\% compared to the previous state-of-the-art result.},
        keywords = {Training, Accuracy, Source coding, Benchmark testing, Brain modeling, Feature extraction, Data augmentation, Electroencephalography, Data models, Decoding},
             url = {https://doi.org/10.1109/slt61566.2024.10832311},
          author = {Zhang, Z and Thwaites, A and Woolgar, A and Moore, B and Zhang, C}
}