用短窗卷积与Mamba模型解码脑电波，判断听觉注意力方向。
SWIM: Short-Window CNN Integrated with Mamba for EEG-Based Auditory Spatial Attention Decoding
- 短窗CNN提取瞬时脑电特征，结合数据增强和多任务训练
- 引入Mamba建模长期依赖，准确率达86.2%，误差降低31%
- 首次将Mamba用于听觉空间注意解码，适合脑机接口研究者
在复杂听觉环境中，人类能聚焦于特定说话者而忽略其他声音。本研究提出SWIM模型，由短窗卷积神经网络（SW$_\text{CNN}$）与Mamba序列模型组成，无需依赖语音包络即可从脑电图（EEG）信号中解码听觉注意力位置（左或右）。SW$_\text{CNN}$作为短期特征提取器，在KUL数据集的留一说话人测试中达到84.9%准确率，得益于改进的CNN结构、数据增强、多任务训练和模型集成。第二部分Mamba首次应用于该任务，捕捉前序时间步的长期依赖关系。通过联合训练，SWIM融合短时与长时信息，准确率达到86.2%，相比此前最优结果相对减少31.0%分类错误。源代码已公开于https://github.com/windowso/SWIM-ASAD。
原文摘要 · Abstract (English)
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 (SW$_\text{CNN}$), 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 SW$_\text{CNN}$ time steps. By joint training SW$_\text{CNN}$ 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. The source code is available at https://github.com/windowso/SWIM-ASAD.
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