用隐马尔可夫模型提升脑电听觉注意力解码的准确性
Post-processing of EEG-based Auditory Attention Decoding Decisions via Hidden Markov Models
- 利用注意力切换概率低的规律，建模脑电信号的时间动态
- 在实时与离线场景下均显著提升解码准确率
- 方法高效易用，适合实际应用场景
听觉注意力解码（AAD）算法通过脑电图（EEG）信号识别听众在多说话人环境中的关注对象。尽管现有先进算法能在短时间窗内识别关注说话人，但其预测精度仍不足以满足实际应用需求。本文提出使用隐马尔可夫模型（HMM）对AAD结果进行后处理，利用人类注意力在短时间内更可能持续关注同一说话人而非频繁切换这一特性，建模注意力的时间结构。实验表明，该方法在因果（实时）和非因果（离线）设置中均能显著提升现有算法的性能。进一步对比显示，HMM在准确性和响应速度上优于现有后处理方法，并系统分析了窗口长度、切换频率及原始算法精度对整体性能的影响。所提方法计算效率高、原理直观，适用于实时与离线场景。
原文摘要 · Abstract (English)
Auditory attention decoding (AAD) algorithms exploit brain signals, such as electroencephalography (EEG), to identify which speaker a listener is focusing on in a multi-speaker environment. While state-of-the-art AAD algorithms can identify the attended speaker on short time windows, their predictions are often too inaccurate for practical use. In this work, we propose augmenting AAD with a hidden Markov model (HMM) that models the temporal structure of attention. More specifically, the HMM relies on the fact that a subject is much less likely to switch attention than to keep attending the same speaker at any moment in time. We show how a HMM can significantly improve existing AAD algorithms in both causal (real-time) and non-causal (offline) settings. We further demonstrate that HMMs outperform existing postprocessing approaches in both accuracy and responsiveness, and explore how various factors such as window length, switching frequency, and AAD accuracy influence overall performance. The proposed method is computationally efficient, intuitive to use and applicable in both real-time and offline settings.
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