Haro S, Beauchene C, Quatieri T F, Smalt C J
Human Health & Performance Systems Group, MIT Lincoln Laboratory, Lexington, MA, USA.
Speech and Hearing Bioscience and Technology, Harvard Medical School, Boston, MA, USA.
bioRxiv. 2025 Mar 13:2025.03.13.641661. doi: 10.1101/2025.03.13.641661.
There is significant research in accurately determining the focus of a listener's attention in a multi-talker environment using auditory attention decoding (AAD) algorithms. These algorithms rely on neural signals to identify the intended speaker, assuming that these signals consistently reflect the listener's focus. However, some listeners struggle with this competing talkers task, leading to suboptimal tracking of the desired speaker due to potential interference from distractors. The goal of this study was to enhance a listener's attention to the target speaker in real time and investigate the underlying neural bases of this improvement.
This paper describes a closed-loop neurofeedback system that decodes the auditory attention of the listener in real time, utilizing data from a non-invasive, wet electroencephalography (EEG) brain-computer interface (BCI). Fluctuations in the listener's real-time attention decoding accuracy was used to provide acoustic feedback. As accuracy improved, the ignored talker in the two-talker listening scenario was attenuated; making the desired talker easier to attend to due to the improved attended talker signal-to-noise ratio (SNR). A one-hour session was divided into a 10-minute decoder training phase, with the rest of the session allocated to observing changes in neural decoding.
In this study, we found evidence of suppression of (i.e., reduction in) neural tracking of the unattended talker when comparing the first and second half of the neurofeedback session ( = 0.012). We did not find a statistically significant increase in the neural tracking of the attended talker.
These results establish a single session performance benchmark for a time-invariant, non-adaptive attended talker linear decoder utilized to extract attention from a listener integrated within a closed-loop neurofeedback system. This research lays the engineering and scientific foundation for prospective multi-session clinical trials of an auditory attention training paradigm.
在多说话者环境中，利用听觉注意力解码（AAD）算法准确确定听众注意力焦点的研究颇多。这些算法依靠神经信号来识别目标说话者，假定这些信号始终反映听众的注意力焦点。然而，一些听众在这项有多个竞争说话者的任务中存在困难，由于干扰因素的潜在干扰，导致对目标说话者的跟踪效果欠佳。本研究的目的是实时增强听众对目标说话者的注意力，并探究这种改善的潜在神经基础。
本文描述了一种闭环神经反馈系统，该系统利用来自非侵入性湿式脑电图（EEG）脑机接口（BCI）的数据实时解码听众的听觉注意力。听众实时注意力解码准确性的波动被用于提供听觉反馈。随着准确性提高，在双说话者聆听场景中被忽略的说话者的声音会减弱；由于目标说话者的信号噪声比（SNR）提高，使得目标说话者更易于被关注。一个小时的实验分为一个10分钟的解码器训练阶段，其余时间用于观察神经解码的变化。
在本研究中，当比较神经反馈实验的前半段和后半段时，我们发现了未被关注的说话者的神经跟踪受到抑制（即减少）的证据（P = 0.012）。我们没有发现被关注的说话者的神经跟踪有统计学上的显著增加。
这些结果为用于从闭环神经反馈系统中的听众提取注意力的时不变、非自适应目标说话者线性解码器建立了单次实验性能基准。这项研究为听觉注意力训练范式的前瞻性多阶段临床试验奠定了工程和科学基础。
