Adverse listening conditions increase the demand on cognitive resources needed for speech comprehension. In an exploratory study, we aimed to identify independent power spectral features in the EEG useful for studying the cognitive processes involved in this effortful listening. Listeners performed the coordinate response measure task with a single-talker masker at a 0-dB signal-to-noise ratio. Sounds were left unfiltered or degraded with low-pass filtering. Independent component analysis (ICA) was used to identify independent components (ICs) in the EEG data, the power spectral dynamics of which were then analyzed. Frontal midline theta, left frontal, right frontal, left mu, right mu, left temporal, parietal, left occipital, central occipital, and right occipital clusters of ICs were identified. All IC clusters showed some significant listening-related changes in their power spectrum. This included sustained theta enhancements, gamma enhancements, alpha enhancements, alpha suppression, beta enhancements, and mu rhythm suppression. Several of these effects were absent or negligible using traditional channel analyses. Comparison of filtered to unfiltered speech revealed a stronger alpha suppression in the parietal and central occipital clusters of ICs for the filtered speech condition. This not only replicates recent findings showing greater alpha suppression as listening difficulty increases but also suggests that such alpha-band effects can stem from multiple cortical sources. We lay out the advantages of the ICA approach over the restrictive analyses that have been used as of late in the study of listening effort. We also make suggestions for moving into hypothesis-driven studies regarding the power spectral features that were revealed.

中文翻译：

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与在不利条件下收听相关的脑电图功率谱动态

不利的听力条件增加了对语音理解所需的认知资源的需求。在一项探索性研究中，我们旨在识别脑电图中的独立功率谱特征，这些特征对于研究这种努力聆听所涉及的认知过程很有用。听众使用单说话者掩蔽器以 0-dB 的信噪比执行坐标响应测量任务。声音未被过滤或使用低通滤波降级。独立分量分析（ICA）用于识别脑电数据中的独立分量（IC），然后分析其功率谱动态。额中线 theta、左额、右额、左 mu、右 mu、左颞、顶叶、左枕骨、中央枕骨和右枕骨簇被识别。所有 IC 集群在其功率谱中都显示出一些与听力相关的显着变化。这包括持续的 theta 增强、伽马增强、α 增强、α 抑制、β 增强和 mu 节律抑制。使用传统的渠道分析，其中一些影响不存在或可以忽略不计。过滤后的语音与未过滤的语音的比较显示，在过滤后的语音条件下，IC 的顶叶和中央枕骨簇中的 alpha 抑制更强。这不仅复制了最近的研究结果，显示随着听力难度的增加，α 抑制会更大，而且还表明这种 α 波段效应可能源于多个皮层来源。我们列出了 ICA 方法相对于最近在听力研究中使用的限制性分析的优势。