Reduced Semantic Context and Signal-to-Noise Ratio Increase Listening Effort As Measured Using Functional Near-Infrared Spectroscopy.

OBJECTIVES: Understanding speech-in-noise can be highly effortful. Decreasing the signal-to-noise ratio (SNR) of speech increases listening effort, but it is relatively unclear if decreasing the level of semantic context does as well. The current study used functional near-infrared spectroscopy to evaluate two primary hypotheses: (1) listening effort (operationalized as oxygenation of the left lateral PFC) increases as the SNR decreases and (2) listening effort increases as context decreases. DESIGN: Twenty-eight younger adults with normal hearing completed the Revised Speech Perception in Noise Test, in which they listened to sentences and reported the final word. These sentences either had an easy SNR (+4 dB) or a hard SNR (-2 dB), and were either low in semantic context (e.g., "Tom could have thought about the sport") or high in context (e.g., "She had to vacuum the rug"). PFC oxygenation was measured throughout using functional near-infrared spectroscopy. RESULTS: Accuracy on the Revised Speech Perception in Noise Test was worse when the SNR was hard than when it was easy, and worse for sentences low in semantic context than high in context. Similarly, oxygenation across the entire PFC (including the left lateral PFC) was greater when the SNR was hard, and left lateral PFC oxygenation was greater when context was low. CONCLUSIONS: These results suggest that activation of the left lateral PFC (interpreted here as reflecting listening effort) increases to compensate for acoustic and linguistic challenges. This may reflect the increased engagement of domain-general and domain-specific processes subserved by the dorsolateral prefrontal cortex (e.g., cognitive control) and inferior frontal gyrus (e.g., predicting the sensory consequences of articulatory gestures), respectively.

A Singular Spectrum Analysis-Based Data-Driven Technique for the Removal of Cardiogenic Oscillations in Esophageal Pressure Signals.

Objective: Assessing the respiratory and lung mechanics of the patients in intensive care units is of utmost need in order to guide the management of ventilation support. The esophageal pressure ([Formula: see text]) signal is a minimally invasive measure, which portrays the mechanics of the lung and the pattern of breathing. Because of the close proximity of the lung to the beating heart inside the thoracic cavity, the [Formula: see text] signals always get contaminated with that of the oscillatory-pressure-signal of the heart, which is known as the cardiogenic oscillation ([Formula: see text]) signal. However, the area of research addressing the removal of [Formula: see text] from [Formula: see text] signal is still lagging behind. Methods and results: This paper presents a singular spectrum analysis-based high-efficient, adaptive and robust technique for the removal of [Formula: see text] from [Formula: see text] signal utilizing the inherent periodicity and morphological property of the [Formula: see text] signal. The performance of the proposed technique is tested on [Formula: see text] signals collected from the patients admitted to the intensive care unit, cadavers, and also on synthetic [Formula: see text] signals. The efficiency of the proposed technique in removing [Formula: see text] from the [Formula: see text] signal is quantified through both qualitative and quantitative measures, and the mean opinion scores of the denoised [Formula: see text] signal fall under the categories 'very good' as per the subjective measure. Conclusion and clinical impact: The proposed technique: (1) does not follow any predefined mathematical model and hence, it is data-driven, (2) is adaptive to the sampling rate, and (3) can be adapted for denoising other biomedical signals which exhibit periodic or quasi-periodic nature.