Techniques and errors in measuring cross- correlation and cross-spectral density functions

Techniques and errors in measuring cross spectral density and cross correlation functions of stationary dynamic pressure dat

Adaptive spectral tracking for coherence estimation: the z-tracker

Objective: A major challenge in non-stationary signal analysis is reliable estimation of correlation. Neurophysiological recordings can be many minutes in duration with data that exhibits correlation which changes over different time scales. Local smoothing can be used to estimate time-dependency, however, an effective framework needs to adjust levels of smoothing in response to changes in correlation. Approach: Here we present a novel data-adaptive algorithm, the z-tracker, for estimating local correlation in segmented data. The algorithm constructs single segment coherence estimates using multi-taper windows. These are subject to adaptive Kalman filtering/smoothing in the z-domain to construct a local coherence estimate for each segment. The error residual for each segment determines the levels of process noise, allowing the filter to adapt rapidly to sudden changes in correlation while applying greater smoothing to data where the correlation is consistent across segments. The method is compared to wavelet coherence, calculated using orthogonal Morse wavelets. Main results: The performance of the z-tracker is quantified against Morse wavelet coherence using a mean square deviation (MSD) metric. The z-tracker has significantly lower MSD than the wavelet estimate for time-varying coherence over long time scales (∼10–20 s), whereas the wavelet has lower MSD for coherence varying over short time scales (∼1–2 s). The z-tracker also has a lower MSD for slowly varying coherence with occasional step changes. The method is applied to detect changes in coherence in paired LFP recordings from rat prefrontal cortex and amygdala in response to a pharmacological challenge. Significance: The z-tracker provides an effective and efficient method to estimate time varying correlation in multivariate data, leading to better characterisation of neurophysiology signals where correlation is subject to slow modulation over time. A number of suggestions are included for future refinements

Collecting Psycholinguistic Response Time Data Using Amazon Mechanical Turk

Researchers in linguistics and related fields have recently begun exploiting online crowd-sourcing tools, like Amazon Mechanical Turk (AMT), to gather behavioral data. While this method has been successfully validated for various offline measures--grammaticality judgment or other forced-choice tasks--its use for mainstream psycholinguistic research remains limited. This is because psycholinguistic effects are often dependent on relatively small differences in response times, and there remains some doubt as to whether precise timing measurements can be gathered over the web. Here we show that three classic psycholinguistic effects can in fact be replicated using AMT in combination with open-source software for gathering response times client-side. Specifically, we find reliable effects of subject definiteness, filler-gap dependency processing, and agreement attraction in self-paced reading tasks using approximately the same numbers of participants and/or trials as similar laboratory studies. Our results suggest that psycholinguists can and should be taking advantage of AMT and similar online crowd-sourcing marketplaces as a fast, low-resource alternative to traditional laboratory research

Acoustic monitoring of changes in well-defined hyaluronan layers exposed to chondrocytes.

The interaction of human-derived chondrocytes and thin hyaluronan layers was studied using the quartz crystal microbalance with dissipation (QCM-D) technique combined with light microscopy. This approach allowed unique real-time monitoring of the interface between the cells and the sensor surface. Our results suggest that the hyaluronan layer is rapidly degraded by chondrocytes.peerReviewe