Short-window spectral analysis using AMVAR and multitaper methods: a comparison

Nalatore, Hariharan; Rangarajan, Govindan

doi:10.1007/s00422-009-0318-5

Cited by 9 publications

(4 citation statements)

References 19 publications

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“…As our results in human EEG suggest, STOK is also a promising tool for parametric time-varying power spectrum density estimation [ 111 ], as it is less affected by the choice of the model order compared to other parametric approaches [ 98 ]. Therefore, because of its ability to track fast temporal dynamics while maintaining high frequency specificity, STOK may be preferred for PSD analysis over non-parametric methods affected by the trade-off between temporal and frequency resolution [ 69 ].…”

Section: Discussionmentioning

confidence: 99%

Modeling time-varying brain networks with a self-tuning optimized Kalman filter

2020

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Brain networks are complex dynamical systems in which directed interactions between different areas evolve at the sub-second scale of sensory, cognitive and motor processes. Due to the highly non-stationary nature of neural signals and their unknown noise components, however, modeling dynamic brain networks has remained one of the major challenges in contemporary neuroscience. Here, we present a new algorithm based on an innovative formulation of the Kalman filter that is optimized for tracking rapidly evolving patterns of directed functional connectivity under unknown noise conditions. The Self-Tuning Optimized Kalman filter (STOK) is a novel adaptive filter that embeds a self-tuning memory decay and a recursive regularization to guarantee high network tracking accuracy, temporal precision and robustness to noise. To validate the proposed algorithm, we performed an extensive comparison against the classical Kalman filter, in both realistic surrogate networks and real electroencephalography (EEG) data. In both simulations and real data, we show that the STOK filter estimates time-frequency patterns of directed connectivity with significantly superior performance. The advantages of the STOK filter were even clearer in real EEG data, where the algorithm recovered latent structures of dynamic connectivity from epicranial EEG recordings in rats and human visual evoked potentials, in excellent agreement with known physiology. These results establish the STOK filter as a powerful tool for modeling dynamic network structures in biological systems, with the potential to yield new insights into the rapid evolution of network states from which brain functions emerge.

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Section: Discussionmentioning

confidence: 99%

Modeling time-varying brain networks with a self-tuning optimized Kalman filter

2020

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“…AR spectral analysis involves application of Fourier-based techniques to an AR model rather than directly to the LFP data. These techniques were utilized instead of direct-data Fourier-based techniques since the spectral resolution of the latter is not sufficient for the short time window analyzed 23 , whereas the spectral resolution and minimal frequency that may be resolved by the AR approach are not limited by the data period that is analyzed 24 , 25 . Supplementary Figure S1 provides a detailed simulation demonstrating this advantage of AR modeling.…”

Section: Methodsmentioning

confidence: 99%

Top-down beta oscillatory signaling conveys behavioral context in early visual cortex

Richter

Coppola

Bressler

2018

Sci Rep

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Top-down modulation of sensory processing is a critical neural mechanism subserving numerous important cognitive roles, one of which may be to inform lower-order sensory systems of the current ‘task at hand’ by conveying behavioral context to these systems. Accumulating evidence indicates that top-down cortical influences are carried by directed interareal synchronization of oscillatory neuronal populations, with recent results pointing to beta-frequency oscillations as particularly important for top-down processing. However, it remains to be determined if top-down beta-frequency oscillations indeed convey behavioral context. We measured spectral Granger Causality (sGC) using local field potentials recorded from microelectrodes chronically implanted in visual areas V1/V2, V4, and TEO of two rhesus macaque monkeys, and applied multivariate pattern analysis to the spatial patterns of top-down sGC. We decoded behavioral context by discriminating patterns of top-down (V4/TEO-to-V1/V2) beta-peak sGC for two different task rules governing correct responses to identical visual stimuli. The results indicate that top-down directed influences are carried to visual cortex by beta oscillations, and differentiate task demands even before visual stimulus processing. They suggest that top-down beta-frequency oscillatory processes coordinate processing of sensory information by conveying global knowledge states to early levels of the sensory cortical hierarchy independently of bottom-up stimulus-driven processing.

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“…The most commonly used parametric spectral estimation technique is based on autoregressive (AR) modeling (Astolfi et al, 2008; Schloegl et al, 2006; Seth et al, 2011). Although spectra computed from AR-models can potentially result in higher time and/or frequency resolution (Nalatore and Rangarajan, 2009) they are sensitive to user-defined parameters such as the model order.…”

Section: Spectral Analysismentioning

confidence: 99%

Good practice for conducting and reporting MEG research

et al. 2013

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Magnetoencephalographic (MEG) recordings are a rich source of information about the neural dynamics underlying cognitive processes in the brain, with excellent temporal and good spatial resolution. In recent years there have been considerable advances in MEG hardware developments and methods. Sophisticated analysis techniques are now routinely applied and continuously improved, leading to fascinating insights into the intricate dynamics of neural processes. However, the rapidly increasing level of complexity of the different steps in a MEG study make it difficult for novices, and sometimes even for experts, to stay aware of possible limitations and caveats. Furthermore, the complexity of MEG data acquisition and data analysis requires special attention when describing MEG studies in publications, in order to facilitate interpretation and reproduction of the results. This manuscript aims at making recommendations for a number of important data acquisition and data analysis steps and suggests details that should be specified in manuscripts reporting MEG studies. These recommendations will hopefully serve as guidelines that help to strengthen the position of the MEG research community within the field of neuroscience, and may foster discussion in order to further enhance the quality and impact of MEG research.

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Short-window spectral analysis using AMVAR and multitaper methods: a comparison

Cited by 9 publications

References 19 publications

Modeling time-varying brain networks with a self-tuning optimized Kalman filter

Modeling time-varying brain networks with a self-tuning optimized Kalman filter

Top-down beta oscillatory signaling conveys behavioral context in early visual cortex

Good practice for conducting and reporting MEG research

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