ArticleJournal of neuroscience methods2009
Measuring instantaneous frequency of local field potential oscillations using the Kalman smoother.
Article in Journal of neuroscience methods, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Model-Based Electroencephalogram Instantaneous Frequency Tracking: Application in Automated Sleep-Wake Stage Classification.Sensors (Basel, Switzerland) · 2024Article
- Subthalamic nucleus deep brain stimulation driven by primary motor cortex γ2 activity in parkinsonian monkeys.Scientific reports · 2022Article
- Modeling multiscale causal interactions between spiking and field potential signals during behavior.Journal of neural engineering · 2022Article
- Article
- UKF-based closed loop iterative learning control of epileptiform wave in a neural mass model.Cognitive neurodynamics · 2015Article
- Adaptive Parametric Spectral Estimation with Kalman Smoothing for Online Early Seizure Detection.International IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering · 2013Article
- Head direction cells in the postsubiculum do not show replay of prior waking sequences during sleep.Hippocampus · 2012Article
- Emergent dynamics of fast ripples in the epileptic hippocampus.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2010Article
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Authors and funding
4 authors.
Funding
Abstract
Rhythmic local field potentials (LFPs) arise from coordinated neural activity. Inference of neural function based on the properties of brain rhythms remains a challenging data analysis problem. Algorithms that characterize non-stationary rhythms with high temporal and spectral resolution may be useful for interpreting LFP activity on the timescales in which they are generated. We propose a Kalman smoother based dynamic autoregressive model for tracking the instantaneous frequency (iFreq) and frequency modulation (FM) of noisy and non-stationary sinusoids such as those found in LFP data. We verify the performance of our algorithm using simulated data with broad spectral content, and demonstrate its application using real data recorded from behavioral learning experiments. In analyses of ripple oscillations (100-250Hz) recorded from the rodent hippocampus, our algorithm identified novel repetitive, short timescale frequency dynamics. Our results suggest that iFreq and FM may be useful measures for the quantification of small timescale LFP dynamics.
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Registered trials
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