Real-Time Brain Oscillation Detection and Phase-Locked Stimulation Using Autoregressive Spectral Estimation and Time-Series Forward Prediction

Neural oscillations are important features in a working central nervous system, facilitating efficient communication across large networks of neurons. They are implicated in a diverse range of processes such as synchronization and synaptic plasticity, and can be seen in a variety of cognitive proces...

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Bibliographic Details
Published in:IEEE transactions on biomedical engineering 2013-03, Vol.60 (3), p.753-762
Main Authors: Chen, L. Leon, Madhavan, Radhika, Rapoport, Benjamin I., Anderson, William S.
Format: Article
Language:English
Subjects:
Algorithms
Autoregressive (AR) model
Brain - physiology
Brain modeling
closed-loop stimulation
Electrodes
Electroencephalography
Electroencephalography - methods
Epilepsy
genetic algorithm
Humans
intracranial EEG(iEEG)
Mathematical model
Memory - physiology
Models, Neurological
neural oscillations
Oscillators
phase-locking
real time
Real time systems
Regression Analysis
Reproducibility of Results
Signal Processing, Computer-Assisted
Task Performance and Analysis
theta rhythm
Theta Rhythm - physiology
Time frequency analysis
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Summary:Neural oscillations are important features in a working central nervous system, facilitating efficient communication across large networks of neurons. They are implicated in a diverse range of processes such as synchronization and synaptic plasticity, and can be seen in a variety of cognitive processes. For example, hippocampal theta oscillations are thought to be a crucial component of memory encoding and retrieval. To better study the role of these oscillations in various cognitive processes, and to be able to build clinical applications around them, accurate and precise estimations of the instantaneous frequency and phase are required. Here, we present methodology based on autoregressive modeling to accomplish this in real time. This allows the targeting of stimulation to a specific phase of a detected oscillation. We first assess performance of the algorithm on two signals where the exact phase and frequency are known. Then, using intracranial EEG recorded from two patients performing a Sternberg memory task, we characterize our algorithm's phase-locking performance on physiologic theta oscillations: optimizing algorithm parameters on the first patient using a genetic algorithm, we carried out cross-validation procedures on subsequent trials and electrodes within the same patient, as well as on data recorded from the second patient.
ISSN:0018-9294
1558-2531
DOI:10.1109/TBME.2011.2109715