Name
Title | ||
|---|---|---|
Real-Time Brain Oscillation Detection and Phase-Locked Stimulation Using Autoregressive Spectral Estimation and Time-Series Forward Prediction |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/TBME.2011.2109715 | Biomedical Engineering, IEEE Transactions |
Keywords | Field | DocType |
autoregressive processes,biomedical electrodes,cognition,electroencephalography,encoding,estimation theory,genetic algorithms,medical signal processing,neurophysiology,oscillations,phase locked oscillators,synchronisation,time series,Sternberg memory task,autoregressive modeling,autoregressive spectral estimation,central nervous system,clinical applications,cognitive processes,cross-validation procedures,electrodes,genetic algorithm,hippocampal theta oscillations,intracranial EEG recording,memory encoding,memory retrieval,neural oscillations,optimizing algorithm parameters,phase-locked stimulation,physiologic theta oscillations,real-time brain oscillation detection,synaptic plasticity,synchronization,time-series forward prediction,Autoregressive (AR) model,closed-loop stimulation,genetic algorithm,intracranial EEG(iEEG),neural oscillations,phase-locking,real time,theta rhythm | Autoregressive model,Computer vision,Synchronization,Neurophysiology,Pattern recognition,Computer science,Artificial intelligence,Estimation theory,Instantaneous phase,Genetic algorithm,Electroencephalography,Encoding (memory) | Journal |
Volume | Issue | ISSN |
60 | 3 | 0018-9294 |
Citations | PageRank | References |
3 | 0.48 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| L Leon Chen | 1 | 5 | 1.66 |
| Radhika Madhavan | 2 | 3 | 0.82 |
| Benjamin I. Rapoport | 3 | 3 | 0.48 |
| William S. Anderson | 4 | 10 | 5.52 |