Name
Abstract | ||
|---|---|---|
Neural oscillations are important features in a working central nervous system, facilitating efficient communication across large networks of neurons. 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. Using intracranial EEG recorded from two patients performing a Sternberg memory task, we characterize our algorithm's phase-locking performance on physiologic theta oscillations. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/IEMBS.2011.6090843 | 2011 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) |
Keywords | Field | DocType |
electroencephalography,real time,cognitive process,coherence,cognition,autoregressive model,oscillators,central nervous system,time frequency analysis,electrodes,instantaneous frequency,neurophysiology,oscillations | Oscillation,Neuroscience,Computer science,Artificial intelligence,Cognition,Instantaneous phase,Electroencephalography,Computer vision,Autoregressive model,Neurophysiology,Pattern recognition,Coherence (physics),Time–frequency analysis | Conference |
Volume | ISSN | Citations |
2011 | 1557-170X | 0 |
PageRank | References | Authors |
0.34 | 1 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| L Leon Chen | 1 | 5 | 1.66 |
| Radhika Madhavan | 2 | 3 | 0.82 |
| Benjamin I Rapoport | 3 | 0 | 0.34 |
| William S. Anderson | 4 | 10 | 5.52 |