TY - JOUR
T1 - A method for detection and classification of events in neural activity
AU - Bokil, Hemant S.
AU - Pesaran, Bijan
AU - Andersen, Richard A.
AU - Mitra, Partha P.
N1 - Funding Information:
Prof. Pesaran is a member of the Society for Neuroscience and Neural Control of Movement Society. He is a recipient of Career Award in the Biomedical Sciences from the Burroughs-Wellcome Fund.
Funding Information:
Manuscript received July 29, 2005; revised February 19, 2006. This work was supported in part by Defense Advanced Research Projects Agency (DARPA), in part by the McKnight Foundation, in part by the Swartz Foundation, in part by the National Institutes of Health (NIH) under Grant R01 MH62528-02 and Grany EY 13337-03. The work of R. A. Anderson was supported in part by a Boswell Professorship. Asterisk indicates corresponding author. *H. S. Bokil is with the Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724 USA (e-mail: [email protected]).
PY - 2006/8
Y1 - 2006/8
N2 - We present a method for the real time prediction of punctuate events in neural activity, based on the time-frequency spectrum of the signal, applicable both to continuous processes like local field potentials (LFPs) as well as to spike trains. We test it on recordings of LFP and spiking activity acquired previously from the lateral intraparietal area (LIP) of macaque monkeys performing a memory-saccade task. In contrast to earlier work, where trials with known start times were classified, our method detects and classifies trials directly from the data. It provides a means to quantitatively compare and contrast the content of LFP signals and spike trains: we find that the detector performance based on the LFP matches the performance based on spike rates. The method should find application in the development of neural prosthetics based on the LFP signal. Our approach uses a new feature vector, which we call the 2d cepstrum.
AB - We present a method for the real time prediction of punctuate events in neural activity, based on the time-frequency spectrum of the signal, applicable both to continuous processes like local field potentials (LFPs) as well as to spike trains. We test it on recordings of LFP and spiking activity acquired previously from the lateral intraparietal area (LIP) of macaque monkeys performing a memory-saccade task. In contrast to earlier work, where trials with known start times were classified, our method detects and classifies trials directly from the data. It provides a means to quantitatively compare and contrast the content of LFP signals and spike trains: we find that the detector performance based on the LFP matches the performance based on spike rates. The method should find application in the development of neural prosthetics based on the LFP signal. Our approach uses a new feature vector, which we call the 2d cepstrum.
KW - Cepstral analysis
KW - Decoding
KW - Multitaper spectral analysis
KW - Nervous system
KW - Prediction methods
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U2 - 10.1109/TBME.2006.877802
DO - 10.1109/TBME.2006.877802
M3 - Article
C2 - 16916103
AN - SCOPUS:33746644804
SN - 0018-9294
VL - 53
SP - 1678
EP - 1687
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
IS - 8
M1 - 1658163
ER -