TY - JOUR
T1 - Human ECoG Analysis during Speech Perception Using Matching Pursuit
T2 - A Comparison between Stochastic and Dyadic Dictionaries
AU - Ray, Supratim
AU - Jouny, Christophe C.
AU - Crone, Nathan E.
AU - Boatman, Dana
AU - Thakor, Nitish V.
AU - Franaszczuk, Piotr J.
N1 - Funding Information:
Manuscript received November 15, 2002; revised April 3, 2002. The work of P. J. Franaszczuk and C. C. Jouny was supported by National Institutes of Health (NIH) under Grant NS33732. The work of N. E. Crone was supported in part by the NIH under Grant NS40596, The work of D. Boatman was supported in part by the NIH under Grant DC005645. The work of N. V. Thakor was supported in part by the NIH under Grant NS42690. Asterisk indicates corresponding author. *S. Ray is with the Biomedical Engineering, Johns Hopkins University, 3400 N. Charles Street, 338 Krieger Hall, Room 253, Baltimore, MD 21218 USA (e-mail: [email protected]).
PY - 2003/12
Y1 - 2003/12
N2 - We use the matching pursuit (MP) algorithm to detect induced gamma activity in human EEG during speech perception. We show that the MP algorithm is particularly useful for detecting small power changes at high gamma frequencies (>70 Hz). We also compare the performance of the MP using a stochastic versus a dyadic dictionary and show that despite the frequency bias the time-frequency power plot (averaged over 100 trials) generated by the dyadic MP is almost identical (>98.5%) to the one generated by the stochastic MP. However, the dyadic MP is computationally much faster than the stochastic MP.
AB - We use the matching pursuit (MP) algorithm to detect induced gamma activity in human EEG during speech perception. We show that the MP algorithm is particularly useful for detecting small power changes at high gamma frequencies (>70 Hz). We also compare the performance of the MP using a stochastic versus a dyadic dictionary and show that despite the frequency bias the time-frequency power plot (averaged over 100 trials) generated by the dyadic MP is almost identical (>98.5%) to the one generated by the stochastic MP. However, the dyadic MP is computationally much faster than the stochastic MP.
KW - Linear biosignal analysis
KW - Matching pursuit
KW - Time-frequency decomposition
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U2 - 10.1109/TBME.2003.819852
DO - 10.1109/TBME.2003.819852
M3 - Article
C2 - 14656066
AN - SCOPUS:0344925581
SN - 0018-9294
VL - 50
SP - 1371
EP - 1373
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
IS - 12
ER -