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- W2894076376 abstract "T415 article distinguished the different frequencies of sound by the feature of electroencephalogram (EEG). EEGs were evoked by low-frequency (200 Hz) and high-frequency (6400 Hz) pure-tone. The optimal feature set was selected using a sequential backward selection (SBS) algorithm from a primary feature set. The best feature had average amplitude, power spectrum and low-frequency energy of wavelet packet 4 decompose. Statistic test shows that power spectrum and low-frequency energy of wavelet packet 4 decompose of the ERPs with 200 Hz were significantly larger than those with 6400 Hz. The back-propagation artificial neural network (BPANN) algorithm and support vector machine (SVM) were used for binary classification. SVM is better, and the correct recognition rate of the 200 Hz and 6400 Hz are 79.17% and 70.83%. The experimental results has potential value for the brain-computer interface (BCI) and the tone audiometry." @default.
- W2894076376 created "2018-10-05" @default.
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- W2894076376 date "2018-05-01" @default.
- W2894076376 modified "2023-09-27" @default.
- W2894076376 title "Recognition of Sound Frequency Based on EEG" @default.
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- W2894076376 doi "https://doi.org/10.1109/imcec.2018.8469567" @default.
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