2014 IEEE 10th International Colloquium on Signal Processing and Its Applications 2014
DOI: 10.1109/cspa.2014.6805747
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EEG sub-band spectral centroid frequencies extraction based on Hamming and equiripple filters: A comparative study

Abstract: The paper discusses on the effects of Hamming and equiripple filters in the extraction of EEG sub-band spectral centroid frequencies. A total of 40 healthy male subjects have participated in the study. EEG signal sub-bands were filtered using Hamming and equiripple filters with similar frequency response characteristics. It has been observed that the mean difference and variance is very small for all major EEG subbands. Implementation of Hamming filter however, induced 91% higher computational requirements as … Show more

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Cited by 11 publications
(4 citation statements)
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References 21 publications
(37 reference statements)
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“…The filtering block aims to remove artifacts, improve the stationary, and increase accuracy. Many alternatives have been explored in [14] as follows:The first one is using frequency domain transforms such as fast Fourier transform (FFT) or using time-frequency domain such as discrete wavelet transform (DWT).Subtracting artifacts from the acquired signal: this technique requires an average artifacts template estimation to be subtracted from the original EEG signal.Using the same static filtering for all subjects like finite impulse response (FIR) and infinite impulse response (IIR) filters: FIR filters like Equiripple and Kaiserwin are based on Parks-McClellan algorithm using the Remez exchange algorithm and Chebyshev approximation theory to design filters with an optimal_t between the desired and the actual frequency responses [15]. …”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…The filtering block aims to remove artifacts, improve the stationary, and increase accuracy. Many alternatives have been explored in [14] as follows:The first one is using frequency domain transforms such as fast Fourier transform (FFT) or using time-frequency domain such as discrete wavelet transform (DWT).Subtracting artifacts from the acquired signal: this technique requires an average artifacts template estimation to be subtracted from the original EEG signal.Using the same static filtering for all subjects like finite impulse response (FIR) and infinite impulse response (IIR) filters: FIR filters like Equiripple and Kaiserwin are based on Parks-McClellan algorithm using the Remez exchange algorithm and Chebyshev approximation theory to design filters with an optimal_t between the desired and the actual frequency responses [15]. …”
Section: Methodsmentioning
confidence: 99%
“…Using the same static filtering for all subjects like finite impulse response (FIR) and infinite impulse response (IIR) filters: FIR filters like Equiripple and Kaiserwin are based on Parks-McClellan algorithm using the Remez exchange algorithm and Chebyshev approximation theory to design filters with an optimal_t between the desired and the actual frequency responses [15]. …”
Section: Methodsmentioning
confidence: 99%
“…The pre-processed signal is then limited to a implemented with 50% overlapping epochs. Energy spectral density (ESD) for alpha and theta bands is obtained as the area under the respective PSD curve [18]. Power ratio method is then implemented to normalize the ESD between the related EEG [21] bands.…”
Section: Data Collection and Eeg Acquisitionmentioning
confidence: 99%
“…They can be implemented either in the spatial-domain as convolution methods or in the Fourier-domain as multiplication methods. The conventional denoising methods include many classic linear filters, for example, Butterworth filters [9], Hamming filters [10], Hanning filters [11], Gaussian filters [12], moving average filters [13], and autoregressive filters [14]. These linear filters are easy to implement and computationally efficient.…”
Section: Introductionmentioning
confidence: 99%