Cross spectral analysis of nonstationary processes

White, L.B.; Boashash, Boualem

doi:10.1109/18.53742

Cited by 107 publications

(61 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where f is frequency, and P xx and P yy are functions of the power densities for two signals x and y, respectively [29]. P xy is a function of the cross spectral density of signal x and y which is obtained by the product of the Fourier transform of signal x and the complex conjugate of Fourier transform of signal y.…”

Section: Wavelet Coherencementioning

confidence: 99%

Wavelet Energy and Wavelet Coherence as EEG Biomarkers for the Diagnosis of Parkinson’s Disease-Related Dementia and Alzheimer’s Disease

Jeong

Kim

Song

et al. 2015

Entropy

View full text Add to dashboard Cite

Abstract:Parkinson's disease (PD) and Alzheimer's disease (AD) can coexist in severely affected; elderly patients. Since they have different pathological causes and lesions and consequently require different treatments; it is critical to distinguish PD-related dementia (PD-D) from AD. Conventional electroencephalograph (EEG) analysis has produced poor results. This study investigated the possibility of using relative wavelet energy (RWE) and wavelet coherence (WC) analysis to distinguish between PD-D patients; AD patients and healthy elderly subjects. In EEG signals; we found that low-frequency wavelet energy increased and high-frequency wavelet energy decreased in PD-D patients and AD patients relative to healthy subjects. This result suggests that cognitive decline in both diseases is potentially related to slow EEG activity; which is consistent with previous studies. More importantly; WC values were lower in AD patients and higher in PD-D patients compared with healthy subjects. In particular; AD patients exhibited decreased WC primarily in the γ band and in links related to frontal regions; while PD-D patients exhibited increased WC primarily in the α and β bands and in temporo-parietal links. Linear discriminant analysis (LDA) of RWE produced a maximum accuracy of 79.18% for diagnosing PD-D and 81.25% for diagnosing AD. The discriminant accuracy was 73.40% with 78.78% sensitivity and 69.47% specificity. In distinguishing between the two diseases; the maximum performance of LDA using WC was 80.19%. We suggest that using a wavelet approach to evaluate EEG results may facilitate discrimination between PD-D and AD. In particular; RWE is useful for differentiating individuals with and without dementia and WC is useful for differentiating between PD-D and AD.

show abstract

Section: Wavelet Coherencementioning

confidence: 99%

Wavelet Energy and Wavelet Coherence as EEG Biomarkers for the Diagnosis of Parkinson’s Disease-Related Dementia and Alzheimer’s Disease

Jeong

Kim

Song

et al. 2015

Entropy

View full text Add to dashboard Cite

show abstract

“…The coherence analysis [29], [30] is useful to integrate spectral channel correlations into EEG signal processing. It uses the normalized cross-spectrum to represent EEG channel correlations.…”

Section: Signal Processing and Classificationmentioning

confidence: 99%

Exploiting EEG Channel Correlations in P300 Speller Paradigm for Brain-Computer Interface

Liu

Wang

2016

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

SUMMARYA brain-computer interface (BCI) translates the brain activity into commands to control external devices. P300 speller based character recognition is an important kind of application system in BCI. In this paper, we propose a framework to integrate channel correlation analysis into P300 detection. This work is distinguished by two key contributions. First, a coefficient matrix is introduced and constructed for multiple channels with the elements indicating channel correlations. Agglomerative clustering is applied to group correlated channels. Second, the statistics of central tendency are used to fuse the information of correlated channels and generate virtual channels. The generated virtual channels can extend the EEG signals and lift up the signal-to-noise ratio. The correlated features from virtual channels are combined with original signals for classification and the outputs of discriminative classifier are used to determine the characters for spelling. Experimental results prove the effectiveness and efficiency of the channel correlation analysis based framework. Compared with the state-of-the-art, the recognition rate was increased by both 6% with 5 and 10 epochs by the proposed framework. key words: brain-computer interface (BCI), channel correlation analysis, virtual channels, P300 speller paradigm

show abstract

“…Conventional spectral analysis assumes a stationary signal and is therefore unable to identify pattern changes. An approach to account for such changes is to implement a time-varying spectral and coherence analysis [14,36]. Using a 6-min sliding time window with 75 % overlap, both V E and HR signals are divided into segments assumed to be stationary and suitable for power spectral density (PSD) and coherence analysis.…”

Section: Time-varying Signal Processing Techniquesmentioning

confidence: 99%

Time-varying signal analysis to detect high-altitude periodic breathing in climbers ascending to extreme altitude

Garde

Giraldo

Jané

et al. 2015

Med Biol Eng Comput

View full text Add to dashboard Cite

show abstract

Cross spectral analysis of nonstationary processes

Cited by 107 publications

References 14 publications

Wavelet Energy and Wavelet Coherence as EEG Biomarkers for the Diagnosis of Parkinson’s Disease-Related Dementia and Alzheimer’s Disease

Wavelet Energy and Wavelet Coherence as EEG Biomarkers for the Diagnosis of Parkinson’s Disease-Related Dementia and Alzheimer’s Disease

Exploiting EEG Channel Correlations in P300 Speller Paradigm for Brain-Computer Interface

Time-varying signal analysis to detect high-altitude periodic breathing in climbers ascending to extreme altitude

Contact Info

Product

Resources

About