Applications of Higher Order Statistics in Electroencephalography Signal Processing: A Comprehensive Survey

Khoshnevis, Seyed Alireza; Sankar, Ravi

doi:10.1109/rbme.2019.2951328

Cited by 31 publications

(22 citation statements)

References 88 publications

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“…84,85 Given the nonstationarity, nonlinearity, and randomness of EEG signals, which are relatively weak with a low signal-to-noise ratio, advanced signal processing and higher order statistics (eg, nonlinear dynamic methods and chaos theory) are tools that may enhance EEG feature detection for a more precise diagnosis of MDD. [86][87][88] To the best of our knowledge, the present study is the first one to compare the 2 advanced computational techniques, ie MVAR and DL. Classification accuracies of 95.9% with dPDC for the MVAR framework and 90.22% for the DL framework were observed.…”

Section: Discussionmentioning

confidence: 99%

Depression Diagnosis Modeling With Advanced Computational Methods: Frequency-Domain eMVAR and Deep Learning

et al. 2021

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Electroencephalogram (EEG)-based automated depression diagnosis systems have been suggested for early and accurate detection of mood disorders. EEG signals are highly irregular, nonlinear, and nonstationary in nature and are traditionally studied from a linear viewpoint by means of statistical and frequency features. Since, linear metrics present certain limitations and nonlinear methods have proven to be an efficient tool in understanding the complexities of the brain in the identification of underlying behavior of biological signals, such as electrocardiogram, EEG and magnetoencephalogram and thus, can be applied to all nonstationary signals. Various nonlinear algorithms can be used in the analysis of EEG signals. In this research paper, we aim to develop a novel methodology for EEG-based depression diagnosis utilizing 2 advanced computational techniques: frequency-domain extended multivariate autoregressive (eMVAR) and deep learning (DL). We proposed a hybrid method comprising a pretrained ResNet-50 and long-short term memory (LSTM) to capture depression-specific information and compared with a strong conventional machine learning (ML) framework having eMVAR connectivity features. The following 8 causality measures, which interpret the interaction mechanisms among spectrally decomposed oscillations, were used to extract features from multivariate EEG time series: directed coherence (DC), directed transfer function (DTF), partial DC (PDC), generalized PDC (gPDC), extended DC (eDC), delayed DC (dDC), extended PDC (ePDC), and delayed PDC (dPDC). The classification accuracies were 84% with DC, 85% with DTF, 95.3% with PDC, 95.1% with gPDC, 84.8% with eDC, 84.6% with dDC, 84.2% with ePDC, and 95.9% with dPDC for the eMVAR framework. Through a DL framework (ResNet-50 + LSTM), the classification accuracy was achieved as 90.22%. The results demonstrate that our DL methodology is a competitive alternative to the strong feature extraction-based ML methods in depression classification.

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Section: Discussionmentioning

confidence: 99%

Depression Diagnosis Modeling With Advanced Computational Methods: Frequency-Domain eMVAR and Deep Learning

et al. 2021

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“…These signal processing methods are divided in four fundamental domains [33][34][35][36][37][38][39][40], as can be seen in Table 3. Furthermore, various algorithms have been established to visualize brain activity using restructured images from EEGs.…”

Section: Eeg Signal Processingmentioning

confidence: 99%

A Survey on EEG Signal Processing Techniques and Machine Learning: Applications to the Neurofeedback of Autobiographical Memory Deficits in Schizophrenia

et al. 2021

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In this paper, a general overview regarding neural recording, classical signal processing techniques and machine learning classification algorithms applied to monitor brain activity is presented. Currently, several approaches classified as electrical, magnetic, neuroimaging recordings and brain stimulations are available to obtain neural activity of the human brain. Among them, non-invasive methods like electroencephalography (EEG) are commonly employed, as they can provide a high degree of temporal resolution (on the order of milliseconds) and acceptable space resolution. In addition, it is simple, quick, and does not create any physical harm or stress to patients. Concerning signal processing, once the neural signals are acquired, different procedures can be applied for feature extraction. In particular, brain signals are normally processed in time, frequency, and/or space domains. The features extracted are then used for signal classification depending on its characteristics such us the mean, variance or band power. The role of machine learning in this regard has become of key importance during the last years due to its high capacity to analyze complex amounts of data. The algorithms employed are generally classified in supervised, unsupervised and reinforcement techniques. A deep review of the most used machine learning algorithms and the advantages/drawbacks of most used methods is presented. Finally, a study of these procedures utilized in a very specific and novel research field of electroencephalography, i.e., autobiographical memory deficits in schizophrenia, is outlined.

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“…The QFA method, developed recently in the statistical literature [7]- [9], is a nonlinear technique that explores the dynamics of time series data beyond their second-order statistical properties. Instead of higher-order moments [10]- [13], the QFA method examines spectral properties of a signal at different quantiles, and represents these properties as a two-dimensional function of frequency and quantile level. At a fixed quantile level, the cross section of this two-dimensional function reduces to a one-dimensional function of frequency called quantile periodogram.…”

Section: Introductionmentioning

confidence: 99%

Quantile-Frequency Analysis and Deep Learning for Signal Classification

2022

Preprint

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This paper proposes a new method for signal classification based on a combination of recently introduced nonlinear spectral analysis technique called quantile-frequency analysis (QFA) and deep-learning (DL) image classifiers. The QFA method converts a one-dimensional signal into a two-dimensional representation of the signal's oscillatory behavior in the frequency domain at different quantiles or a sequence of such representations that are localized in time. The DL image classifiers utilize these representations for signal classification. The benefit of this QFA-DL classification method, especially in comparison with the traditional method based on the power spectrum and spectrogram, is demonstrated by a numerical experiment using real-world data from a nondestructive evaluation (NDE) application.

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Applications of Higher Order Statistics in Electroencephalography Signal Processing: A Comprehensive Survey

Cited by 31 publications

References 88 publications

Depression Diagnosis Modeling With Advanced Computational Methods: Frequency-Domain eMVAR and Deep Learning

Depression Diagnosis Modeling With Advanced Computational Methods: Frequency-Domain eMVAR and Deep Learning

A Survey on EEG Signal Processing Techniques and Machine Learning: Applications to the Neurofeedback of Autobiographical Memory Deficits in Schizophrenia

Quantile-Frequency Analysis and Deep Learning for Signal Classification

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