Long-range temporal correlations of broadband EEG oscillations for depressed subjects following different hemispheric cerebral infarction

Hou, Dongzhe; Wang, Chunfang; Chen, Yuanyuan; Wang, Weijie; Du, Jingang

doi:10.1007/s11571-017-9451-3

Cited by 10 publications

(4 citation statements)

References 59 publications

(70 reference statements)

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“…Several studies have approximated short-range correlations using autoregressive models to estimate movement correlates from EEG (Schlögl et al, 2005;D'Croz-Baron et al, 2012;Wang et al, 2018). However, there is limited literature on long-range temporal correlations (LRTC) in broadband EEG (Hou et al, 2017;Lombardi et al, 2020), and there are no other studies investigating long-range temporal dynamics of single trial broadband EEG during motor tasks. Hence, in this study, building upon our previous work, we delve deeper to investigate changes in broadband LRTC during movement and compare it with the well-known alpha oscillation LRTC.…”

Section: Introductionmentioning

confidence: 99%

“…LRTCs in neuronal activity and movement patterns are correlated (Hu et al, 2004(Hu et al, , 2013, and neural scale-free dynamics can predict the performance of motor tasks (Samek et al, 2016). There are very few studies in the literature that consider broadband LRTC such as the ones by Hou et al (2017) that found attenuation in broadband LRTC during depression and by Lombardi et al (2020) that characterized LRTC in the resting-state broadband EEG using neuronal avalanches. However, there have been no previous external studies investigating LRTCs in broadband arrhythmic EEG during different motor tasks and their relationship with alpha oscillatory LRTC, which is the focus of this study.…”

Section: Introductionmentioning

confidence: 99%

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Dynamics of Long-Range Temporal Correlations in Broadband EEG During Different Motor Execution and Imagery Tasks

2021

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Brain activity is composed of oscillatory and broadband arrhythmic components; however, there is more focus on oscillatory sensorimotor rhythms to study movement, but temporal dynamics of broadband arrhythmic electroencephalography (EEG) remain unexplored. We have previously demonstrated that broadband arrhythmic EEG contains both short- and long-range temporal correlations that change significantly during movement. In this study, we build upon our previous work to gain a deeper understanding of these changes in the long-range temporal correlation (LRTC) in broadband EEG and contrast them with the well-known LRTC in alpha oscillation amplitude typically found in the literature. We investigate and validate changes in LRTCs during five different types of movements and motor imagery tasks using two independent EEG datasets recorded with two different paradigms—our finger tapping dataset with single self-initiated asynchronous finger taps and publicly available EEG dataset containing cued continuous movement and motor imagery of fists and feet. We quantified instantaneous changes in broadband LRTCs by detrended fluctuation analysis on single trial 2 s EEG sliding windows. The broadband LRTC increased significantly (p < 0.05) during all motor tasks as compared to the resting state. In contrast, the alpha oscillation LRTC, which had to be computed on longer stitched EEG segments, decreased significantly (p < 0.05) consistently with the literature. This suggests the complementarity of underlying fast and slow neuronal scale-free dynamics during movement and motor imagery. The single trial broadband LRTC gave high average binary classification accuracy in the range of 70.54±10.03% to 76.07±6.40% for all motor execution and imagery tasks and hence can be used in brain–computer interface (BCI). Thus, we demonstrate generalizability, robustness, and reproducibility of novel motor neural correlate, the single trial broadband LRTC, during different motor execution and imagery tasks in single asynchronous and cued continuous motor-BCI paradigms and its contrasting behavior with LRTC in alpha oscillation amplitude.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamics of Long-Range Temporal Correlations in Broadband EEG During Different Motor Execution and Imagery Tasks

2021

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“…The statistical self-affinity of V was evaluated by the Detrended Fluctuation Analysis (DFA), and it has been widely used analyze time series data with non-stationary and long-memory property [ 41 ]. Hjorth proposed a mathematical method to describe an EEG trace quantitatively [ 42 ], which has been widely applied to various EEG-based problems [ 43 , 44 ].…”

Section: Methodsmentioning

confidence: 99%

Integration of 24 Feature Types to Accurately Detect and Predict Seizures Using Scalp EEG Signals

Zhang

Shen

Liu

et al. 2018

Sensors

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The neurological disorder epilepsy causes substantial problems to the patients with uncontrolled seizures or even sudden deaths. Accurate detection and prediction of epileptic seizures will significantly improve the life quality of epileptic patients. Various feature extraction algorithms were proposed to describe the EEG signals in frequency or time domains. Both invasive intracranial and non-invasive scalp EEG signals have been screened for the epileptic seizure patterns. This study extracted a comprehensive list of 24 feature types from the scalp EEG signals and found 170 out of the 2794 features for an accurate classification of epileptic seizures. An accuracy (Acc) of 99.40% was optimized for detecting epileptic seizures from the scalp EEG signals. A balanced accuracy (bAcc) was calculated as the average of sensitivity and specificity and our seizure detection model achieved 99.61% in bAcc. The same experimental procedure was applied to predict epileptic seizures in advance, and the model achieved Acc = 99.17% for predicting epileptic seizures 10 s before happening.

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“…The essential feature of LRTCs is their power-law behavior, which indicates that the mechanisms contributing to their build-up are similar at different time scales [21]. Abnormal LRTCs of EEG oscillations in the range of 0.5-30 Hz have been observed in MDD [22]. Detrended fluctuation analysis (DFA) is a wellestablished method for the detection of LRTC in time series with non-stationarities.…”

Section: Introductionmentioning

confidence: 99%

Alterations in Patients With First-Episode Depression in the Eyes-Open and Eyes-Closed Conditions: A Resting-State EEG Study

Liu

Yan

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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Altered resting-state EEG activity has been repeatedly reported in major depressive disorder (MDD), but no robust biomarkers have been identified until now. The poor consistency of EEG alterations may be due to inconsistent resting conditions; that is, the eyes-open (EO) and eyes-closed (EC) conditions. Here, we explored the effect of the EO and EC conditions on EEG biomarkers for discriminating MDD subjects and healthy control (HC) subjects. EEG data were recorded from 30 first-episode MDD and 26 HC subjects during an 8-min resting-state session. The features were extracted using spectral power, Lempel-Ziv complexity, and detrended fluctuation analysis. Significant features were further selected via the sequential backward feature selection algorithm. Support vector machine (SVM), logistic regression, and linear discriminate analysis were used to determine a better resting condition to provide more reliable estimates for identifying MDD. Compared with the HC group, we found that the MDD group exhibited widespread increased β and γ powers (p < 0.01) in both conditions. In the EO condition, the MDD group showed increased complexity and scaling exponents in the α band relative to HC subjects (p < 0.05). The best classification performance of the combined feature sets was found in the EO condition, with the leave-one-out classification accuracy of 89.29%, sensitivity of 90.00%, and specificity of 88.46% using SVM with the linear kernel classifier when the threshold was set to 0.7, followed by the β and γ spectral features with an average accuracy of 83.93%. Overall, EO and EC conditions indeed affected the between-group variance, and the EO condition is suggested as the more separable resting condition to identify depression. Specially, the β and γ powers are suggested as potential biomarkers for first-episode MDD.

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Long-range temporal correlations of broadband EEG oscillations for depressed subjects following different hemispheric cerebral infarction

Cited by 10 publications

References 59 publications

Dynamics of Long-Range Temporal Correlations in Broadband EEG During Different Motor Execution and Imagery Tasks

Dynamics of Long-Range Temporal Correlations in Broadband EEG During Different Motor Execution and Imagery Tasks

Integration of 24 Feature Types to Accurately Detect and Predict Seizures Using Scalp EEG Signals

Alterations in Patients With First-Episode Depression in the Eyes-Open and Eyes-Closed Conditions: A Resting-State EEG Study

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