Brain functional and effective connectivity based on electroencephalography recordings: A review

Cao, Jun; Zhao, Yifan; Shan, Xiaocai; Wei, Hua‐Liang; Guo, Yuzhu; Chen, Liang‐Yu; Erkoyuncu, John Ahmet; Sarrigiannis, Ptolemaios G.

doi:10.1002/hbm.25683

Cited by 110 publications

(65 citation statements)

References 170 publications

(287 reference statements)

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“…Additionally, another future work is to use fuzzy methods [114,115] in epileptic seizure detection. In other future works, effective connectivity techniques may be used to diagnose epileptic seizures [116][117][118]; first, EEG signals are transformed into 2D images using effective connectivity methods. Then, these 2 D images are applied to different 2D deep learning networks.…”

Section: Discussion Conclusion and Future Workmentioning

confidence: 99%

Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features

Malekzadeh

Zare

Yaghoobi

et al. 2021

Sensors

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Epilepsy is a brain disorder disease that affects people’s quality of life. Electroencephalography (EEG) signals are used to diagnose epileptic seizures. This paper provides a computer-aided diagnosis system (CADS) for the automatic diagnosis of epileptic seizures in EEG signals. The proposed method consists of three steps, including preprocessing, feature extraction, and classification. In order to perform the simulations, the Bonn and Freiburg datasets are used. Firstly, we used a band-pass filter with 0.5–40 Hz cut-off frequency for removal artifacts of the EEG datasets. Tunable-Q Wavelet Transform (TQWT) is used for EEG signal decomposition. In the second step, various linear and nonlinear features are extracted from TQWT sub-bands. In this step, various statistical, frequency, and nonlinear features are extracted from the sub-bands. The nonlinear features used are based on fractal dimensions (FDs) and entropy theories. In the classification step, different approaches based on conventional machine learning (ML) and deep learning (DL) are discussed. In this step, a CNN–RNN-based DL method with the number of layers proposed is applied. The extracted features have been fed to the input of the proposed CNN–RNN model, and satisfactory results have been reported. In the classification step, the K-fold cross-validation with k = 10 is employed to demonstrate the effectiveness of the proposed CNN–RNN classification procedure. The results revealed that the proposed CNN–RNN method for Bonn and Freiburg datasets achieved an accuracy of 99.71% and 99.13%, respectively.

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Section: Discussion Conclusion and Future Workmentioning

confidence: 99%

Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features

Malekzadeh

Zare

Yaghoobi

et al. 2021

Sensors

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“…The functional connectivity metrics used in the current study are a small subset of the methods that are available. Most recent studies combining brain connectivity estimations with machine learning methods focus on various neurological conditions with much less extensive evidence on functional connectivity influencing perceptual responses (Cao et al 2022; Sadaghiani et al 2022). It is very possible that functional connectivity contains more decodable information about the task conditions and performance measures than these results suggest.…”

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Connecting Covert Attention and Visual Perception to the Spatiotemporal Dynamics of Alpha Band Activity, Cross-Frequency Coupling (CFC), and Functional Connectivity using Multivariate Pattern Analysis (MVPA)

Sheldon

Fyshe

Mathewson

2022

Preprint

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Some evidence suggests that alpha activity is directly related to the baseline firing rate of sensory neurons which alters the probability of responding to a stimulus. Other evidence points to alpha indirectly modulating cortical excitability through its interactions with gamma oscillations. A third possibility is that alpha-based functional connectivity better explains attentional modulation and perceptual responses. To test this, alpha amplitude, CFC, and functional connectivity measures were extracted from EEG data recorded while participants performed a cued orientation perception task. Using pre-target data, the spatiotemporal activity of each metric was submitted to a SVM classifier to determine which activity pattern best distinguished trials with covert attention from trials without. The same metrics were submitted to SVR to find the activity that best predicted task performance. Results indicate the best metric for classifying trials with and without covert attention was alpha amplitude. This indicates that, prior to target onset, alpha amplitude alone is most sensitive to the presence of covert attention. In contrast, none of the metrics were strong predictors of task performance. Overall, our results support the idea that alpha activity is directly related to changes in the baseline firing rate of sensory neurons which changes responsiveness but not performance.

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“…The former focuses on neuroanatomical links between particular brain structures, the latter reflects the statistical dependencies between the activity of spatially distinct brain areas (Friston, 2011 ). Functional connectivity is usually measured by correlation, coherence, and information theory (Cao et al, 2021 ). While functional connectivity can be assessed based on BOLD and EEG signals, structural connections cannot be revealed by the EEG technique.…”

Section: Introductionmentioning

confidence: 99%

Brain Activity Characteristics of Patients With Disorders of Consciousness in the EEG Resting State Paradigm: A Review

Duszyk-Bogorodzka

Zieleniewska

Jankowiak-Siuda

2022

Front. Syst. Neurosci.

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The assessment of the level of consciousness in disorders of consciousness (DoC) is still one of the most challenging problems in contemporary medicine. Nevertheless, based on the multitude of studies conducted over the last 20 years on resting states based on electroencephalography (EEG) in DoC, it is possible to outline the brain activity profiles related to both patients without preserved consciousness and minimally conscious ones. In the case of patients without preserved consciousness, the dominance of low, mostly delta, frequency, and the marginalization of the higher frequencies were observed, both in terms of the global power of brain activity and in functional connectivity patterns. In turn, the minimally conscious patients revealed the opposite brain activity pattern—the characteristics of higher frequency bands were preserved both in global power and in functional long-distance connections. In this short review, we summarize the state of the art of EEG-based research in the resting state paradigm, in the context of providing potential support to the traditional clinical assessment of the level of consciousness.

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Brain functional and effective connectivity based on electroencephalography recordings: A review

Cited by 110 publications

References 170 publications

Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features

Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features

Connecting Covert Attention and Visual Perception to the Spatiotemporal Dynamics of Alpha Band Activity, Cross-Frequency Coupling (CFC), and Functional Connectivity using Multivariate Pattern Analysis (MVPA)

Brain Activity Characteristics of Patients With Disorders of Consciousness in the EEG Resting State Paradigm: A Review

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