directed transfer function
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2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gert Pfurtscheller ◽  
Katarzyna J. Blinowska ◽  
Maciej Kaminski ◽  
Andreas R. Schwerdtfeger ◽  
Beate Rassler ◽  
...  

AbstractBrain–heart synchronization is fundamental for emotional-well-being and brain–heart desynchronization is characteristic for anxiety disorders including specific phobias. Recording BOLD signals with functional magnetic resonance imaging (fMRI) is an important noninvasive diagnostic tool; however, 1–2% of fMRI examinations have to be aborted due to claustrophobia. In the present study, we investigated the information flow between regions of interest (ROI’s) in the cortex and brain stem by using a frequency band close to 0.1 Hz. Causal coupling between signals important in brain–heart interaction (cardiac intervals, respiration, and BOLD signals) was studied by means of Directed Transfer Function based on the Granger causality principle. Compared were initial resting states with elevated anxiety and final resting states with low or no anxiety in a group of fMRI-naïve young subjects. During initial high anxiety the results showed an increased information flow from the middle frontal gyrus (MFG) to the pre-central gyrus (PCG) and to the brainstem. There also was an increased flow from the brainstem to the PCG. While the top-down flow during increased anxiety was predominant, the weaker ascending flow from brainstem structures may characterize a rhythmic pacemaker-like activity that (at least in part) drives respiration. We assume that these changes in information flow reflect successful anxiety processing.


2021 ◽  
Author(s):  
Mingai Li ◽  
Na Zhang

Abstract Directed transfer function (DTF) is good at characterizing the pairwise interactions from the whole brain network and has been applied for discriminating different motor imagery (MI) tasks. Considering the fact that MI electroencephalogram signals are more non-stationary in frequency domain than in time domain, and the activated intensities of α band (8-13Hz) and β band (13-30Hz, with \({\beta }_{1}\)(13-21Hz) and \({\beta }_{2}\)(21-30Hz) included) have considerable differences for different subjects, a dynamic DTF (DDTF) with variable model order and frequency band is proposed to construct the brain functional networks (BFNs), whose information flows and outflows are further calculated as network features and evaluated by support vector machine. Extensive experiments are conducted based on a public BCI competition dataset and a real-world dataset, the highest recognition rate achieve 100% and 86%, respectively. The experimental results suggest that DDTF can reflect the dynamic evolution of BFN, the best subject-based DDTF appears in one of four frequency subbands (α, β, \({\beta }_{1}\),\({\beta }_{2}\)) for discrimination of MI tasks and is much more related to the current and previous states. Besides, DDTF is superior compared to granger causality-based and traditional feature extraction methods, the t-test and Kappa values show its statistical significance and high consistency as well.


Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 1037
Author(s):  
Luiz A. Baccalá ◽  
Koichi Sameshima

Using directed transfer function (DTF) and partial directed coherence (PDC) in the information version, this paper extends the theoretical framework to incorporate the instantaneous Granger causality (iGC) frequency domain description into a single unified perspective. We show that standard vector autoregressive models allow portraying iGC’s repercussions associated with Granger connectivity, where interactions mediated without delay between time series can be easily detected.


2021 ◽  
Vol 15 ◽  
Author(s):  
Tao Zhang ◽  
Chengcheng Hua ◽  
Jichi Chen ◽  
Enqiu He ◽  
Hong Wang

Tacit knowledge is the kind of knowledge that is difficult to transfer to another person by means of writing it down or verbalizing it. In the mineral grinding process, the proficiency of the operators depends on the tacit knowledge gained from their experience and training rather than on knowledge learned from a handbook. This article proposed a method combining the electroencephalogram (EEG) signals and the industrial process to detect the proficiency of the operators in the mineral grinding process to reveal the effect of tacit knowledge on the functional cortical connection. The functional brain networks of operators were established based on partial direct coherence and directed transfer function of EEG, and the multi-classifiers were used with the graph-theoretic indexes of the FBNs as input to distinguish the trained operators (Hps) from the non-trained operators (Lps). The results showed that the brain networks of Hps had a better connectivity than those of Lps (p < 0.01), and the accuracy of classification was up to 94.2%. Our studies confirm that based on the performance of EEG features and the combination of industrial operational operation and cognitive processes, the proficiency of the operators can be detected.


Author(s):  
Luiz Antonio Baccalá ◽  
Koichi Sameshima

Using Directed Transfer Function (DTF) and Partial Directed Coherence (PDC) in their information version, this paper extends their theoretical framework to incorporate instantaneous Granger Causality (iGC)’s frequency domain description into a single unified perspective. We show that standard vector autoregressive models allow portraying iGC’s repercussions associated with Granger Connectivity where interactions mediated without delay between time series can be easily detected.


2021 ◽  
pp. 155005942110185
Author(s):  
Caglar Uyulan ◽  
Sara de la Salle ◽  
Turker T. Erguzel ◽  
Emma Lynn ◽  
Pierre Blier ◽  
...  

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.


2021 ◽  
Vol 15 ◽  
Author(s):  
Manuel A. Vázquez ◽  
Arash Maghsoudi ◽  
Inés P. Mariño

In this work we propose a machine learning (ML) method to aid in the diagnosis of schizophrenia using electroencephalograms (EEGs) as input data. The computational algorithm not only yields a proposal of diagnostic but, even more importantly, it provides additional information that admits clinical interpretation. It is based on an ML model called random forest that operates on connectivity metrics extracted from the EEG signals. Specifically, we use measures of generalized partial directed coherence (GPDC) and direct directed transfer function (dDTF) to construct the input features to the ML model. The latter allows the identification of the most performance-wise relevant features which, in turn, provide some insights about EEG signals and frequency bands that are associated with schizophrenia. Our preliminary results on real data show that signals associated with the occipital region seem to play a significant role in the diagnosis of the disease. Moreover, although every frequency band might yield useful information for the diagnosis, the beta and theta (frequency) bands provide features that are ultimately more relevant for the ML classifier that we have implemented.


Author(s):  
Ahmad Shalbaf ◽  
◽  
Arash Maghsoudi ◽  

Introduction: Mental arithmetic analysis based on Electroencephalogram (EEG) signal for monitoring the state of the user’s brain functioning can be helpful for understanding some psychological disorders such as attention deficit hyperactivity disorder, autism spectrum disorder, or dyscalculia where the difficulty in learning or understanding the arithmetic exists. Most mental arithmetic recognition systems rely on features of a single channel of EEG; however, the relationships among EEG channels in the form of effective brain connectivity analysis can contain valuable information. The aim of this paper is to identify a set of discriminative effective brain connectivity features from EEG signal and develop a hierarchical feature selection structure for classification of mental arithmetic and baseline tasks effectively. Methods: We estimated effective connectivity using Directed Transfer Function (DTF), direct Directed Transfer Function (dDTF) and Generalized Partial Directed Coherence (GPDC) methods. These measures determine the causal relation between different brain areas. To select most significant effective connectivity features, a hierarchical feature subset selection method is used. First Kruskal–Wallis test was performed and consequently, five feature selection algorithms namely Support Vector Machine ( SVM ) method based on Recursive Feature Elimination, Fisher score, mutual information, minimum Redundancy Maximum Relevance and concave minimization and SVM are used to select the best discriminative features. Finally, SVM method was used for classification. Results: Results show that the best EEG classification performance in 29 participants and 60 trials is obtained using GPDC and feature selection via concave minimization method in Beta2 (15−22Hz) frequency band with 89% accuracy. Conclusion: This new hierarchical automated system could be useful for discrimination of mental arithmetic and baseline tasks from EEG signal effectively.


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