scholarly journals Kernel-Based Phase Transfer Entropy with Enhanced Feature Relevance Analysis for Brain Computer Interfaces

2021 ◽  
Vol 11 (15) ◽  
pp. 6689
Author(s):  
Iván De La Pava Panche ◽  
Andrés Álvarez-Meza ◽  
Paula Marcela Herrera Gómez ◽  
David Cárdenas-Peña ◽  
Jorge Iván Ríos Patiño ◽  
...  
Keyword(s):  
Working Memory ◽  
Time Series ◽  
Motor Imagery ◽  
Phase Transfer ◽  
Effective Connectivity ◽  
Transfer Entropy ◽  
Neural Oscillations ◽  
Computer Interfaces ◽  
Relevance Analysis ◽  
The Brain

Neural oscillations are present in the brain at different spatial and temporal scales, and they are linked to several cognitive functions. Furthermore, the information carried by their phases is fundamental for the coordination of anatomically distributed processing in the brain. The concept of phase transfer entropy refers to an information theory-based measure of directed connectivity among neural oscillations that allows studying such distributed processes. Phase TE is commonly obtained from probability estimations carried out over data from multiple trials, which bars its use as a characterization strategy in brain–computer interfaces. In this work, we propose a novel methodology to estimate TE between single pairs of instantaneous phase time series. Our approach combines a kernel-based TE estimator defined in terms of Renyi’s α entropy, which sidesteps the need for probability distribution computation with phase time series obtained by complex filtering the neural signals. Besides, a kernel-alignment-based relevance analysis is added to highlight relevant features from effective connectivity-based representation supporting further classification stages in EEG-based brain–computer interface systems. Our proposal is tested on simulated coupled data and two publicly available databases containing EEG signals recorded under motor imagery and visual working memory paradigms. Attained results demonstrate how the introduced effective connectivity succeeds in detecting the interactions present in the data for the former, with statistically significant results around the frequencies of interest. It also reflects differences in coupling strength, is robust to realistic noise and signal mixing levels, and captures bidirectional interactions of localized frequency content. Obtained results for the motor imagery and working memory databases show that our approach, combined with the relevance analysis strategy, codes discriminant spatial and frequency-dependent patterns for the different conditions in each experimental paradigm, with classification performances that do well in comparison with those of alternative methods of similar nature.

A fuzzy sensitivity analysis approach to estimate brain effective connectivity and its application to epileptic seizure detection

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Nader Moharamzadeh ◽  
Ali Motie Nasrabadi
Keyword(s):  
Time Series ◽  
Sensitivity Analysis ◽  
Effective Connectivity ◽  
Multivariate Time Series ◽  
Inference System ◽  
Eeg Data ◽  
Adaptive Neurofuzzy Inference System ◽  
Intracranial Electroencephalography ◽  
Fuzzy Network ◽  
The Brain

Abstract The brain is considered to be the most complicated organ in human body. Inferring and quantification of effective (causal) connectivity among regions of the brain is an important step in characterization of its complicated functions. The proposed method is comprised of modeling multivariate time series with Adaptive Neurofuzzy Inference System (ANFIS) and carrying out a sensitivity analysis using Fuzzy network parameters as a new approach to introduce a connectivity measure for detecting causal interactions between interactive input time series. The results of simulations indicate that this method is successful in detecting causal connectivity. After validating the performance of the proposed method on synthetic linear and nonlinear interconnected time series, it is applied to epileptic intracranial Electroencephalography (EEG) signals. The result of applying the proposed method on Freiburg epileptic intracranial EEG data recorded during seizure shows that the proposed method is capable of discriminating between the seizure and non-seizure states of the brain.

Multiscale Symbolic Phase Transfer Entropy in Financial Time Series Classification

2017 ◽  
Vol 16 (02) ◽  
pp. 1750019 ◽  
Author(s):  
Ningning Zhang ◽  
Aijing Lin ◽  
Pengjian Shang
Keyword(s):  
Time Series ◽  
Pharmaceutical Industry ◽  
Phase Transfer ◽  
Financial Time Series ◽  
Market Mechanism ◽  
Transfer Entropy ◽  
Aviation Industry ◽  
Financial Time ◽  
High Degree ◽  
Degree Of Similarity

We address the challenge of classifying financial time series via a newly proposed multiscale symbolic phase transfer entropy (MSPTE). Using MSPTE method, we succeed to quantify the strength and direction of information flow between financial systems and classify financial time series, which are the stock indices from Europe, America and China during the period from 2006 to 2016 and the stocks of banking, aviation industry and pharmacy during the period from 2007 to 2016, simultaneously. The MSPTE analysis shows that the value of symbolic phase transfer entropy (SPTE) among stocks decreases with the increasing scale factor. It is demonstrated that MSPTE method can well divide stocks into groups by areas and industries. In addition, it can be concluded that the MSPTE analysis quantify the similarity among the stock markets. The symbolic phase transfer entropy (SPTE) between the two stocks from the same area is far less than the SPTE between stocks from different areas. The results also indicate that four stocks from America and Europe have relatively high degree of similarity and the stocks of banking and pharmaceutical industry have higher similarity for CA. It is worth mentioning that the pharmaceutical industry has weaker particular market mechanism than banking and aviation industry.

scholarly journals Investigation of EEG signals generated by motor imagery for application in BCIs

2018 ◽  
Author(s):  
Caique de Medeiros Mendes ◽  
Gabriela Castellano ◽  
Carlos Alberto Stefano Filho
Keyword(s):  
Motor Imagery ◽  
Low Complexity ◽  
Eeg Signals ◽  
Brain Response ◽  
K Nearest Neighbors ◽  
Computer Interfaces ◽  
Power Spectral ◽  
Motor Movements ◽  
Accuracy Rates ◽  
The Brain

Motor imagery (MI) is a commonly used strategy in brain-computer interfaces (BCIs) to modify neuronal activity, in which the user, by imagining motor movements, generates signals that can be recorded and interpreted to control a device. In this study, we sought to investigate how the brain response of users during MI happens, by analyzing a database of EEG signals in which healthy subjects were asked to imagine the movement of their right and left hands. Our goal was to recognize patterns associated with this task, through a spectral evaluation of different segments of the signal. Therefore, we estimated the power spectral density (PSD) for each evaluated segment and then used it for classification, via k-nearest neighbors (k-NN). We found that the accuracy rates obtained with k-NN classification were very similar to random, suggesting, mainly, high inter-subjects variability and choice of a low complexity classifier.

scholarly journals Classification of the Children with ADHD and Healthy Children Based on the Directed Phase Transfer Entropy of EEG Signals

2021 ◽  
Author(s):  
Ali Ekhlasi ◽  
Ali Motie Nasrabadi ◽  
Mohammadreza Mohammadi
Keyword(s):  
Frequency Band ◽  
Phase Transfer ◽  
Effective Connectivity ◽  
Brain Regions ◽  
Transfer Entropy ◽  
Healthy Children ◽  
Eeg Signals ◽  
Children With Adhd ◽  
Frequency Bands ◽  
Artificial Neural Network Ann

Purpose: The present study was conducted to investigate and classify two groups of healthy children and children with Attention Deficit Hyperactivity Disorder (ADHD) by Effective Connectivity (EC) measure. Since early detection of ADHD can make the treatment process more effective, it is important to diagnose it using new methods.   Materials and Methods: For this purpose, Effective Connectivity Matrices (ECMs) were constructed based on Electroencephalography (EEG) signals of 61 children with ADHD and 60 healthy children of the same age. ECMs of each individual were obtained by the directed Phase Transfer Entropy (dPTE) between each pair of electrodes. ECMs were calculated in five frequency bands including, delta, theta, alpha, beta, and gamma. Based on ECM, an Effective Connectivity Vector (ECV) was constructed as a feature vector for the classification process. Furthermore, ECV of different frequency bands was pooled in one global ECV (gECV). Multilayer Artificial Neural Network (ANN) was used in the steps of classification and feature selection by the Genetic Algorithm (GA). Results: The highest classification accuracy with the selected features of ECV was related to theta frequency band with 89.7%. After that, the delta frequency band had the highest accuracy with 89.2%. The results of ANN classification and GA on the gECV reported 89.1% of accuracy. Conclusion: Our findings show that the dPTE measure, which determines effective connectivity between the brain regions, can be used to classify between ADHD and healthy groups. The results of the classification have improved compared to some studies that used the functional connectivity measures.

scholarly journals An Efficient Framework for EEG Analysis with Application to Hybrid Brain Computer Interfaces Based on Motor Imagery and P300

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Jinyi Long ◽  
Jue Wang ◽  
Tianyou Yu
Keyword(s):  
Motor Imagery ◽  
Discrimination Performance ◽  
Diagonal Form ◽  
Brain State ◽  
Hybrid Features ◽  
Data Set ◽  
Computer Interfaces ◽  
Combined Features ◽  
Block Diagonal Form ◽  
The Brain

The hybrid brain computer interface (BCI) based on motor imagery (MI) and P300 has been a preferred strategy aiming to improve the detection performance through combining the features of each. However, current methods used for combining these two modalities optimize them separately, which does not result in optimal performance. Here, we present an efficient framework to optimize them together by concatenating the features of MI and P300 in a block diagonal form. Then a linear classifier under a dual spectral norm regularizer is applied to the combined features. Under this framework, the hybrid features of MI and P300 can be learned, selected, and combined together directly. Experimental results on the data set of hybrid BCI based on MI and P300 are provided to illustrate competitive performance of the proposed method against other conventional methods. This provides an evidence that the method used here contributes to the discrimination performance of the brain state in hybrid BCI.

scholarly journals Effects of healthy ageing on activation pattern within the brain during movement and motor imagery: an fMRI study

2019 ◽  
Author(s):  
Sanjay Budhdeo ◽  
Jean-Claude Baron ◽  
Nikhil Sharma
Keyword(s):  
Motor Imagery ◽  
Primary Motor Cortex ◽  
Block Design ◽  
Improve Performance ◽  
Computer Interfaces ◽  
Fmri Study ◽  
Age Dependent ◽  
The Neural Networks ◽  
Shared Networks ◽  
The Brain

AbstractIntroductionMotor imagery (MI) has potential as an intervention to improve performance in neurological disease affecting the motor system and to modulate brain computer interfaces (BCI). We hypothesized that the shared networks of MI and executed movement (EM) would be affected by age. Understanding these changes is important in application of MI in neurological disorders.MethodsUsing tensor-independent component analysis (TICA), we mapped the neural networks involved during MI and EM in 31 healthy volunteers (ages 20-72), who were recruited and screened for their ability to perform imagery. We used an fMRI block-design with MI & rest and EM & rest.ResultsTICA defined 37 independent components (ICs). Eight remained after excluding ICs representing artifacts. These ICs accounted for 35% of variance. While all ICs had greater activation in EM than MI. Two ICs increased with greater age for EM only. These ICs contained a bilateral network of brain areas, including primary motor cortex and cerebellum.ConclusionThis study demonstrates the prominence of shared cerebral networks between MI and EM. There are age-dependent changes to EM activation, while MI activation appeared age independent. This strengthens the rationale for using MI to access the motor networks independent of age.

scholarly journals Estimating Directed Phase-Amplitude Interactions from EEG Data through Kernel-Based Phase Transfer Entropy

2021 ◽  
Vol 11 (21) ◽  
pp. 9803
Author(s):  
Iván De La Pava Panche ◽  
Viviana Gómez-Orozco ◽  
Andrés Álvarez-Meza ◽  
David Cárdenas-Peña ◽  
Álvaro Orozco-Gutiérrez
Keyword(s):  
Working Memory ◽  
Time Series ◽  
Cognitive Task ◽  
Memory Task ◽  
Synthetic Data ◽  
Transfer Entropy ◽  
Alternative Methods ◽  
Interaction Detection ◽  
Eeg Data ◽  
Phase Amplitude

Cross-frequency interactions, a form of oscillatory neural activity, are thought to play an essential role in the integration of distributed information in the brain. Indeed, phase-amplitude interactions are believed to allow for the transfer of information from large-scale brain networks, oscillating at low frequencies, to local, rapidly oscillating neural assemblies. A promising approach to estimating such interactions is the use of transfer entropy (TE), a non-linear, information-theory-based effective connectivity measure. The conventional method involves feeding instantaneous phase and amplitude time series, extracted at the target frequencies, to a TE estimator. In this work, we propose that the problem of directed phase-amplitude interaction detection is recast as a phase TE estimation problem, under the hypothesis that estimating TE from data of the same nature, i.e., two phase time series, will improve the robustness to the common confounding factors that affect connectivity measures, such as the presence of high noise levels. We implement our proposal using a kernel-based TE estimator, defined in terms of Renyi’s α entropy, which has successfully been used to compute single-trial phase TE. We tested our approach on the synthetic data generated through a simulation model capable of producing a time series with directed phase-amplitude interactions at two given frequencies, and on EEG data from a cognitive task designed to activate working memory, a memory system whose underpinning mechanisms are thought to include phase–amplitude couplings. Our proposal detected statistically significant interactions between the simulated signals at the desired frequencies for the synthetic data, identifying the correct direction of the interaction. It also displayed higher robustness to noise than the alternative methods. The results attained for the working memory data showed that the proposed approach codes connectivity patterns based on directed phase–amplitude interactions, that allow for the different cognitive load levels of the working memory task to be differentiated.

The Efficiency of the Brain-Computer Interfaces Based on Motor Imagery with Tactile and Visual Feedback

2018 ◽  
Vol 44 (3) ◽  
pp. 280-288 ◽  
Author(s):  
M. V. Lukoyanov ◽  
S. Yu. Gordleeva ◽  
A. S. Pimashkin ◽  
N. A. Grigor’ev ◽  
A. V. Savosenkov ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Visual Feedback ◽  
Brain Computer Interfaces ◽  
Computer Interfaces ◽  
The Brain

scholarly journals Effective Connectivity for Decoding Electroencephalographic Motor Imagery Using a Probabilistic Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6570
Author(s):  
Muhammad Ahsan Awais ◽  
Mohd Zuki Yusoff ◽  
Danish M. Khan ◽  
Norashikin Yahya ◽  
Nidal Kamel ◽  
...  
Keyword(s):  
Neural Network ◽  
Motor Imagery ◽  
Error Rate ◽  
Classification Accuracy ◽  
Probabilistic Neural Network ◽  
Brain Connectivity ◽  
Effective Connectivity ◽  
Brain Regions ◽  
Brain Computer Interfaces ◽  
Computer Interfaces

Motor imagery (MI)-based brain–computer interfaces have gained much attention in the last few years. They provide the ability to control external devices, such as prosthetic arms and wheelchairs, by using brain activities. Several researchers have reported the inter-communication of multiple brain regions during motor tasks, thus making it difficult to isolate one or two brain regions in which motor activities take place. Therefore, a deeper understanding of the brain’s neural patterns is important for BCI in order to provide more useful and insightful features. Thus, brain connectivity provides a promising approach to solving the stated shortcomings by considering inter-channel/region relationships during motor imagination. This study used effective connectivity in the brain in terms of the partial directed coherence (PDC) and directed transfer function (DTF) as intensively unconventional feature sets for motor imagery (MI) classification. MANOVA-based analysis was performed to identify statistically significant connectivity pairs. Furthermore, the study sought to predict MI patterns by using four classification algorithms—an SVM, KNN, decision tree, and probabilistic neural network. The study provides a comparative analysis of all of the classification methods using two-class MI data extracted from the PhysioNet EEG database. The proposed techniques based on a probabilistic neural network (PNN) as a classifier and PDC as a feature set outperformed the other classification and feature extraction techniques with a superior classification accuracy and a lower error rate. The research findings indicate that when the PDC was used as a feature set, the PNN attained the greatest overall average accuracy of 98.65%, whereas the same classifier was used to attain the greatest accuracy of 82.81% with the DTF. This study validates the activation of multiple brain regions during a motor task by achieving better classification outcomes through brain connectivity as compared to conventional features. Since the PDC outperformed the DTF as a feature set with its superior classification accuracy and low error rate, it has great potential for application in MI-based brain–computer interfaces.

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