scholarly journals Identifying Patients with Poststroke Mild Cognitive Impairment by Pattern Recognition of Working Memory Load-Related ERP

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Xiaoou Li ◽  
Yuning Yan ◽  
Wenshi Wei
Keyword(s):  
Working Memory ◽  
Memory Load ◽  
Treatment Strategies ◽  
Classification Performance ◽  
Support Vector ◽  
Healthy Controls ◽  
Stroke Patients ◽  
Mode Decomposition ◽  
Kernel Support Vector Machine ◽  
Stimulus Features

The early detection of subjects with probable cognitive deficits is crucial for effective appliance of treatment strategies. This paper explored a methodology used to discriminate between evoked related potential signals of stroke patients and their matched control subjects in a visual working memory paradigm. The proposed algorithm, which combined independent component analysis and orthogonal empirical mode decomposition, was applied to extract independent sources. Four types of target stimulus features including P300 peak latency, P300 peak amplitude, root mean square, and theta frequency band power were chosen. Evolutionary multiple kernel support vector machine (EMK-SVM) based on genetic programming was investigated to classify stroke patients and healthy controls. Based on 5-fold cross-validation runs, EMK-SVM provided better classification performance compared with other state-of-the-art algorithms. Comparing stroke patients with healthy controls using the proposed algorithm, we achieved the maximum classification accuracies of 91.76% and 82.23% for 0-back and 1-back tasks, respectively. Overall, the experimental results showed that the proposed method was effective. The approach in this study may eventually lead to a reliable tool for identifying suitable brain impairment candidates and assessing cognitive function.

scholarly journals EEG Artifact Removal System for Depression Using a Hybrid Denoising Approach

2021 ◽  
Author(s):  
Chamandeep Kaur ◽  
◽  
Preeti Singh ◽  
Sukhtej Sahni ◽  
◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Wavelet Packet ◽  
Mode Selection ◽  
Classification Performance ◽  
Support Vector ◽  
Fluctuation Analysis ◽  
Analysis Tool ◽  
Denoising Method ◽  
Mode Decomposition ◽  
Mixing Problem

Introduction: A number of computer- aided diagnosis systems for depression are being offered to be used by the clinicians as a method to authorize the diagnosis. EEG may be used as an objective analysis tool for identification of depression in the initial stage so as to avoid it from reaching a severe and permanent state. However, artifact contamination reduces the accuracy in EEG signal processing systems. Methods: This work proposes a novel denoising method based on EMD (Empirical Mode Decomposition) with detrended fluctuation analysis (DFA) and wavelet packet transform. As the first stage, real EEG recordings corresponding to depression patients are decomposed into various mode functions by applying EMD. Then, DFA is used as the mode selection criteria. Further wavelet packets decomposition (WPD) based evaluation is used to extract the cleaner signal. Results: Simulations have been carried out on real EEG databases for depression to demonstrate the effectiveness of the proposed techniques. To conclude the efficacy of the proposed technique, SNR and MAE have been identified. The results show improved signal to noise ratio and lower values of MAE for the combined EMD-DFA-WPD technique. Also, Random Forest and SVM (Support Vector Machine) based classification shows improved accuracy of 98.51% and 98.10% for the proposed denoising technique. Whereas the accuracy of the EMD- DFA is 98.01% and 95.81% and EMD combined with DWT technique is 98.0% and 97.21% for the EMD- DFA technique for RF and SVM respectively as compared to the proposed method. Also, the classification performance for both the classifiers has been compared with and without denoising to highlight the effectiveness of the proposed technique. Conclusion: Proposed denoising system results in better classification of depressed and healthy individuals resulting in better diagnosing system. These results can be further analyzed using other approaches as a solution to the mode mixing problem of EMD approach.

scholarly journals Identifying Individuals With Mild Cognitive Impairment Using Working Memory-Induced Intra-Subject Variability of Resting-State EEGs

2021 ◽  
Vol 15 ◽  
Author(s):  
Thanh-Tung Trinh ◽  
Chia-Fen Tsai ◽  
Yu-Tsung Hsiao ◽  
Chun-Ying Lee ◽  
Chien-Te Wu ◽  
...  
Keyword(s):  
Working Memory ◽  
Feature Extraction ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Early Detection ◽  
Resting State ◽  
Spectral Power ◽  
Classification Performance ◽  
Support Vector ◽  
Subject Variability

Individuals with mild cognitive impairment (MCI) are at high risk of developing into dementia (e. g., Alzheimer's disease, AD). A reliable and effective approach for early detection of MCI has become a critical challenge. Although compared with other costly or risky lab tests, electroencephalogram (EEG) seems to be an ideal alternative measure for early detection of MCI, searching for valid EEG features for classification between healthy controls (HCs) and individuals with MCI remains to be largely unexplored. Here, we design a novel feature extraction framework and propose that the spectral-power-based task-induced intra-subject variability extracted by this framework can be an encouraging candidate EEG feature for the early detection of MCI. In this framework, we extracted the task-induced intra-subject spectral power variability of resting-state EEGs (as measured by a between-run similarity) before and after participants performing cognitively exhausted working memory tasks as the candidate feature. The results from 74 participants (23 individuals with AD, 24 individuals with MCI, 27 HC) showed that the between-run similarity over the frontal and central scalp regions in the HC group is higher than that in the AD or MCI group. Furthermore, using a feature selection scheme and a support vector machine (SVM) classifier, the between-run similarity showed encouraging leave-one-participant-out cross-validation (LOPO-CV) classification performance for the classification between the MCI and HC (80.39%) groups and between the AD vs. HC groups (78%), and its classification performance is superior to other widely-used features such as spectral powers, coherence, and the complexity estimated by Katz's method extracted from single-run resting-state EEGs (a common approach in previous studies). The results based on LOPO-CV, therefore, suggest that the spectral-power-based task-induced intra-subject EEG variability extracted by the proposed feature extraction framework has the potential to serve as a neurophysiological feature for the early detection of MCI in individuals.

scholarly journals On the Performance of Variational Mode Decomposition-Based Radio Frequency Fingerprinting of Bluetooth Devices

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1704 ◽  
Author(s):  
Alghannai Aghnaiya ◽  
Yaser Dalveren ◽  
Ali Kara
Keyword(s):  
Radio Frequency ◽  
Classification Performance ◽  
Performance Comparison ◽  
Support Vector ◽  
Performance Bounds ◽  
Variational Mode Decomposition ◽  
Transient Signals ◽  
Mode Decomposition ◽  
Band Limited

Radio frequency fingerprinting (RFF) is one of the communication network’s security techniques based on the identification of the unique features of RF transient signals. However, extracting these features could be burdensome, due to the nonstationary nature of transient signals. This may then adversely affect the accuracy of the identification of devices. Recently, it has been shown that the use of variational mode decomposition (VMD) in extracting features from Bluetooth (BT) transient signals offers an efficient way to improve the classification accuracy. To do this, VMD has been used to decompose transient signals into a series of band-limited modes, and higher order statistical (HOS) features are extracted from reconstructed transient signals. In this study, the performance bounds of VMD in RFF implementation are scrutinized. Firstly, HOS features are extracted from the band-limited modes, and then from the reconstructed transient signals directly. Performance comparison due to both HOS feature sets is presented. Moreover, the lower SNR bound within which the VMD can achieve acceptable accuracy in the classification of BT devices is determined. The approach has been tested experimentally with BT devices by employing a Linear Support Vector Machine (LSVM) classifier. According to the classification results, a higher classification performance is achieved (~4% higher) at lower SNR levels (−5–5 dB) when HOS features are extracted from band-limited modes in the implementation of VMD in RFF of BT devices.

ENSEMBLE EMPIRICAL MODE DECOMPOSITION FOR HYPERSPECTRAL IMAGE CLASSIFICATION

2012 ◽  
Vol 04 (01n02) ◽  
pp. 1250003 ◽  
Author(s):  
MIN ZHANG ◽  
YI SHEN
Keyword(s):  
Empirical Mode Decomposition ◽  
Hyperspectral Image ◽  
Ensemble Empirical Mode Decomposition ◽  
Classification Performance ◽  
Finite Amplitude ◽  
Support Vector ◽  
Time Frequency ◽  
Original Dataset ◽  
Mode Decomposition ◽  
Nonstationary Data

Ensemble empirical mode decomposition (EEMD) is a novel adaptive time-frequency analysis method, which is particularly suitable for extracting useful information from noisy nonlinear or nonstationary data. This paper presents the utilization of EEMD for hyperspectral images to extract signals from them, generated in noisy nonlinear and nonstationary processes. First, EEMD is applied to each hyperspectral image band and defines the true intrinsic mode function (IMF) components as the mean of an ensemble of trials, each consisting of the signal plus a white noise of finite amplitude. After EEMD is performed to each band, new bands are reconstructed as the sum of IMFs and the trend, and classification is executed over these new bands. Finally, the hyperspectral image with new bands was classified with support vector machine (SVM) to show the classification performance of the proposed approach. Experimental results show that the utilization of the EEMD significantly increases the classification accuracy compared to the dataset processed by empirical mode decomposition (EMD) and the original dataset.

Bearing degradation state recognition based on kernel PCA and wavelet kernel SVM

2014 ◽  
Vol 229 (15) ◽  
pp. 2827-2834 ◽  
Author(s):  
Shaojiang Dong ◽  
Dihua Sun ◽  
Baoping Tang ◽  
Zhengyuan Gao ◽  
Yingrui Wang ◽  
...  
Keyword(s):  
Principal Component ◽  
Kernel Principal Component Analysis ◽  
Support Vector ◽  
Feature Extraction Method ◽  
State Identification ◽  
Mode Decomposition ◽  
Kernel Support Vector Machine ◽  
Characteristic Features ◽  
Nonlinear Feature Extraction ◽  
Nonlinear Feature

In order to effectively recognize the bearing’s running state, a new method based on kernel principal component analysis (KPCA) and the Morlet wavelet kernel support vector machine (MWSVM) was proposed. First, the gathered vibration signals were decomposed by the empirical mode decomposition (EMD) to obtain the corresponding intrinsic mode function (IMF). The EMD energy entropy that includes dominant fault information is defined as the characteristic features. However, the extracted features remained high-dimensional, and excessive redundant information still existed. Therefore, the nonlinear feature extraction method KPCA was introduced to extract the characteristic features and to reduce the dimension. The extracted characteristic features were inputted into the MWSVM to train and construct the running state identification model, and the bearing’s running state identification was thereby realized. Cases of test and actual were analyzed. The results validate the effectiveness of the proposed algorithm.

scholarly journals Mental Task Classification Scheme Utilizing Correlation Coefficient Extracted from Interchannel Intrinsic Mode Function

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Md. Mostafizur Rahman ◽  
Shaikh Anowarul Fattah
Keyword(s):  
Correlation Coefficient ◽  
Classification Performance ◽  
Support Vector ◽  
Svm Classifier ◽  
Eeg Signal ◽  
Statistical Features ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
High Level ◽  
Mental Tasks

In view of recent increase of brain computer interface (BCI) based applications, the importance of efficient classification of various mental tasks has increased prodigiously nowadays. In order to obtain effective classification, efficient feature extraction scheme is necessary, for which, in the proposed method, the interchannel relationship among electroencephalogram (EEG) data is utilized. It is expected that the correlation obtained from different combination of channels will be different for different mental tasks, which can be exploited to extract distinctive feature. The empirical mode decomposition (EMD) technique is employed on a test EEG signal obtained from a channel, which provides a number of intrinsic mode functions (IMFs), and correlation coefficient is extracted from interchannel IMF data. Simultaneously, different statistical features are also obtained from each IMF. Finally, the feature matrix is formed utilizing interchannel correlation features and intrachannel statistical features of the selected IMFs of EEG signal. Different kernels of the support vector machine (SVM) classifier are used to carry out the classification task. An EEG dataset containing ten different combinations of five different mental tasks is utilized to demonstrate the classification performance and a very high level of accuracy is achieved by the proposed scheme compared to existing methods.

Striatal function in relation to negative symptoms in schizophrenia

2011 ◽  
Vol 42 (2) ◽  
pp. 267-282 ◽  
Author(s):  
S. Ehrlich ◽  
A. Yendiki ◽  
D. N. Greve ◽  
D. S. Manoach ◽  
B.-C. Ho ◽  
...  
Keyword(s):  
Working Memory ◽  
Gray Matter ◽  
Negative Symptoms ◽  
Memory Load ◽  
Memory Performance ◽  
Region Of Interest ◽  
Healthy Controls ◽  
High Burden ◽  
Gray Matter Volumes ◽  
Striatal Function

BackgroundPrevious studies have suggested that motivational aspects of executive functioning, which may be disrupted in schizophrenia patients with negative symptoms, are mediated in part by the striatum. Negative symptoms have been linked to impaired recruitment of both the striatum and the dorsolateral prefrontal cortex (DLPFC). Here we tested the hypothesis that negative symptoms are associated primarily with striatal dysfunction, using functional magnetic resonance imaging (fMRI).MethodWorking-memory load-dependent activation and gray matter volumes of the striatum and DLPFC were measured using a region-of-interest (ROI) approach, in 147 schizophrenia patients and 160 healthy controls. In addition to testing for a linear relationships between striatal function and negative symptoms, we chose a second, categorical analytic strategy in which we compared three demographically and behaviorally matched subgroups: patients with a high burden of negative symptoms, patients with minimal negative symptoms, and healthy subjects.ResultsThere were no differences in striatal response magnitudes between schizophrenia patients and healthy controls, but right DLPFC activity was higher in patients than in controls. Negative symptoms were inversely associated with striatal, but not DLPFC, activity. In addition, patients with a high burden of negative symptoms exhibited significantly lower bilateral striatal, but not DLPFC, activation than schizophrenia patients with minimal negative symptoms. Working memory performance, antipsychotic exposure and changes in gray matter volumes did not account for these differences.ConclusionsThese data provide further evidence for a robust association between negative symptoms and diminished striatal activity. Future work will determine whether low striatal activity in schizophrenia patients could serve as a reliable biomarker for negative symptoms.

scholarly journals Unity and diversity in working memory load: Evidence for the separability of the executive functions updating and inhibition using machine learning

2018 ◽  
Author(s):  
Tanja Krumpe ◽  
Christian Scharinger ◽  
Wolfgang Rosenstiel ◽  
Peter Gerjets ◽  
Martin Spüler
Keyword(s):  
Machine Learning ◽  
Working Memory ◽  
Executive Functions ◽  
Memory Load ◽  
Brain Computer Interface ◽  
Theoretical Models ◽  
Computer Interface ◽  
Support Vector ◽  
Learning Approaches ◽  
Eeg Data

AbstractObjectiveAccording to current theoretical models of working memory (WM), executive functions (EFs) like updating, inhibition and shifting play an important role in WM functioning. The models state that EFs highly correlate with each other but also have some individual variance which makes them separable processes. Since this theory has mostly been substantiated with behavioral data like reaction time and the ability to execute a task correctly, the aim of this paper is to find evidence for diversity (unique properties) of the EFs updating and inhibition in neural correlates of EEG data by means of using brain-computer interface (BCI) methods as a research tool. To highlight the benefit of this approach we compare this new methodology to classical analysis approaches.MethodsAn existing study has been reinvestigated by applying neurophysiological analysis in combination with support vector machine (SVM) classification on recorded electroenzephalography (EEG) data to determine the separability and variety of the two EFs updating and inhibition on a single trial basis.ResultsThe SVM weights reveal a set of distinct features as well as a set of shared features for the two EFs updating and inhibition in the theta and the alpha band power.SignificanceIn this paper we find evidence that correlates for unity and diversity of EFs can be found in neurophysiological data. Machine learning approaches reveal shared but also distinct properties for the EFs. This study shows that using methods from brain-computer interface (BCI) research, like machine learning, as a tool for the validation of psychological models and theoretical constructs is a new approach that is highly versatile and could lead to many new insights.

scholarly journals EEG Artifact Removal System for Depression Using a Hybrid Denoising Approach

2021 ◽  
Author(s):  
Chamandeep Kaur ◽  
Keyword(s):  
Signal To Noise Ratio ◽  
Wavelet Packet ◽  
Mode Selection ◽  
Classification Performance ◽  
Support Vector ◽  
Fluctuation Analysis ◽  
Analysis Tool ◽  
Denoising Method ◽  
Mode Decomposition ◽  
Mixing Problem

Introduction: A number of computer- aided diagnosis systems for depression are being offered to be used by the clinicians as a method to authorize the diagnosis. EEG may be used as an objective analysis tool for identification of depression in the initial stage so as to avoid it from reaching a severe and permanent state. However, artifact contamination reduces the accuracy in EEG signal processing systems. Methods: This work proposes a novel denoising method based on EMD (Empirical Mode Decomposition) with detrended fluctuation analysis (DFA) and wavelet packet transform. As the first stage, real EEG recordings corresponding to depression patients are decomposed into various mode functions by applying EMD. Then, DFA is used as the mode selection criteria. Further wavelet packets decomposition (WPD) based evaluation is used to extract the cleaner signal. Results: Simulations have been carried out on real EEG databases for depression to demonstrate the effectiveness of the proposed techniques. To conclude the efficacy of the proposed technique, SNR and MAE have been identified. The results show improved signal to noise ratio and lower values of MAE for the combined EMD-DFA-WPD technique. Also, Random Forest and SVM (Support Vector Machine) based classification shows improved accuracy of 98.51% and 98.10% for the proposed denoising technique. Whereas the accuracy of the EMD- DFA is 98.01% and 95.81% and EMD combined with DWT technique is 98.0% and 97.21% for the EMD- DFA technique for RF and SVM respectively as compared to the proposed method. Also, the classification performance for both the classifiers has been compared with and without denoising to highlight the effectiveness of the proposed technique. Conclusion: Proposed denoising system results in better classification of depressed and healthy individuals resulting in better diagnosing system. These results can be further analyzed using other approaches as a solution to the mode mixing problem of EMD approach.

scholarly journals Interpreting neural decoding models using grouped model reliance

2019 ◽  
Author(s):  
Simon Valentin ◽  
Maximilian Harkotte ◽  
Tzvetan Popov
Keyword(s):  
Machine Learning ◽  
Working Memory ◽  
Memory Load ◽  
Memory Task ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Neural Decoding ◽  
Working Memory Load ◽  
Eeg Data

AbstractThe application of machine learning algorithms for decoding psychological constructs based on neural data is becoming increasingly popular. However, there is a need for methods that allow to interpret trained decoding models, as a step towards bridging the gap between theory-driven cognitive neuroscience and data-driven decoding approaches. The present study demonstrates grouped model reliance as a model-agnostic permutation-based approach to this problem. Grouped model reliance indicates the extent to which a trained model relies on conceptually related groups of variables, such as frequency bands or regions of interest in electroencephalographic (EEG) data. As a case study to demonstrate the method, random forest and support vector machine models were trained on within-participant single-trial EEG data from a Sternberg working memory task. Participants were asked to memorize a sequence of digits (0–9), varying randomly in length between one, four and seven digits, where EEG recordings for working memory load estimation were taken from a 3-second retention interval. Present results confirm previous findings in so far, as both random forest and support vector machine models relied on alpha-band activity in most subjects. However, as revealed by further analyses, patterns in frequency and particularly topography varied considerably between individuals, pointing to more pronounced inter-individual differences than reported previously.Author summaryModern machine learning algorithms currently receive considerable attention for their predictive power in neural decoding applications. However, there is a need for methods that make such predictive models interpretable. In the present work, we address the problem of assessing which aspects of the input data a trained model relies upon to make predictions. We demonstrate the use of grouped model-reliance as a generally applicable method for interpreting neural decoding models. Illustrating the method on a case study, we employed an experimental design in which a comparably small number of participants (10) completed a large number of trials (972) over multiple electroencephalography (EEG) recording sessions from a Sternberg working memory task. Trained decoding models consistently relied on alpha frequency activity, which is in line with existing research on the relationship between neural oscillations and working memory. However, our analyses also indicate large inter-individual variability with respect to the relation between activity patterns and working memory load in frequency and topography. Taken together, we argue that grouped model reliance provides a useful tool to better understand the workings of (sometimes otherwise black-box) decoding models.

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