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Ruiz de Miras J, Casali AG, Massimini M, Ibáñez-Molina AJ, Soriano MF, Iglesias-Parro S. FDI: A MATLAB tool for computing the fractal dimension index of sources reconstructed from EEG data. Comput Biol Med 2024; 179:108871. [PMID: 39002315 DOI: 10.1016/j.compbiomed.2024.108871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Revised: 07/08/2024] [Accepted: 07/08/2024] [Indexed: 07/15/2024]
Abstract
BACKGROUND The fractal dimension (FD) is a valuable tool for analysing the complexity of neural structures and functions in the human brain. To assess the spatiotemporal complexity of brain activations derived from electroencephalogram (EEG) signals, the fractal dimension index (FDI) was developed. This measure integrates two distinct complexity metrics: 1) integration FD, which calculates the FD of the spatiotemporal coordinates of all significantly active EEG sources (4DFD); and 2) differentiation FD, determined by the complexity of the temporal evolution of the spatial distribution of cortical activations (3DFD), estimated via the Higuchi FD [HFD(3DFD)]. The final FDI value is the product of these two measurements: 4DFD × HFD(3DFD). Although FDI has shown utility in various research on neurological and neurodegenerative disorders, existing literature lacks standardized implementation methods and accessible coding resources, limiting wider adoption within the field. METHODS We introduce an open-source MATLAB software named FDI for measuring FDI values in EEG datasets. RESULTS By using CUDA for leveraging the GPU massive parallelism to optimize performance, our software facilitates efficient processing of large-scale EEG data while ensuring compatibility with pre-processed data from widely used tools such as Brainstorm and EEGLab. Additionally, we illustrate the applicability of FDI by demonstrating its usage in two neuroimaging studies. Access to the MATLAB source code and a precompiled executable for Windows system is provided freely. CONCLUSIONS With these resources, neuroscientists can readily apply FDI to investigate cortical activity complexity within their own studies.
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Affiliation(s)
- Juan Ruiz de Miras
- Department of Software Engineering, University of Granada, Granada, Spain.
| | - Adenauer G Casali
- Institute of Science and Technology, Federal University of São Paulo, São Paulo, Brazil
| | - Marcello Massimini
- Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy; Fondazione Don Carlo Gnocchi, Milan, Italy
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Guo Z, Wang J, Jing T, Fu L. Investigating the interpretability of schizophrenia EEG mechanism through a 3DCNN-based hidden layer features aggregation framework. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 247:108105. [PMID: 38447316 DOI: 10.1016/j.cmpb.2024.108105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 02/07/2024] [Accepted: 02/26/2024] [Indexed: 03/08/2024]
Abstract
BACKGROUND AND OBJECTIVE Electroencephalogram (EEG) signals record brain activity, with growing interest in quantifying neural activity through complexity analysis as a potential biological marker for schizophrenia. Presently, EEG complexity analysis primarily relies on manual feature extraction, which is subjective and yields varied findings in studies involving schizophrenia and healthy controls. METHODS This study aims to leverage deep learning methods for enhanced EEG complexity exploration, aiding early schizophrenia screening and diagnosis. Our proposed approach utilizes a three-dimensional Convolutional Neural Network (3DCNN) to extract enhanced data features for early schizophrenia identification and subsequent complexity analysis. Leveraging the spatiotemporal capabilities of 3DCNN, we extract advanced latent features and employ knowledge distillation to reintegrate these features into the original channels, creating feature-enhanced data. RESULTS We employ a 10-fold cross-validation strategy, achieving the average accuracies of 99.46% and 98.06% in subject-dependent experiments on Dataset 1(14SZ and 14HC) and Dataset 2 (45SZ and 39HC). The average accuracy for subject-independent is 96.04% and 92.67% on both datasets. Feature extraction and classification are conducted on both the re-aggregated data and the original data. Our results demonstrate that re-aggregated data exhibit superior classification performance and a more stable training process after feature extraction. In the complexity analysis of re-aggregated data, we observe lower entropy features in schizophrenic patients compared to healthy controls, with more pronounced differences in the temporal and frontal lobes. Analyzing Katz's Fractal Dimension (KFD) across three sub-bands of lobe channels reveals the lowest α band KFD value in schizophrenia patients. CONCLUSIONS This emphasizes the ability of our method to enhance the discrimination and interpretability in schizophrenia detection and analysis. Our approach enhances the potential for EEG-based schizophrenia diagnosis by leveraging deep learning, offering superior discrimination capabilities and richer interpretive insights.
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Affiliation(s)
- Zhifen Guo
- College of Information Science and Engineering, Northeastern University, Shenyang, China.
| | - Jiao Wang
- College of Information Science and Engineering, Northeastern University, Shenyang, China.
| | - Tianyu Jing
- College of Information Science and Engineering, Northeastern University, Shenyang, China.
| | - Longyue Fu
- College of Information Science and Engineering, Northeastern University, Shenyang, China.
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Singh J, Singh S, Gupta S, Chavan BS. Cognitive Remediation and Schizophrenia: Effects on Brain Complexity. Neurosci Lett 2023; 808:137268. [PMID: 37100222 DOI: 10.1016/j.neulet.2023.137268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 04/10/2023] [Accepted: 04/20/2023] [Indexed: 04/28/2023]
Abstract
The objective of this study is to investigate nonlinear neural dynamics of chronic patients with schizophrenia following 3 months of cognitive remediation and to find correlations with neuropsychological measures of cognition. Twenty nine patients were randomized to Cognitive Training (CT) and Treatment as Usual (TAU) group. The system complexity is estimated by Correlation Dimension (D2) and Largest Lyapunov Exponent (LLE) from the reconstructed attractor of the underlying system. Significant increase in dimensional complexity (D2) over time is observed in prefrontal and medial frontal-central regions in eyes open and arithmetic condition; and posterior parietal-occipital region under eyes closed after 3 months. Dynamical complexity (LLE) significantly decreased over time in medial left central region under eyes closed and eyes open condition; prefrontal region in eyes open and lateral right temporal region in arithmetic condition. Interaction is significant for medial left central region with TAU group exhibiting greater decrease in LLE compared to CT group. The CT group showed significant correlation of increased D2 with focused attention. In this study it is found that patients with schizophrenia exhibit higher dimensional and lower dynamical complexity over time indicating improvement in neurodynamics of underlying physiological system.
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Affiliation(s)
- Jaskirat Singh
- Computational Neuroscience Lab, UIET, Panjab University, Chandigarh Pincode: 160014, India
| | - Sukhwinder Singh
- Computational Neuroscience Lab, UIET, Panjab University, Chandigarh Pincode: 160014, India.
| | - Savita Gupta
- Computational Neuroscience Lab, UIET, Panjab University, Chandigarh Pincode: 160014, India.
| | - B S Chavan
- Department of Psychiatry, Government Medical College and Hospital, Sector 32, Chandigarh, Pincode:160032, India.
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4
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Lord B, Allen JJB. Evaluating EEG complexity metrics as biomarkers for depression. Psychophysiology 2023:e14274. [PMID: 36811526 DOI: 10.1111/psyp.14274] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 12/23/2022] [Accepted: 01/13/2023] [Indexed: 02/24/2023]
Abstract
Nonlinear EEG analysis offers the potential for both increased diagnostic accuracy and deeper mechanistic understanding of psychopathology. EEG complexity measures have previously been shown to positively correlate with clinical depression. In this study, resting state EEG recordings were taken across multiple sessions and days with both eyes open and eyes closed conditions from a total of 306 subjects, 62 of which were in a current depressive episode, and 81 of which had a history of diagnosed depression but were not currently depressed. Three different EEG montages (mastoids, average, and Laplacian) were also computed. Higuchi fractal dimension (HFD) and sample entropy (SampEn) were calculated for each unique condition. The complexity metrics showed high internal consistency within session and high stability across days. Higher complexity was found in open-eye recordings compared to closed eyes. The predicted correlation between complexity and depression was not found. However, an unexpected sex effect was observed, in which males and females exhibited different topographic patterns of complexity.
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Affiliation(s)
- Brian Lord
- Department of Psychology, University of Arizona, Tucson, Arizona, USA
| | - John J B Allen
- Department of Psychology, University of Arizona, Tucson, Arizona, USA
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Lau ZJ, Pham T, Chen SHA, Makowski D. Brain entropy, fractal dimensions and predictability: A review of complexity measures for EEG in healthy and neuropsychiatric populations. Eur J Neurosci 2022; 56:5047-5069. [PMID: 35985344 PMCID: PMC9826422 DOI: 10.1111/ejn.15800] [Citation(s) in RCA: 69] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 07/20/2022] [Accepted: 08/10/2022] [Indexed: 01/11/2023]
Abstract
There has been an increasing trend towards the use of complexity analysis in quantifying neural activity measured by electroencephalography (EEG) signals. On top of revealing complex neuronal processes of the brain that may not be possible with linear approaches, EEG complexity measures have also demonstrated their potential as biomarkers of psychopathology such as depression and schizophrenia. Unfortunately, the opacity of algorithms and descriptions originating from mathematical concepts have made it difficult to understand what complexity is and how to draw consistent conclusions when applied within psychology and neuropsychiatry research. In this review, we provide an overview and entry-level explanation of existing EEG complexity measures, which can be broadly categorized as measures of predictability and regularity. We then synthesize complexity findings across different areas of psychological science, namely, in consciousness research, mood and anxiety disorders, schizophrenia, neurodevelopmental and neurodegenerative disorders, as well as changes across the lifespan, while addressing some theoretical and methodological issues underlying the discrepancies in the data. Finally, we present important considerations when choosing and interpreting these metrics.
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Affiliation(s)
- Zen J. Lau
- School of Social SciencesNanyang Technological UniversitySingapore
| | - Tam Pham
- School of Social SciencesNanyang Technological UniversitySingapore
| | - S. H. Annabel Chen
- School of Social SciencesNanyang Technological UniversitySingapore,Centre for Research and Development in LearningNanyang Technological UniversitySingapore,Lee Kong Chian School of MedicineNanyang Technological UniversitySingapore,National Institute of EducationNanyang Technological UniversitySingapore
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6
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Roughness Scaling Extraction Accelerated by Dichotomy-Binary Strategy and Its Application to Milling Vibration Signal. MATHEMATICS 2022. [DOI: 10.3390/math10071105] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Fractal algorithms for signal analysis are developed from geometric fractals and can be used to describe various complex signals in nature. A roughness scaling extraction algorithm with first-order flattening (RSE-f1) was shown in our previous studies to have a high accuracy, strong noise resistance, and a unique capacity to recognize the complexity of non-fractals that are common in signals. In this study, its disadvantage of a long calculation duration was addressed by using a dichotomy-binary strategy. The accelerated RSE-f1 algorithm (A-RSE-f1) retains the three above-mentioned advantages of the original algorithm according to theoretical analysis and artificial signal testing, while its calculation speed is significantly accelerated by 13 fold, which also makes it faster than the typical Higuchi algorithm. Afterwards, the vibration signals of the milling process are analyzed using the A-RSE-f1 algorithm, demonstrating the ability to distinguish different machining statuses (idle, stable, and chatter) effectively. The results of this study demonstrate that the RSE algorithm has been improved to meet the requirements of practical engineering with both a fast speed and a high performance.
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Hu H, Pu Z, Wang P. A flexible and accurate method for electroencephalography rhythms extraction based on circulant singular spectrum analysis. PeerJ 2022; 10:e13096. [PMID: 35345585 PMCID: PMC8957273 DOI: 10.7717/peerj.13096] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 02/20/2022] [Indexed: 01/12/2023] Open
Abstract
Rhythms extraction from electroencephalography (EEG) signals can be used to monitor the physiological and pathological states of the brain and has attracted much attention in recent studies. A flexible and accurate method for EEG rhythms extraction was proposed by incorporating a novel circulant singular spectrum analysis (CiSSA). The EEG signals are decomposed into the sum of a set of orthogonal reconstructed components (RCs) at known frequencies. The frequency bandwidth of each RC is limited to a particular brain rhythm band, with no frequency mixing between different RCs. The RCs are then grouped flexibly to extract the desired EEG rhythms based on the known frequencies. The extracted brain rhythms are accurate and no mixed components of other rhythms or artifacts are included. Simulated EEG data based on the Markov Process Amplitude EEG model and experimental EEG data in the eyes-open and eyes-closed states were used to verify the CiSSA-based method. The results showed that the CiSSA-based method is flexible in alpha rhythms extraction and has a higher accuracy in distinguishing between the eyes-open and eyes-closed states, compared with the basic SSA method, the wavelet decomposition method, and the finite impulse response filtering method.
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EEG-Based Identification of Emotional Neural State Evoked by Virtual Environment Interaction. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19042158. [PMID: 35206341 PMCID: PMC8872045 DOI: 10.3390/ijerph19042158] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 02/11/2022] [Accepted: 02/11/2022] [Indexed: 11/17/2022]
Abstract
Classifying emotional states is critical for brain–computer interfaces and psychology-related domains. In previous studies, researchers have tried to identify emotions using neural data such as electroencephalography (EEG) signals or brain functional magnetic resonance imaging (fMRI). In this study, we propose a machine learning framework for emotion state classification using EEG signals in virtual reality (VR) environments. To arouse emotional neural states in brain signals, we provided three VR stimuli scenarios to 15 participants. Fifty-four features were extracted from the collected EEG signals under each scenario. To find the optimal classification in our research design, three machine learning algorithms (XGBoost classifier, support vector classifier, and logistic regression) were applied. Additionally, various class conditions were used in machine learning classifiers to validate the performance of our framework. To evaluate the classification performance, we utilized five evaluation metrics (precision, recall, f1-score, accuracy, and AUROC). Among the three classifiers, the XGBoost classifiers showed the best performance under all experimental conditions. Furthermore, the usability of features, including differential asymmetry and frequency band pass categories, were checked from the feature importance of XGBoost classifiers. We expect that our framework can be applied widely not only to psychological research but also to mental health-related issues.
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Shamsi E, Ahmadi-Pajouh MA, Seifi Ala T. Higuchi fractal dimension: An efficient approach to detection of brain entrainment to theta binaural beats. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102580] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
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10
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Ismael AM, Alçin ÖF, Abdalla KH, Şengür A. Two-stepped majority voting for efficient EEG-based emotion classification. Brain Inform 2020; 7:9. [PMID: 32940803 PMCID: PMC7498529 DOI: 10.1186/s40708-020-00111-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2020] [Accepted: 09/08/2020] [Indexed: 12/24/2022] Open
Abstract
In this paper, a novel approach that is based on two-stepped majority voting is proposed for efficient EEG-based emotion classification. Emotion recognition is important for human–machine interactions. Facial features- and body gestures-based approaches have been generally proposed for emotion recognition. Recently, EEG-based approaches become more popular in emotion recognition. In the proposed approach, the raw EEG signals are initially low-pass filtered for noise removal and band-pass filters are used for rhythms extraction. For each rhythm, the best performed EEG channels are determined based on wavelet-based entropy features and fractal dimension-based features. The k-nearest neighbor (KNN) classifier is used in classification. The best five EEG channels are used in majority voting for getting the final predictions for each EEG rhythm. In the second majority voting step, the predictions from all rhythms are used to get a final prediction. The DEAP dataset is used in experiments and classification accuracy, sensitivity and specificity are used for performance evaluation metrics. The experiments are carried out to classify the emotions into two binary classes such as high valence (HV) vs low valence (LV) and high arousal (HA) vs low arousal (LA). The experiments show that 86.3% HV vs LV discrimination accuracy and 85.0% HA vs LA discrimination accuracy is obtained. The obtained results are also compared with some of the existing methods. The comparisons show that the proposed method has potential in the use of EEG-based emotion classification.
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Affiliation(s)
- Aras M Ismael
- Sulaimani Polytechnic University, Sulaymaniyah, Iraq.
| | - Ömer F Alçin
- Electrical Engineering Department, Engineering and Natural Sciences Faculty, Malatya Turgut Ozal University, 44210, Malatya, Turkey
| | | | - Abdulkadir Şengür
- Electrical-Electronics Engineering Department, Technology Faculty, Firat University, Elazig, Turkey
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11
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Performance of machine learning methods applied to structural MRI and ADAS cognitive scores in diagnosing Alzheimer’s disease. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2018.08.009] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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12
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Embedding Dimension Selection for Adaptive Singular Spectrum Analysis of EEG Signal. SENSORS 2018; 18:s18030697. [PMID: 29495415 PMCID: PMC5877194 DOI: 10.3390/s18030697] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2018] [Revised: 02/14/2018] [Accepted: 02/22/2018] [Indexed: 11/16/2022]
Abstract
The recorded electroencephalography (EEG) signal is often contaminated with different kinds of artifacts and noise. Singular spectrum analysis (SSA) is a powerful tool for extracting the brain rhythm from a noisy EEG signal. By analyzing the frequency characteristics of the reconstructed component (RC) and the change rate in the trace of the Toeplitz matrix, it is demonstrated that the embedding dimension is related to the frequency bandwidth of each reconstructed component, in consistence with the component mixing in the singular value decomposition step. A method for selecting the embedding dimension is thereby proposed and verified by simulated EEG signal based on the Markov Process Amplitude (MPA) EEG Model. Real EEG signal is also collected from the experimental subjects under both eyes-open and eyes-closed conditions. The experimental results show that based on the embedding dimension selection method, the alpha rhythm can be extracted from the real EEG signal by the adaptive SSA, which can be effectively utilized to distinguish between the eyes-open and eyes-closed states.
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Hu H, Guo S, Liu R, Wang P. An adaptive singular spectrum analysis method for extracting brain rhythms of electroencephalography. PeerJ 2017; 5:e3474. [PMID: 28674650 PMCID: PMC5493032 DOI: 10.7717/peerj.3474] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2017] [Accepted: 05/29/2017] [Indexed: 11/20/2022] Open
Abstract
Artifacts removal and rhythms extraction from electroencephalography (EEG) signals are important for portable and wearable EEG recording devices. Incorporating a novel grouping rule, we proposed an adaptive singular spectrum analysis (SSA) method for artifacts removal and rhythms extraction. Based on the EEG signal amplitude, the grouping rule determines adaptively the first one or two SSA reconstructed components as artifacts and removes them. The remaining reconstructed components are then grouped based on their peak frequencies in the Fourier transform to extract the desired rhythms. The grouping rule thus enables SSA to be adaptive to EEG signals containing different levels of artifacts and rhythms. The simulated EEG data based on the Markov Process Amplitude (MPA) EEG model and the experimental EEG data in the eyes-open and eyes-closed states were used to verify the adaptive SSA method. Results showed a better performance in artifacts removal and rhythms extraction, compared with the wavelet decomposition (WDec) and another two recently reported SSA methods. Features of the extracted alpha rhythms using adaptive SSA were calculated to distinguish between the eyes-open and eyes-closed states. Results showed a higher accuracy (95.8%) than those of the WDec method (79.2%) and the infinite impulse response (IIR) filtering method (83.3%).
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Affiliation(s)
- Hai Hu
- State Key Laboratory of Precision Measurement Technology and Instruments, Tsinghua University, Beijing, China
| | - Shengxin Guo
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Ran Liu
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Peng Wang
- State Key Laboratory of Precision Measurement Technology and Instruments, Tsinghua University, Beijing, China
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Yu Y, Zhao Y, Si Y, Ren Q, Ren W, Jing C, Zhang H. Estimation of the cool executive function using frontal electroencephalogram signals in first-episode schizophrenia patients. Biomed Eng Online 2016; 15:131. [PMID: 27884145 PMCID: PMC5123362 DOI: 10.1186/s12938-016-0282-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2016] [Accepted: 11/16/2016] [Indexed: 11/10/2022] Open
Abstract
Background In schizophrenia, executive dysfunction is the most critical cognitive impairment, and is associated with abnormal neural activities, especially in the frontal lobes. Complexity estimation using electroencephalogram (EEG) recording based on nonlinear dynamics and task performance tests have been widely used to estimate executive dysfunction in schizophrenia. Methods The present study estimated the cool executive function based on fractal dimension (FD) values of EEG data recorded from first-episode schizophrenia patients and healthy controls during the performance of three cool executive function tasks, namely, the Trail Making Test-A (TMT-A), Trail Making Test-B (TMT-B), and Tower of Hanoi tasks. Results The results show that the complexity of the frontal EEG signals that were measured using FD was different in first-episode schizophrenia patients during the manipulation of executive function. However, no differences between patients and controls were found in the FD values of the EEG data that was recorded during the performance of the Tower of Hanoi task. Conclusions These results suggest that cool executive function exhibits little impairment in first-episode schizophrenia patients.
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Affiliation(s)
- Yi Yu
- Department of Biomedical Engineering, Xinxiang Medical University, Xinxiang, Henan, People's Republic of China
| | - Yun Zhao
- Department of Biomedical Engineering, Xinxiang Medical University, Xinxiang, Henan, People's Republic of China
| | - Yajing Si
- Department of Psychology, Xinxiang Medical University, Xinxiang, Henan, People's Republic of China
| | - Qiongqiong Ren
- Department of Biomedical Engineering, Xinxiang Medical University, Xinxiang, Henan, People's Republic of China
| | - Wu Ren
- Department of Biomedical Engineering, Xinxiang Medical University, Xinxiang, Henan, People's Republic of China
| | - Changqin Jing
- Department of Life Sciences and Technology, Xinxiang Medical University, Xinxiang, Henan, People's Republic of China
| | - Hongxing Zhang
- Department of Psychology, Xinxiang Medical University, Xinxiang, Henan, People's Republic of China.
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Akar SA, Kara S, Agambayev S, Bilgic V. Nonlinear analysis of EEG in major depression with fractal dimensions. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2015:7410-3. [PMID: 26738004 DOI: 10.1109/embc.2015.7320104] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Major depressive disorder (MDD) is a psychiatric mood disorder characterized by cognitive and functional impairments in attention, concentration, learning and memory. In order to investigate and understand its underlying neural activities and pathophysiology, EEG methodologies can be used. In this study, we estimated the nonlinearity features of EEG in MDD patients to assess the dynamical properties underlying the frontal and parietal brain activity. EEG data were obtained from 16 patients and 15 matched healthy controls. A wavelet-chaos methodology was used for data analysis. First, EEGs of subjects were decomposed into 5 EEG sub-bands by discrete wavelet transform. Then, both the Katz's and Higuchi's fractal dimensions (KFD and HFD) were calculated as complexity measures for full-band and sub-bands EEGs. Last, two-way analyses of variances were used to test EEG complexity differences on each fractality measures. As a result, a significantly increased complexity was found in both parietal and frontal regions of MDD patients. This significantly increased complexity was observed not only in full-band activity but also in beta and gamma sub-bands of EEG. The findings of the present study indicate the possibility of using the wavelet-chaos methodology to discriminate the EEGs of MDD patients from healthy controls.
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A Hybrid Multi-Step Rolling Forecasting Model Based on SSA and Simulated Annealing—Adaptive Particle Swarm Optimization for Wind Speed. SUSTAINABILITY 2016. [DOI: 10.3390/su8080754] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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17
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Juneja A, Rana B, Agrawal R. A combination of singular value decomposition and multivariate feature selection method for diagnosis of schizophrenia using fMRI. Biomed Signal Process Control 2016. [DOI: 10.1016/j.bspc.2016.02.009] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Akdemir Akar S, Kara S, Agambayev S, Bilgiç V. Nonlinear analysis of EEGs of patients with major depression during different emotional states. Comput Biol Med 2015; 67:49-60. [PMID: 26496702 DOI: 10.1016/j.compbiomed.2015.09.019] [Citation(s) in RCA: 67] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2015] [Revised: 09/22/2015] [Accepted: 09/24/2015] [Indexed: 12/23/2022]
Affiliation(s)
- Saime Akdemir Akar
- Institute of Biomedical Engineering, Fatih University, Istanbul 34500, Turkey.
| | - Sadık Kara
- Institute of Biomedical Engineering, Fatih University, Istanbul 34500, Turkey
| | - Sümeyra Agambayev
- Institute of Biomedical Engineering, Fatih University, Istanbul 34500, Turkey
| | - Vedat Bilgiç
- Department of Psychiatry, School of Medicine, Fatih University, Istanbul 34500, Turkey
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