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Lersch F, Zingg TJG, Knapp J, Stüber F, Hight D, Kaiser HA. [Processed EEG for personalized dosing of anesthetics during general anesthesia]. DIE ANAESTHESIOLOGIE 2023; 72:662-676. [PMID: 37552241 PMCID: PMC10457248 DOI: 10.1007/s00101-023-01313-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 05/26/2023] [Indexed: 08/09/2023]
Abstract
Electroencephalogram (EEG)-guided anesthesia is indispensable in modern operating rooms and has become established as the standard form of monitoring. Many anesthesiologists rely on processed EEG indices in the hope of averting anesthesia-related complications, such as intraoperative awareness, postoperative delirium and other cognitive complications in their patients. This educational review aims to provide information on the five most prevalent monitors used to guide depth of sedation during general anesthesia. This article elucidates the principles underpinning the application of these monitors where known, which are generally based on power in various EEG frequency bands and on the burst suppression pattern. Convinced that EEG-guided anesthesia has the potential of benefitting many surgical patients, it is felt that many basic principles and shortcomings of processed EEG indices need to be better understood in the clinical practice. After discussing the different monitors and clinically relevant data from the literature, the article gives a short practical guidance on how to critically interpret processed EEG information and troubleshooting of confounded indices in the context of clinical situations.
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Affiliation(s)
- F Lersch
- Universitätsklinik für Anästhesiologie und Schmerzmedizin, Inselspital, Universitätsspital Bern, Universität Bern, Freiburgstrasse, 3010, Bern, Schweiz
| | - T J G Zingg
- Universitätsklinik für Anästhesiologie und Schmerzmedizin, Inselspital, Universitätsspital Bern, Universität Bern, Freiburgstrasse, 3010, Bern, Schweiz
| | - J Knapp
- Universitätsklinik für Anästhesiologie und Schmerzmedizin, Inselspital, Universitätsspital Bern, Universität Bern, Freiburgstrasse, 3010, Bern, Schweiz
| | - F Stüber
- Universitätsklinik für Anästhesiologie und Schmerzmedizin, Inselspital, Universitätsspital Bern, Universität Bern, Freiburgstrasse, 3010, Bern, Schweiz
| | - D Hight
- Universitätsklinik für Anästhesiologie und Schmerzmedizin, Inselspital, Universitätsspital Bern, Universität Bern, Freiburgstrasse, 3010, Bern, Schweiz
| | - H A Kaiser
- Universitätsklinik für Anästhesiologie und Schmerzmedizin, Inselspital, Universitätsspital Bern, Universität Bern, Freiburgstrasse, 3010, Bern, Schweiz.
- Zentrum für Anästhesiologie und Intensivmedizin, Hirslanden Klinik Aarau, Hirslanden AG, Schänisweg, 5001, Aarau, Schweiz.
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Bagherzadeh S, Maghooli K, Shalbaf A, Maghsoudi A. A Hybrid EEG-based Emotion Recognition Approach Using Wavelet Convolutional Neural Networks and Support Vector Machine. Basic Clin Neurosci 2023; 14:87-102. [PMID: 37346875 PMCID: PMC10279985 DOI: 10.32598/bcn.2021.3133.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 03/06/2021] [Accepted: 04/14/2021] [Indexed: 11/02/2023] Open
Abstract
Introduction Nowadays, deep learning and convolutional neural networks (CNNs) have become widespread tools in many biomedical engineering studies. CNN is an end-to-end tool, which makes the processing procedure integrated, but in some situations, this processing tool requires to be fused with machine learning methods to be more accurate. Methods In this paper, a hybrid approach based on deep features extracted from wavelet CNNs (WCNNs) weighted layers and multiclass support vector machine (MSVM) was proposed to improve the recognition of emotional states from electroencephalogram (EEG) signals. First, EEG signals were preprocessed and converted to Time-Frequency (T-F) color representation or scalogram using the continuous wavelet transform (CWT) method. Then, scalograms were fed into four popular pre-trained CNNs, AlexNet, ResNet-18, VGG-19, and Inception-v3 to fine-tune them. Then, the best feature layer from each one was used as input to the MSVM method to classify four quarters of the valence-arousal model. Finally, the subject-independent leave-one-subject-out criterion was used to evaluate the proposed method on DEAP and MAHNOB-HCI databases. Results Results showed that extracting deep features from the earlier convolutional layer of ResNet-18 (Res2a) and classifying using the MSVM increased the average accuracy, precision, and recall by about 20% and 12% for MAHNOB-HCI and DEAP databases, respectively. Also, combining scalograms from four regions of pre-frontal, frontal, parietal, and parietal-occipital and two regions of frontal and parietal achieved the higher average accuracy of 77.47% and 87.45% for MAHNOB-HCI and DEAP databases, respectively. Conclusion Combining CNN and MSVM increased the recognition of emotion from EEG signals and the results were comparable to state-of-the art studies.
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Affiliation(s)
- Sara Bagherzadeh
- Department of Biomedical Engineering, Sciences and Research Branch, Islamic Azad University, Tehran, Iran
| | - Keivan Maghooli
- Department of Biomedical Engineering, Sciences and Research Branch, Islamic Azad University, Tehran, Iran
| | - Ahmad Shalbaf
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Arash Maghsoudi
- Department of Biomedical Engineering, Sciences and Research Branch, Islamic Azad University, Tehran, Iran
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Li H, Ji H, Yu J, Li J, Jin L, Liu L, Bai Z, Ye C. A sequential learning model with GNN for EEG-EMG-based stroke rehabilitation BCI. Front Neurosci 2023; 17:1125230. [PMID: 37139522 PMCID: PMC10150013 DOI: 10.3389/fnins.2023.1125230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 03/27/2023] [Indexed: 05/05/2023] Open
Abstract
Introduction Brain-computer interfaces (BCIs) have the potential in providing neurofeedback for stroke patients to improve motor rehabilitation. However, current BCIs often only detect general motor intentions and lack the precise information needed for complex movement execution, mainly due to insufficient movement execution features in EEG signals. Methods This paper presents a sequential learning model incorporating a Graph Isomorphic Network (GIN) that processes a sequence of graph-structured data derived from EEG and EMG signals. Movement data are divided into sub-actions and predicted separately by the model, generating a sequential motor encoding that reflects the sequential features of the movements. Through time-based ensemble learning, the proposed method achieves more accurate prediction results and execution quality scores for each movement. Results A classification accuracy of 88.89% is achieved on an EEG-EMG synchronized dataset for push and pull movements, significantly outperforming the benchmark method's performance of 73.23%. Discussion This approach can be used to develop a hybrid EEG-EMG brain-computer interface to provide patients with more accurate neural feedback to aid their recovery.
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Affiliation(s)
- Haoyang Li
- Translational Research Center, Shanghai Yangzhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Electronic and Information Engineering, Tongji University, Shanghai, China
| | - Hongfei Ji
- Translational Research Center, Shanghai Yangzhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Electronic and Information Engineering, Tongji University, Shanghai, China
- Hongfei Ji
| | - Jian Yu
- Translational Research Center, Shanghai Yangzhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Electronic and Information Engineering, Tongji University, Shanghai, China
- Jian Yu
| | - Jie Li
- Translational Research Center, Shanghai Yangzhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Electronic and Information Engineering, Tongji University, Shanghai, China
- *Correspondence: Jie Li
| | - Lingjing Jin
- Department of Neurology and Neurological Rehabilitation, Shanghai Disabled Person's Federation Key Laboratory of Intelligent Rehabilitation Assistive Devices and Technologies, Yangzhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Medicine, Tongji University, Shanghai, China
- Neurotoxin Research Center of Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Neurological Department of Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Lingyu Liu
- Department of Neurology and Neurological Rehabilitation, Shanghai Disabled Person's Federation Key Laboratory of Intelligent Rehabilitation Assistive Devices and Technologies, Yangzhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Medicine, Tongji University, Shanghai, China
| | - Zhongfei Bai
- Department of Neurology and Neurological Rehabilitation, Shanghai Disabled Person's Federation Key Laboratory of Intelligent Rehabilitation Assistive Devices and Technologies, Yangzhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Medicine, Tongji University, Shanghai, China
| | - Chen Ye
- Translational Research Center, Shanghai Yangzhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Electronic and Information Engineering, Tongji University, Shanghai, China
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Interictal Gamma Event Connectivity Differentiates the Seizure Network and Outcome in Patients after Temporal Lobe Epilepsy Surgery. eNeuro 2022; 9:ENEURO.0141-22.2022. [PMID: 36418173 PMCID: PMC9770020 DOI: 10.1523/eneuro.0141-22.2022] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 11/03/2022] [Accepted: 11/07/2022] [Indexed: 11/25/2022] Open
Abstract
Studies of interictal EEG functional connectivity in the epileptic brain seek to identify abnormal interactions between brain regions involved in generating seizures, which clinically often is defined by the seizure onset zone (SOZ). However, there is evidence for abnormal connectivity outside the SOZ (NSOZ), and removal of the SOZ does not always result in seizure control, suggesting, in some cases, that the extent of abnormal connectivity indicates a larger seizure network than the SOZ. To better understand the potential differences in interictal functional connectivity in relation to the seizure network and outcome, we computed event connectivity in the theta (4-8 Hz, ThEC), low-gamma (30-55 Hz, LGEC), and high-gamma (65-95 Hz, HGEC) bands from interictal depth EEG recorded in surgical patients with medication-resistant seizures suspected to begin in the temporal lobe. Analysis finds stronger LGEC and HGEC in SOZ than NSOZ of seizure-free (SF) patients (p = 1.10e-9, 0.0217), but no difference in not seizure-free (NSF) patients. There were stronger LGEC and HGEC between mesial and lateral temporal SOZ of SF than NSF patients (p = 0.00114, 0.00205), and stronger LGEC and ThEC in NSOZ of NSF than SF patients (p = 0.0089, 0.0111). These results show that event connectivity is sensitive to differences in the interactions between regions in SOZ and NSOZ and SF and NSF patients. Patients with differential strengths in event connectivity could represent a well-localized seizure network, whereas an absence of differences could indicate a larger seizure network than the one localized by the SOZ and higher likelihood for seizure recurrence.
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Bagherzadeh S, Maghooli K, Shalbaf A, Maghsoudi A. Emotion recognition using effective connectivity and pre-trained convolutional neural networks in EEG signals. Cogn Neurodyn 2022; 16:1087-1106. [PMID: 36237402 PMCID: PMC9508317 DOI: 10.1007/s11571-021-09756-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 09/29/2021] [Accepted: 11/14/2021] [Indexed: 12/26/2022] Open
Abstract
Convolutional Neural Networks (CNN) have recently made considerable advances in the field of biomedical signal processing. These methodologies can assist in emotion recognition for affective brain computer interface. In this paper, a novel emotion recognition system based on the effective connectivity and the fine-tuned CNNs from multichannel Electroencephalogram (EEG) signal is presented. After preprocessing EEG signals, the relationships among 32 channels of EEG in the form of effective brain connectivity analysis which represents information flow between regions are computed by direct Directed Transfer Function (dDTF) method which yields a 32*32 image. Then, these constructed images from EEG signals for each subject were fed as input to four versions of pre-trained CNN models, AlexNet, ResNet-50, Inception-v3 and VGG-19 and the parameters of these models are fine-tuned, independently. The proposed deep learning architectures automatically learn patterns in the constructed image of the EEG signals in frequency bands. The efficiency of the proposed approach is evaluated on MAHNOB-HCI and DEAP databases. The experiments for classifying five emotional states show that the ResNet-50 applied on dDTF images in alpha band achieves best results due to specific architecture which captures the brain connectivity, efficiently. The accuracy and F1-score values for MAHNOB-HCI were obtained 99.41, 99.42 and for DEAP databases, 98.17, and 98.23. Newly proposed model is capable of effectively analyzing the brain function using information flow from multichannel EEG signals using effective connectivity measure of dDTF and ResNet-50.
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Affiliation(s)
- Sara Bagherzadeh
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Keivan Maghooli
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Ahmad Shalbaf
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Arash Maghsoudi
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
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Bagherzadeh S, Maghooli K, Shalbaf A, Maghsoudi A. Recognition of emotional states using frequency effective connectivity maps through transfer learning approach from electroencephalogram signals. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103544] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Three-Level Distributed Real-Time Monitoring of Construction near Underground Infrastructure Using a Combined Intelligent Method. SENSORS 2022; 22:s22093260. [PMID: 35590948 PMCID: PMC9102674 DOI: 10.3390/s22093260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 04/16/2022] [Accepted: 04/21/2022] [Indexed: 02/01/2023]
Abstract
With the rapid development of underground infrastructure and the uncertainty of its location, the possibility of damage due to nearby construction has increased. Thus, for the early warning of dangerous construction behaviors around underground facilities, this paper proposes a novel real-time distributed monitoring method with three levels, comprised of the terminal node, relay node, and server. Corresponding to these three monitoring levels, a vibration-based intelligent solution for recognizing the construction source is presented and compared with the traditional method. First, the blind source separation method was used to separate collected signals into a limited number of monitoring object sources; this helped to minimize the number of required classification categories and reduce the recognition uncertainty caused by signal mixing. Then, the mutual information (MI) method was used to select suitable vibration features, which were used as the input matrix for the resulting intelligent recognition. Finally, the construction behaviors were identified at the server based on returned features. Guided by this method, a sample dataset including pile-driving, train-operation, and environment-vibration signals was constructed and combined with a multi-layer perceptron (MLP) and a long short-term memory (LSTM) network. The effects of blind source separation and the MI method are discussed in depth in this paper.
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Tan G, Wang J, Liu J, Sheng Y, Xie Q, Liu H. A framework for quantifying the effects of transcranial magnetic stimulation on motor recovery from hemiparesis: Corticomuscular Network. J Neural Eng 2022; 19. [PMID: 35366651 DOI: 10.1088/1741-2552/ac636b] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 04/01/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Transcranial magnetic stimulation (TMS) is an experimental therapy for promoting motor recovery from hemiparesis. At present, hemiparesis patients' responses to TMS are variable. To maximize its therapeutic potential, we need an approach that relates the electrophysiology of motor recovery and TMS. To this end, we propose Corticomuscular Network (CMN) representing the holistic motor system, including the cortico-cortical pathway, corticospinal tract, and muscle co-activation. METHODS CMN is made up of coherence between pairs of electrode signals and spatial locations of the electrodes. We associated coherence and graph features of CMN with Fugl-Meyer Assessment (FMA) for the upper extremity. Besides, we compared CMN between 8 patients with hemiparesis and 6 healthy controls and contrasted CMN of patients before and after a 1Hz TMS. MAIN RESULTS Corticomuscular coherence (CMC) correlated positively with FMA. The regression model between FMA and CMC between 5 pairs of channels had 0.99 adjusted R^2 and a p-value less than 0.01. Compared to healthy controls, CMN of patients tended to be a small-world network and was more interconnected with higher CMC. CMC between cortex and triceps brachii long head was higher in patients. 15-minute 1Hz TMS protocol induced coherence changes beyond the stimulation side and had a limited impact on CMN parameters that are related to motor recovery. SIGNIFICANCE CMN is a potential clinical approach to quantify rehabilitating progress. It also sheds light on the desirable electrophysiological effects of TMS based on which rehabilitating strategies can be optimized.
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Affiliation(s)
- Gansheng Tan
- Washington University in St Louis, 520 S Euclid Ave, St. Louis, MO 63110, St Louis, Missouri, 63130-4899, UNITED STATES
| | - Jixian Wang
- Shanghai Jiao Tong University Medical School Affiliated Ruijin Hospital, 800 Dongchuan Rd, Shanghai, 200025, CHINA
| | - Jinbiao Liu
- Shanghai Jiao Tong University, 800 Dongchuan Rd, Shanghai, 200240, CHINA
| | - Yixuan Sheng
- Shanghai Jiao Tong University, 800 Dongchuan Rd, Shanghai, 200240, CHINA
| | - Qing Xie
- Ruijin Hospital, 800 Dongchuan Rd, Shanghai, 200025, CHINA
| | - Honghai Liu
- Harbin Institute of Technology Shenzhen, Pingshan 1 Rd, Nanshan, Shenzhen, Guangdong, 518055, CHINA
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Maghsoudi A, Shalbaf A. Hand Motor Imagery Classification Using Effective Connectivity and Hierarchical Machine Learning in EEG Signals. J Biomed Phys Eng 2022; 12:161-170. [PMID: 35433527 PMCID: PMC8995751 DOI: 10.31661/jbpe.v0i0.1264] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Accepted: 11/14/2019] [Indexed: 11/16/2022]
Abstract
Background Motor Imagery (MI) Brain Computer Interface (BCI) directly links central nervous system to a computer or a device. Most MI-BCI structures rely on features of a single channel of Electroencephalogram (EEG). However, to provide more valuable features, the relationships among EEG channels in the form of effective brain connectivity analysis must be considered. Objective This study aims to identify a set of robust and discriminative effective connectivity features from EEG signals and to develop a hierarchical machine learning structure for discrimination of left and right hand MI task effectively. Material and Methods In this analytical study, we estimated effective connectivity using Granger Causality (GC) methods namely, Generalized Partial Directed Coherence (GPDC), Directed Transfer Function (DTF) and direct Directed Transfer Function (dDTF). These measures determine the transient causal relation between different brain areas. Then a feature subset selection method based on Kruskal-Wallis test was performed to choose most significant directed causal connection between channels. Moreover, the minimal-redundancy-maximal-relevance feature selection method is applied to discard non-significance features. Finally, support vector machine method is used for classification. Results The maximum value of the classification accuracies using GC methods over different frequency bands in 29 subjects in 60 trial is approximately 84% in Mu (8-12 Hz) - Beta1 (12 - 15 Hz) frequency band using GPDC method. Conclusion This new hierarchical automated BCI system could be applied for discrimination of left and right hand MI tasks from EEG signal, effectively.
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Affiliation(s)
- Arash Maghsoudi
- PhD, Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Ahmad Shalbaf
- PhD, Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Sanjari N, Shalbaf A, Shalbaf R, Sleigh J. Assessment of Anesthesia Depth Using Effective Brain Connectivity Based on Transfer Entropy on EEG Signal. Basic Clin Neurosci 2021; 12:269-280. [PMID: 34925723 PMCID: PMC8672664 DOI: 10.32598/bcn.12.2.2034.2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 09/10/2020] [Accepted: 12/12/2020] [Indexed: 11/20/2022] Open
Abstract
Introduction: Ensuring an adequate Depth of Anesthesia (DOA) during surgery is essential for anesthesiologists. Since the effect of anesthetic drugs is on the central nervous system, brain signals such as Electroencephalogram (EEG) can be used for DOA estimation. Anesthesia can interfere among brain regions, so the relationship among different areas can be a key factor in the anesthetic process. Methods: In this paper, by combining the Wiener causality concept and the conditional mutual information, a nonlinear effective connectivity measure called Transfer Entropy (TE) is presented to describe the relationship between EEG signals at frontal and temporal regions from eight volunteers in three anesthetic states (awake, unconscious and recovery). This index is also compared with Granger causality and partial directional coherence methods as common effective connectivity indexes. Results: Based on a statistical analysis of the probability predictive value and Kruskal-Wallis statistical method, TE can effectively fallow the effect-site concentration of propofol and distinguish the anesthetic states well, and perform better than the other effective connectivity indexes. This index is also better than Bispectral Index (BIS) as commercial DOA monitor because of the faster response and higher correlation with the drug concentration effect-site, less irregularity in the unconscious state and better ability to distinguish three states of anesthestesia. Conclusion: TE index is a confident indicator for designing a new monitoring system of the two EEG channels for DOA estimation.
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Affiliation(s)
- Neda Sanjari
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ahmad Shalbaf
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Reza Shalbaf
- Institute for Cognitive Science Studies, Tehran, Iran
| | - Jamie Sleigh
- Department of Anesthesia, Waikato Hospital, Hamilton, New Zealand
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Liu Y, Xu X, Zhou Y, Xu J, Dong X, Li X, Yin S, Wen D. Coupling feature extraction method of resting state EEG Signals from amnestic mild cognitive impairment with type 2 diabetes mellitus based on weight permutation conditional mutual information. Cogn Neurodyn 2021; 15:987-997. [PMID: 34790266 PMCID: PMC8572246 DOI: 10.1007/s11571-021-09682-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 03/28/2021] [Accepted: 04/19/2021] [Indexed: 01/06/2023] Open
Abstract
This study aimed to find a good coupling feature extraction method to effectively analyze resting state EEG signals (rsEEG) of amnestic mild cognitive impairment(aMCI) with type 2 diabetes mellitus(T2DM) and normal control (NC) with T2DM. A method of EEG signal coupling feature extraction based on weight permutation conditional mutual information (WPCMI) was proposed in this research. With the WPCMI method, coupling feature strength of two time series in Alpha1, Alpha2, Beta1, Beta2 and Gamma bands for aMCI with T2DM and NC with T2DM could be extracted respectively. Then selected three frequency bands coupling feature matrix with the help of multi-spectral image transformation method to map it as spectral image characteristics. And finally classified these characteristics through the convolution neural network method(CNN). For aMCI with T2DM and NC with T2DM, the highest classification accuracy of 96%, 95%, 95% could be achieved respectively in the combination of three frequency bands (Alpha1, Alpha2, Gamma), (Beta1, Beta2 and Gamma) and (Alpha2, Beta1, Beta2). This WPCMI method highlighted the coupling dynamic characteristics of EEG signals, and its classification performance was better than all previous methods in aMCI with T2DM diagnosis field. WPCMI method could be used as an effective biomarker to distinguish EEG signals of aMCI with T2DM and NC with T2DM. The coupling feature extraction method used in this paper provided a new perspective for the EEG analysis of aMCI with T2DM.
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Affiliation(s)
- Yijun Liu
- School of Science, Yanshan University, Qinhuangdao, China
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
| | - Xiaodong Xu
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
| | - Yanhong Zhou
- School of Mathematics and Information Science and Technology, Hebei Normal University of Science and Technology, Qinhuangdao, China
| | - Jian Xu
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
| | - Xianling Dong
- Department of Biomedical Engineering, Chengde Medical University, Chengde, China
| | - Xiaoli Li
- The National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Shimin Yin
- Department of Neurology, The Rocket Force Hospital of Chinese People’s Liberation Army, Beijing, China
| | - Dong Wen
- Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, China
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Maghsoudi A, Shalbaf A. Mental Arithmetic Task Recognition Using Effective Connectivity and Hierarchical Feature Selection From EEG Signals. Basic Clin Neurosci 2021; 12:817-826. [PMID: 35693148 PMCID: PMC9168814 DOI: 10.32598/bcn.2021.2034.1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 02/19/2020] [Accepted: 04/20/2020] [Indexed: 11/20/2022] Open
Abstract
Introduction Mental arithmetic analysis based on Electroencephalogram (EEG) signals can help understand disorders, such as attention-deficit hyperactivity, dyscalculia, or autism spectrum disorder 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 between EEG channels in the form of effective brain connectivity analysis can contain valuable information. This study aims to find distinctive, effective brain connectivity features and create a hierarchical feature selection for effectively classifying mental arithmetic and baseline tasks. Methods We estimated effective connectivity using Directed Transfer Function (DTF), direct DTF (dDTF) and Generalized Partial Directed Coherence (GPDC) methods. These measures determine the causal relationship between different brain areas. A hierarchical feature subset selection method selects the most significant effective connectivity features. Initially, Kruskal- Wallis test was performed. Consequently, five feature selection algorithms, namely, Support Vector Machine (SVM) method based on Recursive Feature Elimination, Fisher score, mutual information, minimum Redundancy Maximum Relevance (RMR), and concave minimization and SVM are used to select the best discriminative features. Finally, the SVM method was used for classification. Results The obtained results indicated 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 helpful in the discrimination of mental arithmetic and baseline tasks from EEG signals effectively. Highlights Propose effective connectivity to describe EEG signals during mental arithmetic task.Most significant connectivity features from generalized partial directed coherence method.Hierarchical feature selection from Kruskal-Wallis test and concave minimization method. Plain Language Summary Brain analysis methods by Electroencephalogram (EEG) signals provide a suitable method to monitor human brain activity due to having high temporal resolution, being noninvasive, inexpensive, and portable method. Analysis of mental arithmetic based EEG signal is helpful for psychological disorders like dyscalculia where they have learning understanding arithmetic, attention deficit hyperactivity, and autism spectrum disorders with attention deficit problem. This study finds distinctive effective brain connectivity features and creates a hierarchical feature selection for classification of mental arithmetic and baseline tasks effectively. Best EEG classification performance in 29 participants and 60 trials is obtained using Generalized Partial Directed Coherence (GPDC) methods and feature selection via concave minimization method in Beta2 (15-22Hz) frequency band with 89% accuracy. Thus, this new hierarchical automated system is useful for discrimination of mental arithmetic and baseline tasks from EEG signal effectively.
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Affiliation(s)
- Arash Maghsoudi
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Ahmad Shalbaf
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Altered Functional Connectivity Strength at Rest in Medication-Free Obsessive-Compulsive Disorder. Neural Plast 2021; 2021:3741104. [PMID: 34539777 PMCID: PMC8443365 DOI: 10.1155/2021/3741104] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2021] [Revised: 07/25/2021] [Accepted: 08/17/2021] [Indexed: 11/25/2022] Open
Abstract
Background Previous studies explored the whole-brain functional connectome using the degree approach in patients with obsessive-compulsive disorder (OCD). However, whether the altered degree values can be used to discriminate OCD from healthy controls (HCs) remains unclear. Methods A total of 40 medication-free patients with OCD and 38 HCs underwent a resting-state functional magnetic resonance imaging (rs-fMRI) scan. Data were analyzed with the degree approach and a support vector machine (SVM) classifier. Results Patients with OCD showed increased degree values in the left thalamus and left cerebellum Crus I and decreased degree values in the left dorsolateral prefrontal cortex, right precuneus, and left postcentral gyrus. SVM classification analysis indicated that the increased degree value in the left thalamus is a marker of OCD, with an acceptable accuracy of 88.46%, sensitivity of 87.50%, and specificity of 89.47%. Conclusion Altered degree values within and outside the cortical-striatal-thalamic-cortical (CSTC) circuit may cocontribute to the pathophysiology of OCD. Increased degree values of the left thalamus can be used as a future marker for OCD understanding-classification.
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Sarasso S, Casali AG, Casarotto S, Rosanova M, Sinigaglia C, Massimini M. Consciousness and complexity: a consilience of evidence. Neurosci Conscious 2021; 2021:niab023. [PMID: 38496724 PMCID: PMC10941977 DOI: 10.1093/nc/niab023] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 06/19/2021] [Accepted: 07/29/2021] [Indexed: 03/19/2024] Open
Abstract
Over the last years, a surge of empirical studies converged on complexity-related measures as reliable markers of consciousness across many different conditions, such as sleep, anesthesia, hallucinatory states, coma, and related disorders. Most of these measures were independently proposed by researchers endorsing disparate frameworks and employing different methods and techniques. Since this body of evidence has not been systematically reviewed and coherently organized so far, this positive trend has remained somewhat below the radar. The aim of this paper is to make this consilience of evidence in the science of consciousness explicit. We start with a systematic assessment of the growing literature on complexity-related measures and identify their common denominator, tracing it back to core theoretical principles and predictions put forward more than 20 years ago. In doing this, we highlight a consistent trajectory spanning two decades of consciousness research and provide a provisional taxonomy of the present literature. Finally, we consider all of the above as a positive ground to approach new questions and devise future experiments that may help consolidate and further develop a promising field where empirical research on consciousness appears to have, so far, naturally converged.
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Affiliation(s)
- Simone Sarasso
- Department of Biomedical and Clinical Sciences ‘L. Sacco’, University of Milan, Milan 20157, Italy
| | - Adenauer Girardi Casali
- Instituto de Ciência e Tecnologia, Universidade Federal de São Paulo, Sao Jose dos Campos, 12247-014, Brazil
| | - Silvia Casarotto
- Department of Biomedical and Clinical Sciences ‘L. Sacco’, University of Milan, Milan 20157, Italy
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan 20148, Italy
| | - Mario Rosanova
- Department of Biomedical and Clinical Sciences ‘L. Sacco’, University of Milan, Milan 20157, Italy
| | | | - Marcello Massimini
- Department of Biomedical and Clinical Sciences ‘L. Sacco’, University of Milan, Milan 20157, Italy
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan 20148, Italy
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Prediction of drug response in major depressive disorder using ensemble of transfer learning with convolutional neural network based on EEG. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.06.006] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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Saeedi A, Saeedi M, Maghsoudi A, Shalbaf A. Major depressive disorder diagnosis based on effective connectivity in EEG signals: a convolutional neural network and long short-term memory approach. Cogn Neurodyn 2021; 15:239-252. [PMID: 33854642 PMCID: PMC7969675 DOI: 10.1007/s11571-020-09619-0] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 07/13/2020] [Accepted: 07/16/2020] [Indexed: 02/07/2023] Open
Abstract
Deep learning techniques have recently made considerable advances in the field of artificial intelligence. These methodologies can assist psychologists in early diagnosis of mental disorders and preventing severe trauma. Major Depression Disorder (MDD) is a common and serious medical condition whose exact manifestations are not fully understood. So, early discovery of MDD patients helps to cure or limit the adverse effects. Electroencephalogram (EEG) is prominently used to study brain diseases such as MDD due to having high temporal resolution information, and being a noninvasive, inexpensive and portable method. This paper has proposed an EEG-based deep learning framework that automatically discriminates MDD patients from healthy controls. First, the relationships among EEG channels in the form of effective brain connectivity analysis are extracted by Generalized Partial Directed Coherence (GPDC) and Direct directed transfer function (dDTF) methods. A novel combination of sixteen connectivity methods (GPDC and dDTF in eight frequency bands) was used to construct an image for each individual. Finally, the constructed images of EEG signals are applied to the five different deep learning architectures. The first and second algorithms were based on one and two-dimensional convolutional neural network (1DCNN-2DCNN). The third method is based on long short-term memory (LSTM) model, while the fourth and fifth algorithms utilized a combination of CNN with LSTM model namely, 1DCNN-LSTM and 2DCNN-LSTM. The proposed deep learning architectures automatically learn patterns in the constructed image of the EEG signals. The efficiency of the proposed algorithms is evaluated on resting state EEG data obtained from 30 healthy subjects and 34 MDD patients. The experiments show that the 1DCNN-LSTM applied on constructed image of effective connectivity achieves best results with accuracy of 99.24% due to specific architecture which captures the presence of spatial and temporal relations in the brain connectivity. The proposed method as a diagnostic tool is able to help clinicians for diagnosing the MDD patients for early diagnosis and treatment.
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Affiliation(s)
- Abdolkarim Saeedi
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Maryam Saeedi
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Arash Maghsoudi
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Ahmad Shalbaf
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Voxel-Wise Brain-Wide Functional Connectivity Abnormalities in Patients with Primary Blepharospasm at Rest. Neural Plast 2021; 2021:6611703. [PMID: 33505457 PMCID: PMC7808842 DOI: 10.1155/2021/6611703] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Revised: 12/22/2020] [Accepted: 12/24/2020] [Indexed: 12/12/2022] Open
Abstract
Background Primary blepharospasm (BSP) is one of the most common focal dystonia and its pathophysiological mechanism remains unclear. An unbiased method was used in patients with BSP at rest to observe voxel-wise brain-wide functional connectivity (FC) changes. Method A total of 48 subjects, including 24 untreated patients with BSP and 24 healthy controls, were recruited to undergo functional magnetic resonance imaging (fMRI). The method of global-brain FC (GFC) was adopted to analyze the resting-state fMRI data. We designed the support vector machine (SVM) method to determine whether GFC abnormalities could be utilized to distinguish the patients from the controls. Results Relative to healthy controls, patients with BSP showed significantly decreased GFC in the bilateral superior medial prefrontal cortex/anterior cingulate cortex (MPFC/ACC) and increased GFC in the right postcentral gyrus/precentral gyrus/paracentral lobule, right superior frontal gyrus (SFG), and left paracentral lobule/supplement motor area (SMA), which were included in the default mode network (DMN) and sensorimotor network. SVM analysis showed that increased GFC values in the right postcentral gyrus/precentral gyrus/paracentral lobule could discriminate patients from controls with optimal accuracy, specificity, and sensitivity of 83.33%, 83.33%, and 83.33%, respectively. Conclusion This study suggested that abnormal GFC in the brain areas associated with sensorimotor network and DMN might underlie the pathophysiology of BSP, which provided a new perspective to understand BSP. GFC in the right postcentral gyrus/precentral gyrus/paracentral lobule might be utilized as a latent biomarker to differentiate patients with BSP from controls.
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Shalbaf A, Bagherzadeh S, Maghsoudi A. Transfer learning with deep convolutional neural network for automated detection of schizophrenia from EEG signals. Phys Eng Sci Med 2020; 43:1229-1239. [DOI: 10.1007/s13246-020-00925-9] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 09/08/2020] [Indexed: 12/17/2022]
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Brain connectivity analysis in fathers of children with autism. Cogn Neurodyn 2020; 14:781-793. [PMID: 33101531 DOI: 10.1007/s11571-020-09625-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Revised: 07/28/2020] [Accepted: 08/16/2020] [Indexed: 01/24/2023] Open
Abstract
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder in which changes in brain connectivity, associated with autistic-like traits in some individuals. First-degree relatives of children with autism may show mild deficits in social interaction. The present study investigates electroencephalography (EEG) brain connectivity patterns of the fathers who have children with autism while performing facial emotion labeling task. Fifteen biological fathers of children with the diagnosis of autism (Test Group) and fifteen fathers of neurotypical children with no personal or family history of autism (Control Group) participated in this study. Facial emotion labeling task was evaluated using a set of photos consisting of six categories (mild and extreme: anger, happiness, and sadness). Group Independent Component Analysis method was applied to EEG data to extract neural sources. Dynamic causal connectivity of neural sources signals was estimated using the multivariate autoregressive model and quantified by using the Granger causality-based methods. Statistical analysis showed significant differences (p value < 0.01) in the connectivity of neural sources in recognition of some emotions in two groups, which the most differences observed in the mild anger and mild sadness emotions. Short-range connectivity appeared in Test Group and conversely, long-range and interhemispheric connections are observed in Control Group. Finally, it can be concluded that the Test Group showed abnormal activity and connectivity in the brain network for the processing of emotional faces compared to the Control Group. We conclude that neural source connectivity analysis in fathers may be considered as a potential and promising biomarker of ASD.
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