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Yu Y, Oh Y, Kounios J, Beeman M. Electroencephalography Spectral-power Volatility Predicts Problem-solving Outcomes. J Cogn Neurosci 2024; 36:901-915. [PMID: 38437171 DOI: 10.1162/jocn_a_02136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2024]
Abstract
Temporal variability is a fundamental property of brain processes and is functionally important to human cognition. This study examined how fluctuations in neural oscillatory activity are related to problem-solving performance as one example of how temporal variability affects high-level cognition. We used volatility to assess step-by-step fluctuations of EEG spectral power while individuals attempted to solve word-association puzzles. Inspired by recent results with hidden-state modeling, we tested the hypothesis that spectral-power volatility is directly associated with problem-solving outcomes. As predicted, volatility was lower during trials solved with insight compared with those solved analytically. Moreover, volatility during prestimulus preparation for problem-solving predicted solving outcomes, including solving success and solving time. These novel findings were replicated in a separate data set from an anagram-solving task, suggesting that less-rapid transitions between neural oscillatory synchronization and desynchronization predict better solving performance and are conducive to solving with insight for these types of problems. Thus, volatility can be a valuable index of cognition-related brain dynamics.
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Affiliation(s)
- Yuhua Yu
- Northwestern University, Evanston, IL
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Sun Y, Chen X. Epileptic EEG Signal Detection Using Variational Modal Decomposition and Improved Grey Wolf Algorithm. SENSORS (BASEL, SWITZERLAND) 2023; 23:8078. [PMID: 37836909 PMCID: PMC10575143 DOI: 10.3390/s23198078] [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: 08/29/2023] [Revised: 09/16/2023] [Accepted: 09/18/2023] [Indexed: 10/15/2023]
Abstract
Epilepsy does great harm to the human body, and even threatens human life when it is serious. Therefore, research focused on the diagnosis and treatment of epilepsy holds paramount clinical significance. In this paper, we utilized variational modal decomposition (VMD) and an enhanced grey wolf algorithm to detect epileptic electroencephalogram (EEG) signals. Data were extracted from each patient's preseizure period and seizure period of 200 s each, with every 2 s as a segment, meaning 100 data points could be obtained for each patient's health period as well as 100 data points for each patient's epilepsy period. Variational modal decomposition (VMD) was used to obtain the corresponding intrinsic modal function (VMF) of the data. Then, the differential entropy (DE) and high frequency detection (HFD) of each VMF were extracted as features. The improved grey wolf algorithm is adopted for a selected channel to improve the maximum value of the channel. Finally, the EEG signal samples were classified using a support vector machine (SVM) classifier to achieve the accurate detection of epilepsy EEG signals. Experimental results show that the accuracy, sensitivity and specificity of the proposed method can reach 98.3%, 98.9% and 98.5%, respectively. The proposed algorithm in this paper can be used as an index to detect epileptic seizures and has certain guiding significance for the early diagnosis and effective treatment of epileptic patients.
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Affiliation(s)
- Yongxin Sun
- College of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, China
- College of Physics and Electronic Information, Baicheng Normal University, Baicheng 137099, China
| | - Xiaojuan Chen
- College of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, China
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Khan GH, Khan NA, Altaf MAB, Abbasi Q. A Shallow Autoencoder Framework for Epileptic Seizure Detection in EEG Signals. SENSORS (BASEL, SWITZERLAND) 2023; 23:4112. [PMID: 37112452 PMCID: PMC10144298 DOI: 10.3390/s23084112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 04/11/2023] [Accepted: 04/17/2023] [Indexed: 06/19/2023]
Abstract
This paper presents a trainable hybrid approach involving a shallow autoencoder (AE) and a conventional classifier for epileptic seizure detection. The signal segments of a channel of electroencephalogram (EEG) (EEG epochs) are classified as epileptic and non-epileptic by employing its encoded AE representation as a feature vector. Analysis on a single channel-basis and the low computational complexity of the algorithm allow its use in body sensor networks and wearable devices using one or few EEG channels for wearing comfort. This enables the extended diagnosis and monitoring of epileptic patients at home. The encoded representation of EEG signal segments is obtained based on training the shallow AE to minimize the signal reconstruction error. Extensive experimentation with classifiers has led us to propose two versions of our hybrid method: (a) one yielding the best classification performance compared to the reported methods using the k-nearest neighbor (kNN) classifier and (b) the second with a hardware-friendly architecture and yet with the best classification performance compared to other reported methods in this category using a support-vector machine (SVM) classifier. The algorithm is evaluated on the Children's Hospital Boston, Massachusetts Institute of Technology (CHB-MIT), and University of Bonn EEG datasets. The proposed method achieves 98.85% accuracy, 99.29% sensitivity, and 98.86% specificity on the CHB-MIT dataset using the kNN classifier. The best figures using the SVM classifier for accuracy, sensitivity, and specificity are 99.19%, 96.10%, and 99.19%, respectively. Our experiments establish the superiority of using an AE approach with a shallow architecture to generate a low-dimensionality yet effective EEG signal representation capable of high-performance abnormal seizure activity detection at a single-channel EEG level and with a fine granularity of 1 s EEG epochs.
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Affiliation(s)
- Gul Hameed Khan
- Department of Electrical Engineering, Lahore University of Management Sciences (LUMS), Lahore 54792, Pakistan; (N.A.K.); (M.A.B.A.)
| | - Nadeem Ahmad Khan
- Department of Electrical Engineering, Lahore University of Management Sciences (LUMS), Lahore 54792, Pakistan; (N.A.K.); (M.A.B.A.)
| | - Muhammad Awais Bin Altaf
- Department of Electrical Engineering, Lahore University of Management Sciences (LUMS), Lahore 54792, Pakistan; (N.A.K.); (M.A.B.A.)
- Engineering and Design Department, Western Washington University, Bellingham, WA 98225, USA
| | - Qammer Abbasi
- Communications Sensing and Imaging Research Group, James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK;
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Lee PL, Chen SH, Chang TC, Lee WK, Hsu HT, Chang HH. Continual Learning of a Transformer-Based Deep Learning Classifier Using an Initial Model from Action Observation EEG Data to Online Motor Imagery Classification. Bioengineering (Basel) 2023; 10:bioengineering10020186. [PMID: 36829681 PMCID: PMC9952173 DOI: 10.3390/bioengineering10020186] [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: 12/04/2022] [Revised: 01/20/2023] [Accepted: 01/24/2023] [Indexed: 02/04/2023] Open
Abstract
The motor imagery (MI)-based brain computer interface (BCI) is an intuitive interface that enables users to communicate with external environments through their minds. However, current MI-BCI systems ask naïve subjects to perform unfamiliar MI tasks with simple textual instruction or a visual/auditory cue. The unclear instruction for MI execution not only results in large inter-subject variability in the measured EEG patterns but also causes the difficulty of grouping cross-subject data for big-data training. In this study, we designed an BCI training method in a virtual reality (VR) environment. Subjects wore a head-mounted device (HMD) and executed action observation (AO) concurrently with MI (i.e., AO + MI) in VR environments. EEG signals recorded in AO + MI task were used to train an initial model, and the initial model was continually improved by the provision of EEG data in the following BCI training sessions. We recruited five healthy subjects, and each subject was requested to participate in three kinds of tasks, including an AO + MI task, an MI task, and the task of MI with visual feedback (MI-FB) three times. This study adopted a transformer- based spatial-temporal network (TSTN) to decode the user's MI intentions. In contrast to other convolutional neural network (CNN) or recurrent neural network (RNN) approaches, the TSTN extracts spatial and temporal features, and applies attention mechanisms along spatial and temporal dimensions to perceive the global dependencies. The mean detection accuracies of TSTN were 0.63, 0.68, 0.75, and 0.77 in the MI, first MI-FB, second MI-FB, and third MI-FB sessions, respectively. This study demonstrated the AO + MI gave an easier way for subjects to conform their imagery actions, and the BCI performance was improved with the continual learning of the MI-FB training process.
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Affiliation(s)
- Po-Lei Lee
- Department of Electrical Engineering, National Central University, Taoyuan 320, Taiwan
- Pervasive Artificial Intelligence Research Labs, Hsinchu 300, Taiwan
| | - Sheng-Hao Chen
- Department of Electrical Engineering, National Central University, Taoyuan 320, Taiwan
| | - Tzu-Chien Chang
- Department of Electrical Engineering, National Central University, Taoyuan 320, Taiwan
| | - Wei-Kung Lee
- Department of Rehabilitation, Taoyuan General Hospital, Taoyuan 330, Taiwan
| | - Hao-Teng Hsu
- Department of Electrical Engineering, National Central University, Taoyuan 320, Taiwan
- Pervasive Artificial Intelligence Research Labs, Hsinchu 300, Taiwan
| | - Hsiao-Huang Chang
- Division of Cardiovascular Surgery, Department of Surgery, Taipei Veterans General Hospital, Taipei 112, Taiwan
- Department of Surgery, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
- Correspondence: ; Tel.: +886-937-919-107
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Chen X, Gupta RS, Gupta L. Exploiting the Cone of Influence for Improving the Performance of Wavelet Transform-Based Models for ERP/EEG Classification. Brain Sci 2022; 13:brainsci13010021. [PMID: 36672003 PMCID: PMC9856575 DOI: 10.3390/brainsci13010021] [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: 11/22/2022] [Revised: 12/10/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022] Open
Abstract
Features extracted from the wavelet transform coefficient matrix are widely used in the design of machine learning models to classify event-related potential (ERP) and electroencephalography (EEG) signals in a wide range of brain activity research and clinical studies. This novel study is aimed at dramatically improving the performance of such wavelet-based classifiers by exploiting information offered by the cone of influence (COI) of the continuous wavelet transform (CWT). The COI is a boundary that is superimposed on the wavelet scalogram to delineate the coefficients that are accurate from those that are inaccurate due to edge effects. The features derived from the inaccurate coefficients are, therefore, unreliable. In this study, it is hypothesized that the classifier performance would improve if unreliable features, which are outside the COI, are zeroed out, and the performance would improve even further if those features are cropped out completely. The entire, zeroed out, and cropped scalograms are referred to as the "same" (S)-scalogram, "zeroed out" (Z)-scalogram, and the "valid" (V)-scalogram, respectively. The strategy to validate the hypotheses is to formulate three classification approaches in which the feature vectors are extracted from the (a) S-scalogram in the standard manner, (b) Z-scalogram, and (c) V-scalogram. A subsampling strategy is developed to generate small-sample ERP ensembles to enable customized classifier design for single subjects, and a strategy is developed to select a subset of channels from multiple ERP channels. The three scalogram approaches are implemented using support vector machines, random forests, k-nearest neighbor, multilayer perceptron neural networks, and deep learning convolution neural networks. In order to validate the performance hypotheses, experiments are designed to classify the multi-channel ERPs of five subjects engaged in distinguishing between synonymous and non-synonymous word pairs. The results confirm that the classifiers using the Z-scalogram features outperform those using the S-scalogram features, and the classifiers using the V-scalogram features outperform those using the Z-scalogram features. Most importantly, the relative improvement of the V-scalogram classifiers over the standard S-scalogram classifiers is dramatic. Additionally, enabling the design of customized classifiers for individual subjects is an important contribution to ERP/EEG-based studies and diagnoses of patient-specific disorders.
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Affiliation(s)
- Xiaoqian Chen
- School of Electrical, Computer, and Biomedical Engineering, Southern Illinois University, Carbondale, IL 62901, USA
| | - Resh S. Gupta
- Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, San Diego, CA 92161, USA
| | - Lalit Gupta
- School of Electrical, Computer, and Biomedical Engineering, Southern Illinois University, Carbondale, IL 62901, USA
- Correspondence:
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Epileptic Seizure Detection Based on Variational Mode Decomposition and Deep Forest Using EEG Signals. Brain Sci 2022; 12:brainsci12101275. [PMID: 36291210 PMCID: PMC9599930 DOI: 10.3390/brainsci12101275] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Revised: 09/16/2022] [Accepted: 09/20/2022] [Indexed: 11/16/2022] Open
Abstract
Electroencephalography (EEG) records the electrical activity of the brain, which is an important tool for the automatic detection of epileptic seizures. It is certainly a very heavy burden to only recognize EEG epilepsy manually, so the method of computer-assisted treatment is of great importance. This paper presents a seizure detection algorithm based on variational modal decomposition (VMD) and a deep forest (DF) model. Variational modal decomposition is performed on EEG recordings, and the first three variational modal functions (VMFs) are selected to construct the time–frequency distribution of the EEG signals. Then, the log−Euclidean covariance matrix (LECM) is computed to represent the EEG properties and form EEG features. The deep forest model is applied to complete the EEG signal classification, which is a non-neural network deep model with a cascade structure that performs feature learning through the forest. In addition, to improve the classification accuracy, postprocessing techniques are performed to generate the discriminant results by moving average filtering and adaptive collar expansion. The algorithm was evaluated on the Bonn EEG dataset and the Freiburg long−term EEG dataset, and the former achieved a sensitivity and specificity of 99.32% and 99.31%, respectively. The mean sensitivity and specificity of this method for the 21 patients in the Freiburg dataset were 95.2% and 98.56%, respectively, with a false detection rate of 0.36/h. These results demonstrate the superior performance advantage of our algorithm and indicate its great research potential in epilepsy detection.
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