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Rosenfelder MJ, Spiliopoulou M, Hoppenstedt B, Pryss R, Fissler P, della Piedra Walter M, Kolassa IT, Bender A. Stability of mental motor-imagery classification in EEG depends on the choice of classifier model and experiment design, but not on signal preprocessing. Front Comput Neurosci 2023; 17:1142948. [PMID: 37180880 PMCID: PMC10169631 DOI: 10.3389/fncom.2023.1142948] [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: 01/12/2023] [Accepted: 04/05/2023] [Indexed: 05/16/2023] Open
Abstract
Introduction Modern consciousness research has developed diagnostic tests to improve the diagnostic accuracy of different states of consciousness via electroencephalography (EEG)-based mental motor imagery (MI), which is still challenging and lacks a consensus on how to best analyse MI EEG-data. An optimally designed and analyzed paradigm must detect command-following in all healthy individuals, before it can be applied in patients, e.g., for the diagnosis of disorders of consciousness (DOC). Methods We investigated the effects of two important steps in the raw signal preprocessing on predicting participant performance (F1) and machine-learning classifier performance (area-under-curve, AUC) in eight healthy individuals, that are based solely on MI using high-density EEG (HD-EEG): artifact correction (manual correction with vs. without Independent Component Analysis [ICA]), region of interest (ROI; motor area vs. whole brain), and machine-learning algorithm (support-vector machine [SVM] vs. k-nearest neighbor [KNN]). Results Results revealed no significant effects of artifact correction and ROI on predicting participant performance (F1) and classifier performance (AUC) scores (all ps > 0.05) in the SVM classification model. In the KNN model, ROI had a significant influence on the classifier performance [F(1,8.939) = 7.585, p = 0.023]. There was no evidence for artifact correction and ROI selection changing the prediction of participants performance and classifier performance in EEG-based mental MI if using SVM-based classification (71-100% correct classifications across different signal preprocessing methods). The variance in the prediction of participant performance was significantly higher when the experiment started with a resting-state compared to a mental MI task block [X2(1) = 5.849, p = 0.016]. Discussion Overall, we could show that classification is stable across different modes of EEG signal preprocessing when using SVM models. Exploratory analysis gave a hint toward potential effects of the sequence of task execution on the prediction of participant performance, which should be taken into account in future studies.
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Affiliation(s)
- Martin Justinus Rosenfelder
- Institute of Psychology and Education, Clinical and Biological Psychology, Ulm University, Ulm, Germany
- Therapiezentrum Burgau, Burgau, Germany
| | - Myra Spiliopoulou
- Knowledge Management and Discovery Lab, Faculty of Computer Science, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany
| | | | - Rüdiger Pryss
- Institute of Databases and Information Systems, Ulm University, Ulm, Germany
- Institute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany
| | - Patrick Fissler
- Institute of Psychology and Education, Clinical and Biological Psychology, Ulm University, Ulm, Germany
- Psychiatric Services Thurgau, Münsterlingen, Switzerland
- University Hospital for Psychiatry and Psychotherapy, Paracelsus Medical University, Salzburg, Austria
| | - Mario della Piedra Walter
- Therapiezentrum Burgau, Burgau, Germany
- Faculty 2: Biology/Chemistry, University of Bremen, Bremen, Germany
| | - Iris-Tatjana Kolassa
- Institute of Psychology and Education, Clinical and Biological Psychology, Ulm University, Ulm, Germany
| | - Andreas Bender
- Therapiezentrum Burgau, Burgau, Germany
- Department of Neurology, University of Munich, Munich, Germany
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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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Mirjalili S, Powell P, Strunk J, James T, Duarte A. Evaluation of classification approaches for distinguishing brain states predictive of episodic memory performance from electroencephalography: Abbreviated Title: Evaluating methods of classifying memory states from EEG. Neuroimage 2022; 247:118851. [PMID: 34954026 PMCID: PMC8824531 DOI: 10.1016/j.neuroimage.2021.118851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 12/20/2021] [Accepted: 12/21/2021] [Indexed: 11/21/2022] Open
Abstract
Previous studies have attempted to separate single trial neural responses for events a person is likely to remember from those they are likely to forget using machine learning classification methods. Successful single trial classification holds potential for translation into the clinical realm for real-time detection of memory and other cognitive states to provide real-time interventions (i.e., brain-computer interfaces). However, most of these studies-and classification analyses in general- do not make clear if the chosen methodology is optimally suited for the classification of memory-related brain states. To address this problem, we systematically compared different methods for every step of classification (i.e., feature extraction, feature selection, classifier selection) to investigate which methods work best for decoding episodic memory brain states-the first analysis of its kind. Using an adult lifespan sample EEG dataset collected during performance of an episodic context encoding and retrieval task, we found that no specific feature type (including Common Spatial Pattern (CSP)-based features, mean, variance, correlation, features based on AR model, entropy, phase, and phase synchronization) outperformed others consistently in distinguishing different memory classes. However, extracting all of these feature types consistently outperformed extracting only one type of feature. Additionally, the combination of filtering and sequential forward selection was the optimal method to select the effective features compared to filtering alone or performing no feature selection at all. Moreover, although all classifiers performed at a fairly similar level, LASSO was consistently the highest performing classifier compared to other commonly used options (i.e., naïve Bayes, SVM, and logistic regression) while naïve Bayes was the fastest classifier. Lastly, for multiclass classification (i.e., levels of context memory confidence and context feature perception), generalizing the binary classification using the binary decision tree performed better than the voting or one versus rest method. These methods were shown to outperform alternative approaches for three orthogonal datasets (i.e., EEG working memory, EEG motor imagery, and MEG working memory), supporting their generalizability. Our results provide an optimized methodological process for classifying single-trial neural data and provide important insight and recommendations for a cognitive neuroscientist's ability to make informed choices at all stages of the classification process for predicting memory and other cognitive states.
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Affiliation(s)
| | | | | | - Taylor James
- School of Psychology, Georgia Institute of Technology; Department of Neurology, Emory University, Atlanta, GA, USA.
| | - Audrey Duarte
- Department of Psychology, University of Texas at Austin.
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Tan X, Guo C, Jiang T, Fu K, Zhou N, Yuan J, Zhang G. A new semi-supervised algorithm combined with MCICA optimizing SVM for motion imagination EEG classification. INTELL DATA ANAL 2021. [DOI: 10.3233/ida-205188] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
This paper proposed a new semi-supervised algorithm combined with Mutual-cross Imperial Competition Algorithm (MCICA) optimizing Support Vector Machine (SVM) for motion imagination EEG classification, which not only reduces the tedious and time-consuming training process and enhances the adaptability of Brain Computer Interface (BCI), but also utilizes the MCICA to optimize the parameters of SVM in the semi-supervised process. This algorithm combines mutual information and cross validation to construct objective function in the semi-supervised training process, and uses the constructed objective function to establish the semi-supervised model of MCICA for optimizing the parameters of SVM, and finally applies the selected optimal parameters to the data set Iva of 2005 BCI competition to verify its effectiveness. The results showed that the proposed algorithm is effective in optimizing parameters and has good robustness and generalization in solving small sample classification problems.
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Affiliation(s)
- Xuemin Tan
- College of Control Engineering, Chengdu University of Information Technology, Chengdu, Sichuan, China
| | - Chao Guo
- State Grid Chengdu Power Supply Company, Chengdu, Sichuan, China
| | - Tao Jiang
- College of Control Engineering, Chengdu University of Information Technology, Chengdu, Sichuan, China
| | - Kechang Fu
- College of Control Engineering, Chengdu University of Information Technology, Chengdu, Sichuan, China
| | - Nan Zhou
- College of Control Engineering, Chengdu University of Information Technology, Chengdu, Sichuan, China
| | - Jianying Yuan
- College of Control Engineering, Chengdu University of Information Technology, Chengdu, Sichuan, China
| | - Guoliang Zhang
- College of Control Engineering, Chengdu University of Information Technology, Chengdu, Sichuan, China
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Malan N, Sharma S. Motor Imagery EEG Spectral-Spatial Feature Optimization Using Dual-Tree Complex Wavelet and Neighbourhood Component Analysis. Ing Rech Biomed 2021. [DOI: 10.1016/j.irbm.2021.01.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Feature Selection Model based on EEG Signals for Assessing the Cognitive Workload in Drivers. SENSORS 2020; 20:s20205881. [PMID: 33080866 PMCID: PMC7589097 DOI: 10.3390/s20205881] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 10/09/2020] [Accepted: 10/13/2020] [Indexed: 11/17/2022]
Abstract
In recent years, research has focused on generating mechanisms to assess the levels of subjects’ cognitive workload when performing various activities that demand high concentration levels, such as driving a vehicle. These mechanisms have implemented several tools for analyzing the cognitive workload, and electroencephalographic (EEG) signals have been most frequently used due to their high precision. However, one of the main challenges in implementing the EEG signals is finding appropriate information for identifying cognitive states. Here, we present a new feature selection model for pattern recognition using information from EEG signals based on machine learning techniques called GALoRIS. GALoRIS combines Genetic Algorithms and Logistic Regression to create a new fitness function that identifies and selects the critical EEG features that contribute to recognizing high and low cognitive workloads and structures a new dataset capable of optimizing the model’s predictive process. We found that GALoRIS identifies data related to high and low cognitive workloads of subjects while driving a vehicle using information extracted from multiple EEG signals, reducing the original dataset by more than 50% and maximizing the model’s predictive capacity, achieving a precision rate greater than 90%.
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Xu J, Zheng H, Wang J, Li D, Fang X. Recognition of EEG Signal Motor Imagery Intention Based on Deep Multi-View Feature Learning. SENSORS (BASEL, SWITZERLAND) 2020; 20:E3496. [PMID: 32575798 PMCID: PMC7349253 DOI: 10.3390/s20123496] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Revised: 06/05/2020] [Accepted: 06/18/2020] [Indexed: 01/16/2023]
Abstract
Recognition of motor imagery intention is one of the hot current research focuses of brain-computer interface (BCI) studies. It can help patients with physical dyskinesia to convey their movement intentions. In recent years, breakthroughs have been made in the research on recognition of motor imagery task using deep learning, but if the important features related to motor imagery are ignored, it may lead to a decline in the recognition performance of the algorithm. This paper proposes a new deep multi-view feature learning method for the classification task of motor imagery electroencephalogram (EEG) signals. In order to obtain more representative motor imagery features in EEG signals, we introduced a multi-view feature representation based on the characteristics of EEG signals and the differences between different features. Different feature extraction methods were used to respectively extract the time domain, frequency domain, time-frequency domain and spatial features of EEG signals, so as to made them cooperate and complement. Then, the deep restricted Boltzmann machine (RBM) network improved by t-distributed stochastic neighbor embedding(t-SNE) was adopted to learn the multi-view features of EEG signals, so that the algorithm removed the feature redundancy while took into account the global characteristics in the multi-view feature sequence, reduced the dimension of the multi-visual features and enhanced the recognizability of the features. Finally, support vector machine (SVM) was chosen to classify deep multi-view features. Applying our proposed method to the BCI competition IV 2a dataset we obtained excellent classification results. The results show that the deep multi-view feature learning method further improved the classification accuracy of motor imagery tasks.
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Affiliation(s)
- Jiacan Xu
- School of Information Science and Engineering, Northeastern University, Shenyang 110819, China; (J.W.); (D.L.); (X.F.)
| | - Hao Zheng
- School of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, China;
| | - Jianhui Wang
- School of Information Science and Engineering, Northeastern University, Shenyang 110819, China; (J.W.); (D.L.); (X.F.)
| | - Donglin Li
- School of Information Science and Engineering, Northeastern University, Shenyang 110819, China; (J.W.); (D.L.); (X.F.)
| | - Xiaoke Fang
- School of Information Science and Engineering, Northeastern University, Shenyang 110819, China; (J.W.); (D.L.); (X.F.)
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