1
|
Chen A, Sun D, Gao X, Zhang D. A novel feature extraction method PSS-CSP for binary motor imagery - based brain-computer interfaces. Comput Biol Med 2024; 177:108619. [PMID: 38796879 DOI: 10.1016/j.compbiomed.2024.108619] [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: 11/19/2023] [Revised: 05/07/2024] [Accepted: 05/14/2024] [Indexed: 05/29/2024]
Abstract
In order to improve the performance of binary motor imagery (MI) - based brain-computer interfaces (BCIs) using electroencephalography (EEG), a novel method (PSS-CSP) is proposed, which combines spectral subtraction with common spatial pattern. Spectral subtraction is an effective denoising method which is initially adopted to process MI-based EEG signals for binary BCIs in this work. On this basis, we proposed a novel feature extraction method called power spectral subtraction-based common spatial pattern (PSS-CSP) , which calculates the differences in power spectrum between binary classes of EEG signals and uses the differences in the feature extraction process. Additionally, support vector machine (SVM) algorithm is used for signal classification. Results show the proposed method (PSS-CSP) outperforms certain existing methods, achieving a classification accuracy of 76.8% on the BCIIV dataset 2b, and 76.25% and 77.38% on the OpenBMI dataset session 1 and session 2, respectively.
Collapse
Affiliation(s)
- Ao Chen
- College of Communication Engineering, Jilin University, Changchun 130012, China
| | - Dayang Sun
- College of Communication Engineering, Jilin University, Changchun 130012, China.
| | - Xin Gao
- Centre for Autonomous Robotics (CENTAUR), Department of Electronic Electrical Engineering, University of Bath, Bath BA2 7AY, United Kingdom
| | - Dingguo Zhang
- Centre for Autonomous Robotics (CENTAUR), Department of Electronic Electrical Engineering, University of Bath, Bath BA2 7AY, United Kingdom
| |
Collapse
|
2
|
Luo Y, Mu W, Wang L, Wang J, Wang P, Gan Z, Zhang L, Kang X. An EEG channel selection method for motor imagery based on Fisher score and local optimization. J Neural Eng 2024; 21:036030. [PMID: 38842111 DOI: 10.1088/1741-2552/ad504a] [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: 10/25/2023] [Accepted: 05/24/2024] [Indexed: 06/07/2024]
Abstract
Objective. Multi-channel electroencephalogram (EEG) technology in brain-computer interface (BCI) research offers the advantage of enhanced spatial resolution and system performance. However, this also implies that more time is needed in the data processing stage, which is not conducive to the rapid response of BCI. Hence, it is a necessary and challenging task to reduce the number of EEG channels while maintaining decoding effectiveness.Approach. In this paper, we propose a local optimization method based on the Fisher score for within-subject EEG channel selection. Initially, we extract the common spatial pattern characteristics of EEG signals in different bands, calculate Fisher scores for each channel based on these characteristics, and rank them accordingly. Subsequently, we employ a local optimization method to finalize the channel selection.Main results. On the BCI Competition IV Dataset IIa, our method selects an average of 11 channels across four bands, achieving an average accuracy of 79.37%. This represents a 6.52% improvement compared to using the full set of 22 channels. On our self-collected dataset, our method similarly achieves a significant improvement of 24.20% with less than half of the channels, resulting in an average accuracy of 76.95%.Significance. This research explores the importance of channel combinations in channel selection tasks and reveals that appropriately combining channels can further enhance the quality of channel selection. The results indicate that the model selected a small number of channels with higher accuracy in two-class motor imagery EEG classification tasks. Additionally, it improves the portability of BCI systems through channel selection and combinations, offering the potential for the development of portable BCI systems.
Collapse
Affiliation(s)
- Yangjie Luo
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI & Robotics, Academy for Engineering & Technology, Fudan University, Shanghai, People's Republic of China
| | - Wei Mu
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI & Robotics, Academy for Engineering & Technology, Fudan University, Shanghai, People's Republic of China
| | - Lu Wang
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI & Robotics, Academy for Engineering & Technology, Fudan University, Shanghai, People's Republic of China
| | - Junkongshuai Wang
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI & Robotics, Academy for Engineering & Technology, Fudan University, Shanghai, People's Republic of China
| | - Pengchao Wang
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI & Robotics, Academy for Engineering & Technology, Fudan University, Shanghai, People's Republic of China
| | - Zhongxue Gan
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI & Robotics, Academy for Engineering & Technology, Fudan University, Shanghai, People's Republic of China
- Ji Hua Laboratory, Foshan, People's Republic of China
| | - Lihua Zhang
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI & Robotics, Academy for Engineering & Technology, Fudan University, Shanghai, People's Republic of China
- Ji Hua Laboratory, Foshan, People's Republic of China
| | - Xiaoyang Kang
- Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI & Robotics, Academy for Engineering & Technology, Fudan University, Shanghai, People's Republic of China
- Ji Hua Laboratory, Foshan, People's Republic of China
- Yiwu Research Institute of Fudan University, Yiwu City, People's Republic of China
- Research Center for Intelligent Sensing, Zhejiang Lab, Hangzhou, People's Republic of China
| |
Collapse
|
3
|
Xue Q, Song Y, Wu H, Cheng Y, Pan H. Graph neural network based on brain inspired forward-forward mechanism for motor imagery classification in brain-computer interfaces. Front Neurosci 2024; 18:1309594. [PMID: 38606308 PMCID: PMC11008472 DOI: 10.3389/fnins.2024.1309594] [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: 10/08/2023] [Accepted: 03/04/2024] [Indexed: 04/13/2024] Open
Abstract
Introduction Within the development of brain-computer interface (BCI) systems, it is crucial to consider the impact of brain network dynamics and neural signal transmission mechanisms on electroencephalogram-based motor imagery (MI-EEG) tasks. However, conventional deep learning (DL) methods cannot reflect the topological relationship among electrodes, thereby hindering the effective decoding of brain activity. Methods Inspired by the concept of brain neuronal forward-forward (F-F) mechanism, a novel DL framework based on Graph Neural Network combined forward-forward mechanism (F-FGCN) is presented. F-FGCN framework aims to enhance EEG signal decoding performance by applying functional topological relationships and signal propagation mechanism. The fusion process involves converting the multi-channel EEG into a sequence of signals and constructing a network grounded on the Pearson correlation coeffcient, effectively representing the associations between channels. Our model initially pre-trains the Graph Convolutional Network (GCN), and fine-tunes the output layer to obtain the feature vector. Moreover, the F-F model is used for advanced feature extraction and classification. Results and discussion Achievement of F-FGCN is assessed on the PhysioNet dataset for a four-class categorization, compared with various classical and state-of-the-art models. The learned features of the F-FGCN substantially amplify the performance of downstream classifiers, achieving the highest accuracy of 96.11% and 82.37% at the subject and group levels, respectively. Experimental results affirm the potency of FFGCN in enhancing EEG decoding performance, thus paving the way for BCI applications.
Collapse
Affiliation(s)
- Qiwei Xue
- Institute of Plasma Physics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China
- University of Science and Technology of China, Hefei, China
- Mechanical Department, School of Energy Systems, Lappeenranta University of Technology (LUT), Lappeenranta, Finland
| | - Yuntao Song
- Institute of Plasma Physics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China
- University of Science and Technology of China, Hefei, China
| | - Huapeng Wu
- Mechanical Department, School of Energy Systems, Lappeenranta University of Technology (LUT), Lappeenranta, Finland
| | - Yong Cheng
- Institute of Plasma Physics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China
| | - Hongtao Pan
- Institute of Plasma Physics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China
| |
Collapse
|
4
|
Zhong XC, Wang Q, Liu D, Liao JX, Yang R, Duan S, Ding G, Sun J. A deep domain adaptation framework with correlation alignment for EEG-based motor imagery classification. Comput Biol Med 2023; 163:107235. [PMID: 37442010 DOI: 10.1016/j.compbiomed.2023.107235] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 06/30/2023] [Accepted: 07/01/2023] [Indexed: 07/15/2023]
Abstract
It is impractical to collect sufficient and well-labeled EEG data in Brain-computer interface because of the time-consuming data acquisition and costly annotation. Conventional classification methods reusing EEG data from different subjects and time periods (across domains) significantly decrease the classification accuracy of motor imagery. In this paper, we propose a deep domain adaptation framework with correlation alignment (DDAF-CORAL) to solve the problem of distribution divergence for motor imagery classification across domains. Specifically, a two-stage framework is adopted to extract deep features for raw EEG data. The distribution divergence caused by subjected-related and time-related variations is further minimized by aligning the covariance of the source and target EEG feature distributions. Finally, the classification loss and adaptation loss are optimized simultaneously to achieve sufficient discriminative classification performance and low feature distribution divergence. Extensive experiments on three EEG datasets demonstrate that our proposed method can effectively reduce the distribution divergence between the source and target EEG data. The results show that our proposed method delivers outperformance (an average classification accuracy of 92.9% for within-session, an average kappa value of 0.761 for cross-session, and an average classification accuracy of 83.3% for cross-subject) in two-class classification tasks compared to other state-of-the-art methods.
Collapse
Affiliation(s)
- Xiao-Cong Zhong
- School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China
| | - Qisong Wang
- School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China.
| | - Dan Liu
- School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China
| | - Jing-Xiao Liao
- School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China
| | - Runze Yang
- School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China
| | - Sanhe Duan
- School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China
| | - Guohua Ding
- School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China
| | - Jinwei Sun
- School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin, 150001, China
| |
Collapse
|
5
|
Shang Y, Gao X, An A. Multi-band spatial feature extraction and classification for motor imaging EEG signals based on OSFBCSP-GAO-SVM model : EEG signal processing. Med Biol Eng Comput 2023; 61:1581-1602. [PMID: 36813927 DOI: 10.1007/s11517-023-02793-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 12/09/2022] [Indexed: 02/24/2023]
Abstract
Electroencephalogram (EEG) is a non-stationary random signal with strong background noise, which makes its feature extraction difficult and recognition rate low. This paper presents a feature extraction and classification model of motor imagery EEG signals based on wavelet threshold denoising. Firstly, this paper uses the improved wavelet threshold algorithm to obtain the denoised EEG signal, divides all EEG channel data into multiple partially overlapping frequency bands, and uses the common spatial pattern (CSP) method to construct multiple spatial filters to extract the characteristics of EEG signals. Secondly, EEG signal classification and recognition are realized by the support vector machine algorithm optimized by a genetic algorithm. Finally, the dataset of the third brain-computer interface (BCI) competition and the dataset of the fourth BCI competition is selected to verify the classification effect of the algorithm. The highest accuracy of this method for two BCI competition datasets is 92.86% and 87.16%, respectively, which is obviously superior to the traditional algorithm model. The accuracy of EEG feature classification is improved. It shows that an overlapping sub-band filter banks common spatial pattern-genetic algorithms optimization-support vector machines (OSFBCSP-GAO-SVM) model is an effective model for feature extraction and classification of motor imagination EEG signals.
Collapse
Affiliation(s)
- Yong Shang
- College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, 730050, Gansu, China
| | - Xing Gao
- Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou University of Technology, Lanzhou, 730050, Gansu, China
| | - Aimin An
- College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, 730050, Gansu, China. .,Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou University of Technology, Lanzhou, 730050, Gansu, China. .,National Demonstration Center for Experimental Electrical and Control Engineering Education, Lanzhou University of Technology, Lanzhou, 730050, Gansu, China.
| |
Collapse
|
6
|
Yang L, Shi T, Lv J, Liu Y, Dai Y, Zou L. A multi-feature fusion decoding study for unilateral upper-limb fine motor imagery. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:2482-2500. [PMID: 36899543 DOI: 10.3934/mbe.2023116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
To address the fact that the classical motor imagination paradigm has no noticeable effect on the rehabilitation training of upper limbs in patients after stroke and the corresponding feature extraction algorithm is limited to a single domain, this paper describes the design of a unilateral upper-limb fine motor imagination paradigm and the collection of data from 20 healthy people. It presents a feature extraction algorithm for multi-domain fusion and compares the common spatial pattern (CSP), improved multiscale permutation entropy (IMPE) and multi-domain fusion features of all participants through the use of decision tree, linear discriminant analysis, naive Bayes, a support vector machine, k-nearest neighbor and ensemble classification precision algorithms in the ensemble classifier. For the same subject, the average classification accuracy improvement of the same classifier for multi-domain feature extraction relative to CSP feature results went up by 1.52%. The average classification accuracy improvement of the same classifier went up by 32.87% relative to the IMPE feature classification results. This study's unilateral fine motor imagery paradigm and multi-domain feature fusion algorithm provide new ideas for upper limb rehabilitation after stroke.
Collapse
Affiliation(s)
- Liangyu Yang
- The School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, China
| | - Tianyu Shi
- The School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, China
| | - Jidong Lv
- The School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, China
| | - Yan Liu
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Department of Medical Image, Suzhou 215163, China
- Suzhou Guokekangcheng Medical Technique Co., Ltd., Suzhou 215163, China
| | - Yakang Dai
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Department of Medical Image, Suzhou 215163, China
- Suzhou Guokekangcheng Medical Technique Co., Ltd., Suzhou 215163, China
| | - Ling Zou
- The School of Microelectronics and Control Engineering, Changzhou University, Changzhou, Jiangsu 213164, China
- Key Laboratory of Brain Machine Collaborative Intelligence Foundation of Zhejiang Province, Hangzhou, Zhejiang 310018, China
| |
Collapse
|
7
|
Hu H, Pu Z, Li H, Liu Z, Wang P. Learning Optimal Time-Frequency-Spatial Features by the CiSSA-CSP Method for Motor Imagery EEG Classification. SENSORS (BASEL, SWITZERLAND) 2022; 22:8526. [PMID: 36366225 PMCID: PMC9658317 DOI: 10.3390/s22218526] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 10/27/2022] [Accepted: 11/02/2022] [Indexed: 06/16/2023]
Abstract
The common spatial pattern (CSP) is a popular method in feature extraction for motor imagery (MI) electroencephalogram (EEG) classification in brain-computer interface (BCI) systems. However, combining temporal and spectral information in the CSP-based spatial features is still a challenging issue, which greatly affects the performance of MI-based BCI systems. Here, we propose a novel circulant singular spectrum analysis embedded CSP (CiSSA-CSP) method for learning the optimal time-frequency-spatial features to improve the MI classification accuracy. Specifically, raw EEG data are first segmented into multiple time segments and spectrum-specific sub-bands are further derived by CiSSA from each time segment in a set of non-overlapping filter bands. CSP features extracted from all time-frequency segments contain more sufficient time-frequency-spatial information. An experimental study was implemented on the publicly available EEG dataset (BCI Competition III dataset IVa) and a self-collected experimental EEG dataset to validate the effectiveness of the CiSSA-CSP method. Experimental results demonstrate that discriminative and robust features are extracted effectively. Compared with several state-of-the-art methods, the proposed method exhibited optimal accuracies of 96.6% and 95.2% on the public and experimental datasets, respectively, which confirms that it is a promising method for improving the performance of MI-based BCIs.
Collapse
Affiliation(s)
| | | | | | | | - Peng Wang
- Correspondence: ; Tel.: +86-10-6277-2007
| |
Collapse
|
8
|
Salimpour S, Kalbkhani H, Seyyedi S, Solouk V. Stockwell transform and semi-supervised feature selection from deep features for classification of BCI signals. Sci Rep 2022; 12:11773. [PMID: 35817814 PMCID: PMC9273790 DOI: 10.1038/s41598-022-15813-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Accepted: 06/29/2022] [Indexed: 11/30/2022] Open
Abstract
Over the past few years, the processing of motor imagery (MI) electroencephalography (EEG) signals has been attracted for developing brain-computer interface (BCI) applications, since feature extraction and classification of these signals are extremely difficult due to the inherent complexity and tendency to artifact properties of them. The BCI systems can provide a direct interaction pathway/channel between the brain and a peripheral device, hence the MI EEG-based BCI systems seem crucial to control external devices for patients suffering from motor disabilities. The current study presents a semi-supervised model based on three-stage feature extraction and machine learning algorithms for MI EEG signal classification in order to improve the classification accuracy with smaller number of deep features for distinguishing right- and left-hand MI tasks. Stockwell transform is employed at the first phase of the proposed feature extraction method to generate two-dimensional time-frequency maps (TFMs) from one-dimensional EEG signals. Next, the convolutional neural network (CNN) is applied to find deep feature sets from TFMs. Then, the semi-supervised discriminant analysis (SDA) is utilized to minimize the number of descriptors. Finally, the performance of five classifiers, including support vector machine, discriminant analysis, k-nearest neighbor, decision tree, random forest, and the fusion of them are compared. The hyperparameters of SDA and mentioned classifiers are optimized by Bayesian optimization to maximize the accuracy. The presented model is validated using BCI competition II dataset III and BCI competition IV dataset 2b. The performance metrics of the proposed method indicate its efficiency for classifying MI EEG signals.
Collapse
Affiliation(s)
- Sahar Salimpour
- Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
| | - Hashem Kalbkhani
- Faculty of Electrical Engineering, Urmia University of Technology, Urmia, Iran
| | - Saeed Seyyedi
- University of California San Francisco and Berkeley, Berkeley, USA
| | - Vahid Solouk
- Department of IT and Computer Engineering, Urmia University of Technology, Urmia, Iran.
| |
Collapse
|
9
|
Yu Z, Chen W, Zhang T. Motor imagery EEG classification algorithm based on improved lightweight feature fusion network. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103618] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
10
|
Hekmatmanesh A, Wu H, Handroos H. Largest Lyapunov Exponent Optimization for Control of a Bionic-Hand: A Brain Computer Interface Study. FRONTIERS IN REHABILITATION SCIENCES 2022; 2:802070. [PMID: 36188803 PMCID: PMC9397699 DOI: 10.3389/fresc.2021.802070] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 12/28/2021] [Indexed: 01/23/2023]
Abstract
This paper introduces a brain control bionic-hand, and several methods have been developed for predicting and quantifying the behavior of a non-linear system such as a brain. Non-invasive investigations on the brain were conducted by means of electroencephalograph (EEG) signal oscillations. One of the prominent concepts necessary to understand EEG signals is the chaotic concept named the fractal dimension and the largest Lyapunov exponent (LLE). Specifically, the LLE algorithm called the chaotic quantifier method has been employed to compute the complexity of a system. The LLE helps us to understand how the complexity of the brain changes while making a decision to close and open a fist. The LLE has been used for a long time, but here we optimize the traditional LLE algorithm to attain higher accuracy and precision for controlling a bionic hand. In the current study, the main constant input parameters of the LLE, named the false nearest neighbor and mutual information, are parameterized and then optimized by means of the Water Drop (WD) and Chaotic Tug of War (CTW) optimizers. The optimized LLE is then employed to identify imaginary movement patterns from the EEG signals for control of a bionic hand. The experiment includes 21 subjects for recording imaginary patterns. The results illustrated that the CTW solution achieved a higher average accuracy rate of 72.31% in comparison to the traditional LLE and optimized LLE by using a WD optimizer. The study concluded that the traditional LLE required enhancement using optimization methods. In addition, the CTW approximation method has the potential for more efficient solutions in comparison to the WD method.
Collapse
|
11
|
Singh Malan N, Sharma S. Time window and frequency band optimization using regularized neighbourhood component analysis for Multi-View Motor Imagery EEG classification. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102550] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
|