Multiple graph fusion based on Riemannian geometry for motor imagery classification

AbstractIn motor imagery-based brain-computer interfaces (BCIs), the spatial covariance features of electroencephalography (EEG) signals that lie on Riemannian manifolds are used to enhance the classification performance of motor imagery BCIs. However, the problem of subject-specific bandpass frequency selection frequently arises in Riemannian manifold-based methods. In this study, we propose a multiple Riemannian graph fusion (MRGF) model to optimize the subject-specific frequency band for a Riemannian manifold. After constructing multiple Riemannian graphs corresponding to multiple bandpass frequency bands, graph embedding based on bilinear mapping and graph fusion based on mutual information were applied to simultaneously extract the spatial and spectral features of the EEG signals from Riemannian graphs. Furthermore, with a support vector machine (SVM) classifier performed on learned features, we obtained an efficient algorithm, which achieves higher classification performance on various datasets, such as BCI competition IIa and in-house BCI datasets. The proposed methods can also be used in other classification problems with sample data in the form of covariance matrices.

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Shape-restricted support vector machine (SR-SVM): a SVM classifier taking supplementary shape information of input

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-202155 ◽

2021 ◽

Vol 40 (1) ◽

pp. 1481-1494

Author(s):

Geng Deng ◽

Yaoguo Xie ◽

Xindong Wang ◽

Qiang Fu

Keyword(s):

Support Vector Machine ◽

Classification Performance ◽

Research Literature ◽

Support Vector ◽

Svm Classifier ◽

Classification Problems ◽

Active Set ◽

Shape Information ◽

Convex Optimization Problem ◽

Shape Restrictions

Many classification problems contain shape information from input features, such as monotonic, convex, and concave. In this research, we propose a new classifier, called Shape-Restricted Support Vector Machine (SR-SVM), which takes the component-wise shape information to enhance classification accuracy. There exists vast research literature on monotonic classification covering monotonic or ordinal shapes. Our proposed classifier extends to handle convex and concave types of features, and combinations of these types. While standard SVM uses linear separating hyperplanes, our novel SR-SVM essentially constructs non-parametric and nonlinear separating planes subject to component-wise shape restrictions. We formulate SR-SVM classifier as a convex optimization problem and solve it using an active-set algorithm. The approach applies basis function expansions on the input and effectively utilizes the standard SVM solver. We illustrate our methodology using simulation and real world examples, and show that SR-SVM improves the classification performance with additional shape information of input.

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Classification of Motor Imagery EEG Signals with Support Vector Machines and Particle Swarm Optimization

Computational and Mathematical Methods in Medicine ◽

10.1155/2016/4941235 ◽

2016 ◽

Vol 2016 ◽

pp. 1-8 ◽

Cited By ~ 34

Author(s):

Yuliang Ma ◽

Xiaohui Ding ◽

Qingshan She ◽

Zhizeng Luo ◽

Thomas Potter ◽

...

Keyword(s):

Particle Swarm Optimization ◽

Support Vector Machines ◽

Motor Imagery ◽

Particle Swarm ◽

Classification Performance ◽

Support Vector ◽

Eeg Signals ◽

Swarm Optimization ◽

Vector Machines ◽

Selection Of

Support vector machines are powerful tools used to solve the small sample and nonlinear classification problems, but their ultimate classification performance depends heavily upon the selection of appropriate kernel and penalty parameters. In this study, we propose using a particle swarm optimization algorithm to optimize the selection of both the kernel and penalty parameters in order to improve the classification performance of support vector machines. The performance of the optimized classifier was evaluated with motor imagery EEG signals in terms of both classification and prediction. Results show that the optimized classifier can significantly improve the classification accuracy of motor imagery EEG signals.

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A Comprehensive sLORETA Study on the Contribution of Cortical Somatomotor Regions to Motor Imagery

Brain Sciences ◽

10.3390/brainsci9120372 ◽

2019 ◽

Vol 9 (12) ◽

pp. 372

Author(s):

Mustafa Yazici ◽

Mustafa Ulutas ◽

Mukadder Okuyan

Keyword(s):

Motor Imagery ◽

Classification Performance ◽

Support Vector ◽

Problem Solution ◽

Mu Rhythm ◽

Eeg Signals ◽

Common Spatial Pattern ◽

Cortical Sources ◽

Cortical Level ◽

Source Signals

Brain–computer interface (BCI) is a technology used to convert brain signals to control external devices. Researchers have designed and built many interfaces and applications in the last couple of decades. BCI is used for prevention, detection, diagnosis, rehabilitation, and restoration in healthcare. EEG signals are analyzed in this paper to help paralyzed people in rehabilitation. The electroencephalogram (EEG) signals recorded from five healthy subjects are used in this study. The sensor level EEG signals are converted to source signals using the inverse problem solution. Then, the cortical sources are calculated using sLORETA methods at nine regions marked by a neurophysiologist. The features are extracted from cortical sources by using the common spatial pattern (CSP) method and classified by a support vector machine (SVM). Both the sensor and the computed cortical signals corresponding to motor imagery of the hand and foot are used to train the SVM algorithm. Then, the signals outside the training set are used to test the classification performance of the classifier. The 0.1–30 Hz and mu rhythm band-pass filtered activity is also analyzed for the EEG signals. The classification performance and recognition of the imagery improved up to 100% under some conditions for the cortical level. The cortical source signals at the regions contributing to motor commands are investigated and used to improve the classification of motor imagery.

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Developing a Motor Imagery-Based Real-Time Asynchronous Hybrid BCI Controller for a Lower-Limb Exoskeleton

Sensors ◽

10.3390/s20247309 ◽

2020 ◽

Vol 20 (24) ◽

pp. 7309

Author(s):

Junhyuk Choi ◽

Keun Tae Kim ◽

Ji Hyeok Jeong ◽

Laehyun Kim ◽

Song Joo Lee ◽

...

Keyword(s):

Real Time ◽

Motor Imagery ◽

Lower Limb ◽

Neurological Disorders ◽

Support Vector ◽

Svm Classifier ◽

Eeg Signals ◽

Common Spatial Pattern ◽

Lower Limb Exoskeleton ◽

Electroencephalogram Eeg

This study aimed to develop an intuitive gait-related motor imagery (MI)-based hybrid brain-computer interface (BCI) controller for a lower-limb exoskeleton and investigate the feasibility of the controller under a practical scenario including stand-up, gait-forward, and sit-down. A filter bank common spatial pattern (FBCSP) and mutual information-based best individual feature (MIBIF) selection were used in the study to decode MI electroencephalogram (EEG) signals and extract a feature matrix as an input to the support vector machine (SVM) classifier. A successive eye-blink switch was sequentially combined with the EEG decoder in operating the lower-limb exoskeleton. Ten subjects demonstrated more than 80% accuracy in both offline (training) and online. All subjects successfully completed a gait task by wearing the lower-limb exoskeleton through the developed real-time BCI controller. The BCI controller achieved a time ratio of 1.45 compared with a manual smartwatch controller. The developed system can potentially be benefit people with neurological disorders who may have difficulties operating manual control.

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Epileptic seizure detection from EEG signals with phase–amplitude cross-frequency coupling and support vector machine

International Journal of Modern Physics B ◽

10.1142/s0217979218500868 ◽

2018 ◽

Vol 32 (08) ◽

pp. 1850086 ◽

Cited By ~ 3

Author(s):

Yang Liu ◽

Jiang Wang ◽

Lihui Cai ◽

Yingyuan Chen ◽

Yingmei Qin

Keyword(s):

Support Vector Machine ◽

Classification Accuracy ◽

Epileptic Seizures ◽

Classification Performance ◽

Support Vector ◽

Svm Classifier ◽

Eeg Signals ◽

Frequency Coupling ◽

Phase Amplitude ◽

Cross Frequency Coupling

As a pattern of cross-frequency coupling (CFC), phase–amplitude coupling (PAC) depicts the interaction between the phase and amplitude of distinct frequency bands from the same signal, and has been proved to be closely related to the brain’s cognitive and memory activities. This work utilized PAC and support vector machine (SVM) classifier to identify the epileptic seizures from electroencephalogram (EEG) data. The entropy-based modulation index (MI) matrixes are used to express the strength of PAC, from which we extracted features as the input for classifier. Based on the Bonn database, which contains five datasets of EEG segments obtained from healthy volunteers and epileptic subjects, a 100% classification accuracy is achieved for identifying seizure ictal from healthy data, and an accuracy of 97.67% is reached in the classification of ictal EEG signals from inter-ictal EEGs. Based on the CHB–MIT database which is a group of continuously recorded epileptic EEGs by scalp electrodes, a 97.50% classification accuracy is obtained and a raising sign of MI value is found at 6[Formula: see text]s before seizure onset. The classification performance in this work is effective, and PAC can be considered as a useful tool for detecting and predicting the epileptic seizures and providing reference for clinical diagnosis.

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Identification of Anisomerous Motor Imagery EEG Signals Based on Complex Algorithms

Computational Intelligence and Neuroscience ◽

10.1155/2017/2727856 ◽

2017 ◽

Vol 2017 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Rensong Liu ◽

Zhiwen Zhang ◽

Feng Duan ◽

Xin Zhou ◽

Zixuan Meng

Keyword(s):

Motor Imagery ◽

Classification Accuracy ◽

Nearest Neighbor ◽

Recognition System ◽

Classification Performance ◽

Support Vector ◽

Eeg Signals ◽

Accuracy Rate ◽

Common Spatial Pattern ◽

Identification Rate

Motor imagery (MI) electroencephalograph (EEG) signals are widely applied in brain-computer interface (BCI). However, classified MI states are limited, and their classification accuracy rates are low because of the characteristics of nonlinearity and nonstationarity. This study proposes a novel MI pattern recognition system that is based on complex algorithms for classifying MI EEG signals. In electrooculogram (EOG) artifact preprocessing, band-pass filtering is performed to obtain the frequency band of MI-related signals, and then, canonical correlation analysis (CCA) combined with wavelet threshold denoising (WTD) is used for EOG artifact preprocessing. We propose a regularized common spatial pattern (R-CSP) algorithm for EEG feature extraction by incorporating the principle of generic learning. A new classifier combining the K-nearest neighbor (KNN) and support vector machine (SVM) approaches is used to classify four anisomerous states, namely, imaginary movements with the left hand, right foot, and right shoulder and the resting state. The highest classification accuracy rate is 92.5%, and the average classification accuracy rate is 87%. The proposed complex algorithm identification method can significantly improve the identification rate of the minority samples and the overall classification performance.

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Binary Spectrum Feature for Improved Classiﬁer Performance

10.36227/techrxiv.12993122 ◽

2020 ◽

Author(s):

Nalika Ulapane ◽

Karthick Thiyagarajan ◽

sarath kodagoda

Keyword(s):

Machine Learning ◽

Classification Performance ◽

Feature Reduction ◽

Sensor Data ◽

Machine Learning Techniques ◽

Support Vector ◽

Svm Classifier ◽

Monitoring Task ◽

Classifier Performance ◽

Spectrum Feature

<div>Classiﬁcation has become a vital task in modern machine learning and Artiﬁcial Intelligence applications, including smart sensing. Numerous machine learning techniques are available to perform classiﬁcation. Similarly, numerous practices, such as feature selection (i.e., selection of a subset of descriptor variables that optimally describe the output), are available to improve classiﬁer performance. In this paper, we consider the case of a given supervised learning classiﬁcation task that has to be performed making use of continuous-valued features. It is assumed that an optimal subset of features has already been selected. Therefore, no further feature reduction, or feature addition, is to be carried out. Then, we attempt to improve the classiﬁcation performance by passing the given feature set through a transformation that produces a new feature set which we have named the “Binary Spectrum”. Via a case study example done on some Pulsed Eddy Current sensor data captured from an infrastructure monitoring task, we demonstrate how the classiﬁcation accuracy of a Support Vector Machine (SVM) classiﬁer increases through the use of this Binary Spectrum feature, indicating the feature transformation’s potential for broader usage.</div><div><br></div>

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Validating Deep Neural Networks for Online Decoding of Motor Imagery Movements from EEG Signals

Sensors ◽

10.3390/s19010210 ◽

2019 ◽

Vol 19 (1) ◽

pp. 210 ◽

Cited By ~ 32

Author(s):

Zied Tayeb ◽

Juri Fedjaev ◽

Nejla Ghaboosi ◽

Christoph Richter ◽

Lukas Everding ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Convolutional Neural Network ◽

Motor Imagery ◽

Classification Performance ◽

Feature Engineering ◽

Learning Models ◽

Eeg Signals ◽

Learning Methods

Non-invasive, electroencephalography (EEG)-based brain-computer interfaces (BCIs) on motor imagery movements translate the subject’s motor intention into control signals through classifying the EEG patterns caused by different imagination tasks, e.g., hand movements. This type of BCI has been widely studied and used as an alternative mode of communication and environmental control for disabled patients, such as those suffering from a brainstem stroke or a spinal cord injury (SCI). Notwithstanding the success of traditional machine learning methods in classifying EEG signals, these methods still rely on hand-crafted features. The extraction of such features is a difficult task due to the high non-stationarity of EEG signals, which is a major cause by the stagnating progress in classification performance. Remarkable advances in deep learning methods allow end-to-end learning without any feature engineering, which could benefit BCI motor imagery applications. We developed three deep learning models: (1) A long short-term memory (LSTM); (2) a spectrogram-based convolutional neural network model (CNN); and (3) a recurrent convolutional neural network (RCNN), for decoding motor imagery movements directly from raw EEG signals without (any manual) feature engineering. Results were evaluated on our own publicly available, EEG data collected from 20 subjects and on an existing dataset known as 2b EEG dataset from “BCI Competition IV”. Overall, better classification performance was achieved with deep learning models compared to state-of-the art machine learning techniques, which could chart a route ahead for developing new robust techniques for EEG signal decoding. We underpin this point by demonstrating the successful real-time control of a robotic arm using our CNN based BCI.

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Developing Support Vector Machine with New Fuzzy Selection for the Infringement of a Patent Rights Problem

Mathematics ◽

10.3390/math8081263 ◽

2020 ◽

Vol 8 (8) ◽

pp. 1263

Author(s):

Chih-Yao Chang ◽

Kuo-Ping Lin

Keyword(s):

Support Vector Machine ◽

Data Quality ◽

Classification Performance ◽

Patent Infringement ◽

Classification Method ◽

Training Dataset ◽

Support Vector ◽

Classification Problems ◽

Data Quality Management ◽

Patent Rights

Classification problems are very important issues in real enterprises. In the patent infringement issue, accurate classification could help enterprises to understand court decisions to avoid patent infringement. However, the general classification method does not perform well in the patent infringement problem because there are too many complex variables. Therefore, this study attempts to develop a classification method, the support vector machine with new fuzzy selection (SVMFS), to judge the infringement of patent rights. The raw data are divided into training and testing sets. However, the data quality of the training set is not easy to evaluate. Effective data quality management requires a structural core that can support data operations. This study adopts new fuzzy selection based on membership values, which are generated from fuzzy c-means clustering, to select appropriate data to enhance the classification performance of the support vector machine (SVM). An empirical example based on the SVMFS shows that the proposed SVMFS can obtain a superior accuracy rate. Moreover, the new fuzzy selection also verifies that it can effectively select the training dataset.

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Classification of Hyperspectral In Vivo Brain Tissue Based on Linear Unmixing

Applied Sciences ◽

10.3390/app10165686 ◽

2020 ◽

Vol 10 (16) ◽

pp. 5686

Author(s):

Ines A. Cruz-Guerrero ◽

Raquel Leon ◽

Daniel U. Campos-Delgado ◽

Samuel Ortega ◽

Himar Fabelo ◽

...

Keyword(s):

Brain Tissue ◽

Classification Performance ◽

Training Dataset ◽

Support Vector ◽

Svm Classifier ◽

Tissue Classification ◽

Processing Times ◽

Main Challenge ◽

Linear Unmixing

Hyperspectral imaging is a multidimensional optical technique with the potential of providing fast and accurate tissue classification. The main challenge is the adequate processing of the multidimensional information usually linked to long processing times and significant computational costs, which require expensive hardware. In this study, we address the problem of tissue classification for intraoperative hyperspectral images of in vivo brain tissue. For this goal, two methodologies are introduced that rely on a blind linear unmixing (BLU) scheme for practical tissue classification. Both methodologies identify the characteristic end-members related to the studied tissue classes by BLU from a training dataset and classify the pixels by a minimum distance approach. The proposed methodologies are compared with a machine learning method based on a supervised support vector machine (SVM) classifier. The methodologies based on BLU achieve speedup factors of ~459× and ~429× compared to the SVM scheme, while keeping constant and even slightly improving the classification performance.

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