scholarly journals COMPARISONS BETWEEN MOTOR AREA EEG AND ALL-CHANNELS EEG FOR TWO ALGORITHMS IN MOTOR IMAGERY TASK CLASSIFICATION

2014 ◽  
Vol 26 (03) ◽  
pp. 1450040 ◽  
Author(s):  
Siuly ◽  
Yan Li ◽  
Peng Wen
Keyword(s):  
Motor Imagery ◽  
Binary Logistic Regression ◽  
Classification Performance ◽  
Least Square ◽  
Motor Area ◽  
Support Vector ◽  
Imagery Task ◽  
Task Classification ◽  
Svm Algorithm ◽  
The Brain

This article reports on a comparative study to identify electroencephalography (EEG) signals during motor imagery (MI) for motor area EEG and all-channels EEG in the brain–computer interface (BCI) application. In this paper, we present two algorithms: CC-LS-SVM and CC-LR for MI tasks classification. The CC-LS-SVM algorithm combines the cross-correlation (CC) technique and the least square support vector machine (LS-SVM). The CC-LR algorithm assembles the CC technique and binary logistic regression (LR) model. These two algorithms are implemented on the motor area EEG and the all-channels EEG to investigate how well they perform and also to test which area EEG is better for the MI classification. These two algorithms are also compared with some existing methods which reveal their competitive performance during classification. Results on both datasets, IVa and IVb from BCI Competition III, show that the CC-LS-SVM algorithm performs better than the CC-LR algorithm on both the motor area EEG and the all-channels EEG. The results also demonstrate that the CC-LS-SVM algorithm performs much better for the all-channels EEG than for the motor area EEG. Furthermore, the LS-SVM-based approach can correctly identify the discriminative MI tasks, demonstrating the algorithm's superiority in classification performance over some existing methods.

Modeling of Flexible Structures by Means of Least Square Support Vector Machine

2015 ◽  
Author(s):  
Hammam Tamimi ◽  
Dirk Söffker
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Flexible Structures ◽  
Operating Conditions ◽  
Least Square ◽  
Support Vector ◽  
Suitable Model ◽  
Support Vector Machine Algorithm ◽  
Training Time ◽  
Svm Algorithm

This paper investigates modeling of flexible structures by means of the least squares support vector machine (LS-SVM) algorithm. Modeling is the first step to obtain a suitable model-based controller for any given system. Accurate modeling of a flexible structure based on experimental data using LS-SVM algorithm requires less knowledge about the physical system. Least squares support vector machine algorithm can achieve global and unique solution when compared with other soft computing algorithms. Also, LS-SVM algorithm requires less training time. In this paper, the successful use of support vector machine algorithm to model the flexible cantilever is demonstrated. The acquired model is able to provide accurate prediction of the system output under different operating conditions. Experimental results demonstrate the efficiency and high precision of the proposed approach.

scholarly journals Recognition of EEG Signal Motor Imagery Intention Based on Deep Multi-View Feature Learning

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3496
Author(s):  
Jiacan Xu ◽  
Hao Zheng ◽  
Jianhui Wang ◽  
Donglin Li ◽  
Xiaoke Fang
Keyword(s):  
Frequency Domain ◽  
Motor Imagery ◽  
Recognition Performance ◽  
Feature Learning ◽  
Feature Representation ◽  
Support Vector ◽  
Imagery Task ◽  
Learning Method ◽  
Eeg Signals ◽  
Time Frequency

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.

EEG characteristic investigation of the sixth-finger motor imagery and optimal channel selection for classification

2022 ◽  
Author(s):  
Yuan Liu ◽  
Zhuang Wang ◽  
Shuaifei Huang ◽  
Wenjie Wang ◽  
Dong Ming
Keyword(s):  
Frontal Lobe ◽  
Motor Imagery ◽  
Classification Accuracy ◽  
Motor Area ◽  
Support Vector ◽  
Human Hand ◽  
New Paradigm ◽  
Time Frequency ◽  
Spatial Features ◽  
Robotic Devices

Abstract Objective. Supernumerary Robotic Limbs (SRL) are body augmentation robotic devices by adding extra limbs or fingers to the human body different from the traditional wearable robotic devices such as prosthesis and exoskeleton. We proposed a novel MI (Motor imagery)-based BCI paradigm based on the sixth-finger which imagines controlling the extra finger movements. The goal of this work is to investigate the EEG characteristics and the application potential of MI-based BCI systems based on the new imagination paradigm (the sixth finger MI). Approach. 14 subjects participated in the experiment involving the sixth finger MI tasks and rest state. Event-related spectral perturbation (ERSP) was adopted to analyse EEG spatial features and key-channel time-frequency features. Common spatial patterns (CSP) were used for feature extraction and classification was implemented by support vector machine (SVM). A genetic algorithm (GA) was used to select combinations of EEG channels that maximized classification accuracy and verified EEG patterns based on the sixth finger MI. And we conducted a longitudinal 4-week EEG control experiment based on the new paradigm. Main results. ERD (event-related desynchronization) was found in the supplementary motor area (SMA) and primary motor area (M1) with a faint contralateral dominance. Unlike traditional MI based on the human hand, ERD was also found in frontal lobe. GA results showed that the distribution of the optimal 8-channel is similar to EEG topographical distributions, nearing parietal and frontal lobe. And the classification accuracy based on the optimal 8-channel (the highest accuracy of 80% and mean accuracy of 70%) was significantly better than that based on the random 8-channel (p<0.01). Significance. This work provided a new paradigm for MI-based MI system and verified its feasibility, widened the control bandwidth of the BCI system.

Sign in / Sign up

Export Citation Format

Share Document