Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals;these signals can berecorded, processed and classified into different hand movements, which...Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals;these signals can berecorded, processed and classified into different hand movements, which can beused to control other IoT devices. Classification of hand movements will beone step closer to applying these algorithms in real-life situations using EEGheadsets. This paper uses different feature extraction techniques and sophisticatedmachine learning algorithms to classify hand movements from EEG brain signalsto control prosthetic hands for amputated persons. To achieve good classificationaccuracy, denoising and feature extraction of EEG signals is a significant step. Wesaw a considerable increase in all the machine learning models when the movingaverage filter was applied to the raw EEG data. Feature extraction techniques likea fast fourier transform (FFT) and continuous wave transform (CWT) were usedin this study;three types of features were extracted, i.e., FFT Features, CWTCoefficients and CWT scalogram images. We trained and compared differentmachine learning (ML) models like logistic regression, random forest, k-nearestneighbors (KNN), light gradient boosting machine (GBM) and XG boost onFFT and CWT features and deep learning (DL) models like VGG-16, DenseNet201 and ResNet50 trained on CWT scalogram images. XG Boost with FFTfeatures gave the maximum accuracy of 88%.展开更多
Abstract-Two probabilistic methods are extended to research multi-class motor imagery of brain-computer interface （BCI）： support vector machine （SVM） with posteriori probability （PSVM） and Bayesian linear discr...Abstract-Two probabilistic methods are extended to research multi-class motor imagery of brain-computer interface （BCI）： support vector machine （SVM） with posteriori probability （PSVM） and Bayesian linear discriminant analysis with probabilistic output （PBLDA）. A comparative evaluation of these two methods is conducted. The results shows that： 1） probabilistie information can improve the performance of BCI for subjects with high kappa coefficient, and 2） PSVM usually results in a stable kappa coefficient whereas PBLDA is more efficient in estimating the model parameters.展开更多
In recent years,Brain-Computer Interface(BCI)system gained much popularity since it aims at establishing the communication between human brain and computer.BCI systems are applied in several research areas such as neu...In recent years,Brain-Computer Interface(BCI)system gained much popularity since it aims at establishing the communication between human brain and computer.BCI systems are applied in several research areas such as neuro-rehabilitation,robots,exoeskeletons,etc.Electroencephalography(EEG)is a technique commonly applied in capturing brain signals.It is incorporated in BCI systems since it has attractive features such as noninvasive nature,high time-resolution output,mobility and cost-effective.EEG classification process is highly essential in decision making process and it incorporates different processes namely,feature extraction,feature selection,and classification.With this motivation,the current research paper presents an Intelligent Optimal Fuzzy Support Vector Machine-based EEC recognition(IOFSVM-EEG)model for BCI system.Independent Component Analysis(ICA)technique is applied onto the proposed IOFSVM-EEG model to remove the artefacts that exist in EEG signal and to retain the meaningful EEG information.Besides,Common Spatial Pattern(CSP)-based feature extraction technique is utilized to derive a helpful set of feature vectors from the preprocessed EEG signals.Moreover,OFSVM method is applied in the classification of EEG signals,in which the parameters involved in FSVM are optimally tuned using Grasshopper Optimization Algorithm(GOA).In order to validate the enhanced EEG recognition outcomes of the proposed IOFSVM-EEG model,an extensive set of experiments was conducted.The outcomes were examined under distinct aspects.The experimental results highlighted the enhanced performance of the presented IOFSVM-EEG model over other state-of-the-art methods.展开更多
Mental task classification is one of the most important problems in Brain-computer interface.This paper studies the classification of five-class mental tasks.The nonlinear parameter of mean period obtained from freque...Mental task classification is one of the most important problems in Brain-computer interface.This paper studies the classification of five-class mental tasks.The nonlinear parameter of mean period obtained from frequency domain information was used as features for classification implemented by using the method of SVM(support vector machines).The averaged classification accuracy of 85.6% over 7 subjects was achieved for 2-second EEG segments.And the results for EEG segments of 0.5s and 5.0s compared favorably to those of Garrett's.The results indicate that the parameter of mean period represents mental tasks well for classification.Furthermore,the method of mean period is less computationally demanding,which indicates its potential use for online BCI systems.展开更多
本文提出一种跨被试的深度神经网络识别方法,应对运动想象脑电信号的非线性、非平稳特性.该方法首先计算协方差矩阵均值,将不同被试者样本集的协方差对齐至单位矩阵,提升样本的被试间泛化性.然后,将对齐后的样本输入至卷积神经网络中,...本文提出一种跨被试的深度神经网络识别方法,应对运动想象脑电信号的非线性、非平稳特性.该方法首先计算协方差矩阵均值,将不同被试者样本集的协方差对齐至单位矩阵,提升样本的被试间泛化性.然后,将对齐后的样本输入至卷积神经网络中,通过留一被试交叉验证法,构建跨被试的运动想象脑电信号识别方法.在BCI Competition IV dataset 2b公开数据集上进行实验,结果表明,新的方法在该数据集上取得了高的识别性能,且测试场景中的时间复杂度与现有方法相同.展开更多
Brain-machine interfaces (BMIs) translate neural activities of the brain into specific instructions that can be carried out by external devices. BMIs have the potential to restore or augment motor functions of paralyz...Brain-machine interfaces (BMIs) translate neural activities of the brain into specific instructions that can be carried out by external devices. BMIs have the potential to restore or augment motor functions of paralyzed patients suffering from spinal cord damage. The neural activities have been used to predict the 2D or 3D movement trajectory of monkey's arm or hand in many studies. However, there are few studies on decoding the wrist movement from neural activities in center-out paradigm. The present study developed an invasive BMI system with a monkey model using a 10×10-microelectrode array in the primary motor cortex. The monkey was trained to perform a two-dimensional forelimb wrist movement paradigm where neural activities and movement signals were simultaneous recorded. Results showed that neuronal firing rates highly correlated with forelimb wrist movement; > 70% (105/149) neurons exhibited specific firing changes during movement and > 36% (54/149) neurons were used to discriminate directional pairs. The neuronal firing rates were also used to predict the wrist moving directions and continuous trajectories of the forelimb wrist. The four directions could be classified with 96% accuracy using a support vector machine, and the correlation coefficients of trajectory prediction using a general regression neural network were above 0.8 for both horizontal and vertical directions. Results showed that this BMI system could predict monkey wrist movements in high accuracy through the use of neuronal firing information.展开更多
In this paper, robust MPC (Model Predictive Control) with adaptive DA converter method for the wheelchair using EEG (Electroencephalogram) based BMI (Brain Machine Interface) is discussed. The method is developed to a...In this paper, robust MPC (Model Predictive Control) with adaptive DA converter method for the wheelchair using EEG (Electroencephalogram) based BMI (Brain Machine Interface) is discussed. The method is developed to apply to the obstacle avoidance system of wheelchair. This paper is the 1st stage for the development of the BMI based wheelchair in practical use. The robust MPC method is realized by using the minimax optimization with bounded constraint conditions. Some numerical examples are also included to demonstrate the effectiveness of the proposed methodas the former stage of the real experiments.展开更多
Brain-computer interface (BCI) is a communication system that can help lock-in patients to interact with the outside environment by translating brain signals into machine commands.The present work provides a design fo...Brain-computer interface (BCI) is a communication system that can help lock-in patients to interact with the outside environment by translating brain signals into machine commands.The present work provides a design for a virtual reality (VR) based BCI system that allows human participants to control a virtual hand to make gestures by P300 signals,with a positive peak of potential about 300 ms posterior to the onset of target stimulus.In this virtual environment,the participants can obtain a more immersed experience with the BCI system,such as controlling a virtual hand or walking around in the virtual world.Methods of modeling the virtual hand and analyzing the P300 signals are also described in detail.Template matching and support vector machine were used as the P300 classifier and the experiment results showed that both algorithms perform well in the system.After a short time of practice,most participants could learn to control the virtual hand during the online experiment with greater than 70% accuracy.展开更多
文摘Brain-computer interfaces (BCIs) records brain activity using electroencephalogram (EEG) headsets in the form of EEG signals;these signals can berecorded, processed and classified into different hand movements, which can beused to control other IoT devices. Classification of hand movements will beone step closer to applying these algorithms in real-life situations using EEGheadsets. This paper uses different feature extraction techniques and sophisticatedmachine learning algorithms to classify hand movements from EEG brain signalsto control prosthetic hands for amputated persons. To achieve good classificationaccuracy, denoising and feature extraction of EEG signals is a significant step. Wesaw a considerable increase in all the machine learning models when the movingaverage filter was applied to the raw EEG data. Feature extraction techniques likea fast fourier transform (FFT) and continuous wave transform (CWT) were usedin this study;three types of features were extracted, i.e., FFT Features, CWTCoefficients and CWT scalogram images. We trained and compared differentmachine learning (ML) models like logistic regression, random forest, k-nearestneighbors (KNN), light gradient boosting machine (GBM) and XG boost onFFT and CWT features and deep learning (DL) models like VGG-16, DenseNet201 and ResNet50 trained on CWT scalogram images. XG Boost with FFTfeatures gave the maximum accuracy of 88%.
基金supported by the National Natural Science Foundation of China under Grant No. 30525030, 60701015, and 60736029.
文摘Abstract-Two probabilistic methods are extended to research multi-class motor imagery of brain-computer interface （BCI）： support vector machine （SVM） with posteriori probability （PSVM） and Bayesian linear discriminant analysis with probabilistic output （PBLDA）. A comparative evaluation of these two methods is conducted. The results shows that： 1） probabilistie information can improve the performance of BCI for subjects with high kappa coefficient, and 2） PSVM usually results in a stable kappa coefficient whereas PBLDA is more efficient in estimating the model parameters.
文摘In recent years,Brain-Computer Interface(BCI)system gained much popularity since it aims at establishing the communication between human brain and computer.BCI systems are applied in several research areas such as neuro-rehabilitation,robots,exoeskeletons,etc.Electroencephalography(EEG)is a technique commonly applied in capturing brain signals.It is incorporated in BCI systems since it has attractive features such as noninvasive nature,high time-resolution output,mobility and cost-effective.EEG classification process is highly essential in decision making process and it incorporates different processes namely,feature extraction,feature selection,and classification.With this motivation,the current research paper presents an Intelligent Optimal Fuzzy Support Vector Machine-based EEC recognition(IOFSVM-EEG)model for BCI system.Independent Component Analysis(ICA)technique is applied onto the proposed IOFSVM-EEG model to remove the artefacts that exist in EEG signal and to retain the meaningful EEG information.Besides,Common Spatial Pattern(CSP)-based feature extraction technique is utilized to derive a helpful set of feature vectors from the preprocessed EEG signals.Moreover,OFSVM method is applied in the classification of EEG signals,in which the parameters involved in FSVM are optimally tuned using Grasshopper Optimization Algorithm(GOA).In order to validate the enhanced EEG recognition outcomes of the proposed IOFSVM-EEG model,an extensive set of experiments was conducted.The outcomes were examined under distinct aspects.The experimental results highlighted the enhanced performance of the presented IOFSVM-EEG model over other state-of-the-art methods.
基金This work was supportedin part by the National Natural Science Foundation of China(No.60271025,No.30370395)in part by the Science and Technology Depart ment of Shaanxi Province(No.2003K10-G24).
文摘Mental task classification is one of the most important problems in Brain-computer interface.This paper studies the classification of five-class mental tasks.The nonlinear parameter of mean period obtained from frequency domain information was used as features for classification implemented by using the method of SVM(support vector machines).The averaged classification accuracy of 85.6% over 7 subjects was achieved for 2-second EEG segments.And the results for EEG segments of 0.5s and 5.0s compared favorably to those of Garrett's.The results indicate that the parameter of mean period represents mental tasks well for classification.Furthermore,the method of mean period is less computationally demanding,which indicates its potential use for online BCI systems.
文摘本文提出一种跨被试的深度神经网络识别方法,应对运动想象脑电信号的非线性、非平稳特性.该方法首先计算协方差矩阵均值,将不同被试者样本集的协方差对齐至单位矩阵,提升样本的被试间泛化性.然后,将对齐后的样本输入至卷积神经网络中,通过留一被试交叉验证法,构建跨被试的运动想象脑电信号识别方法.在BCI Competition IV dataset 2b公开数据集上进行实验,结果表明,新的方法在该数据集上取得了高的识别性能,且测试场景中的时间复杂度与现有方法相同.
基金supported by the National Natural Science Foundation of China (61031002, 61001172)the National Basic Research Program of China (2011CB504405)the Zhejiang Provincial Key Science and Technology Program for International Cooperation (2011C14005)
文摘Brain-machine interfaces (BMIs) translate neural activities of the brain into specific instructions that can be carried out by external devices. BMIs have the potential to restore or augment motor functions of paralyzed patients suffering from spinal cord damage. The neural activities have been used to predict the 2D or 3D movement trajectory of monkey's arm or hand in many studies. However, there are few studies on decoding the wrist movement from neural activities in center-out paradigm. The present study developed an invasive BMI system with a monkey model using a 10×10-microelectrode array in the primary motor cortex. The monkey was trained to perform a two-dimensional forelimb wrist movement paradigm where neural activities and movement signals were simultaneous recorded. Results showed that neuronal firing rates highly correlated with forelimb wrist movement; > 70% (105/149) neurons exhibited specific firing changes during movement and > 36% (54/149) neurons were used to discriminate directional pairs. The neuronal firing rates were also used to predict the wrist moving directions and continuous trajectories of the forelimb wrist. The four directions could be classified with 96% accuracy using a support vector machine, and the correlation coefficients of trajectory prediction using a general regression neural network were above 0.8 for both horizontal and vertical directions. Results showed that this BMI system could predict monkey wrist movements in high accuracy through the use of neuronal firing information.
文摘In this paper, robust MPC (Model Predictive Control) with adaptive DA converter method for the wheelchair using EEG (Electroencephalogram) based BMI (Brain Machine Interface) is discussed. The method is developed to apply to the obstacle avoidance system of wheelchair. This paper is the 1st stage for the development of the BMI based wheelchair in practical use. The robust MPC method is realized by using the minimax optimization with bounded constraint conditions. Some numerical examples are also included to demonstrate the effectiveness of the proposed methodas the former stage of the real experiments.
基金Project supported by the National Natural Science Foundation of China (No.60873125)the National Institute of Biomedical Imaging and Bioengineering (No.1R03EB008235-01A1)+1 种基金the Shanghai Commission of Science and Technology (No.10440710200)the Fundamental Research Funds for the Central Universities
文摘Brain-computer interface (BCI) is a communication system that can help lock-in patients to interact with the outside environment by translating brain signals into machine commands.The present work provides a design for a virtual reality (VR) based BCI system that allows human participants to control a virtual hand to make gestures by P300 signals,with a positive peak of potential about 300 ms posterior to the onset of target stimulus.In this virtual environment,the participants can obtain a more immersed experience with the BCI system,such as controlling a virtual hand or walking around in the virtual world.Methods of modeling the virtual hand and analyzing the P300 signals are also described in detail.Template matching and support vector machine were used as the P300 classifier and the experiment results showed that both algorithms perform well in the system.After a short time of practice,most participants could learn to control the virtual hand during the online experiment with greater than 70% accuracy.
