In electroencephalogram (EEG) modeling techniques, data segment selection is the first and still an important step. The influence of a set of data-segment-related parameters on feature extraction and classification in...In electroencephalogram (EEG) modeling techniques, data segment selection is the first and still an important step. The influence of a set of data-segment-related parameters on feature extraction and classification in an EEG-based brain-computer interface (BCI) was studied. An auto search algorithm was developed to study four datasegment-related parameters in each trial of 12 subjects’ EEG. The length of data segment (LDS), the start position of data (SPD) segment, AR order, and number of trials (NT) were used to build the model. The study showed that, compared with the classification ratio (CR) without parameter selection, the CR was increased by 20% to 30% with proper selection of these data-segment-related parameters, and the optimum parameter values were subject-dependent. This suggests that the data-segment-related parameters should be individualized when building models for BCI.展开更多
Physiological signals such as electroencephalogram(EEG)signals are often corrupted by artifacts during the acquisition and processing.Some of these artifacts may deteriorate the essential properties of the signal that...Physiological signals such as electroencephalogram(EEG)signals are often corrupted by artifacts during the acquisition and processing.Some of these artifacts may deteriorate the essential properties of the signal that pertains to meaningful information.Most of these artifacts occur due to the involuntary movements or actions the human does during the acquisition process.So,it is recommended to eliminate these artifacts with signal processing approaches.This paper presents two mechanisms of classification and elimination of artifacts.In the first step,a customized deep network is employed to classify clean EEG signals and artifact-included signals.The classification is performed at the feature level,where common space pattern features are extracted with convolutional layers,and these features are later classified with a support vector machine classifier.In the second stage of the work,the artifact signals are decomposed with empirical mode decomposition,and they are then eliminated with the proposed adaptive thresholding mechanism where the threshold value changes for every intrinsic mode decomposition in the iterative mechanism.展开更多
Purpose-Recently,the convolutional neural network(ConvNet)has a wide application in the classification of motor imagery EEG signals,However,the low sigalto-noise electroencephalogram(EEG)signals are ollectede under th...Purpose-Recently,the convolutional neural network(ConvNet)has a wide application in the classification of motor imagery EEG signals,However,the low sigalto-noise electroencephalogram(EEG)signals are ollectede under the interference of noises.However,the conventional ConvNet model cannot directly solve this problem.This study aims to discuss the aforementioned issues.Design/methodology/approach-To solve this problem,this paper adopted a novel residual shrinkage block(RSB)to construct the ComvNet model(RSBConvNet).During the feature extraction from EEG simnals,the proposed RSBConvNet prevented the noise component in EEG signals,and improved the classification accuracy of motor imagery.In the construction of RSBConvNet,the author applied the soft thresholding strategy to prevent the non-related.motor imagery features in EEG sigmals.The soft thresholding was inserted into the residual block(RB),and the suitable threshold for the curent EEG signals distribution can be learned by minimizing the loss function.Therefore,during the feature extraction of motor imagery,the proposed RSBConvNet de noised the EEG signals and improved the discriminative of dassifiation features.Findings-Comparative experiments and ablation studies were done on two public benchumark datasets.Compared with conventionalConvNet models,the proposed RSBConvNet model has olbvious improvements in motor imagery classification accuracy and Kappa officient.Ablation studies have also shown the de noised abilities of the RSBConvNet modeL Morbover,different parameters and computational methods of the RSBConvNet model have been tested om the dassificatiton of motor imagery.Originality/value-Based ou the experimental results,the RSBComvNet constructed in this paper has an excellent reogmition accuracy of M-BCI which can be used for further appications for the online MI-BCI.展开更多
文摘In electroencephalogram (EEG) modeling techniques, data segment selection is the first and still an important step. The influence of a set of data-segment-related parameters on feature extraction and classification in an EEG-based brain-computer interface (BCI) was studied. An auto search algorithm was developed to study four datasegment-related parameters in each trial of 12 subjects’ EEG. The length of data segment (LDS), the start position of data (SPD) segment, AR order, and number of trials (NT) were used to build the model. The study showed that, compared with the classification ratio (CR) without parameter selection, the CR was increased by 20% to 30% with proper selection of these data-segment-related parameters, and the optimum parameter values were subject-dependent. This suggests that the data-segment-related parameters should be individualized when building models for BCI.
文摘Physiological signals such as electroencephalogram(EEG)signals are often corrupted by artifacts during the acquisition and processing.Some of these artifacts may deteriorate the essential properties of the signal that pertains to meaningful information.Most of these artifacts occur due to the involuntary movements or actions the human does during the acquisition process.So,it is recommended to eliminate these artifacts with signal processing approaches.This paper presents two mechanisms of classification and elimination of artifacts.In the first step,a customized deep network is employed to classify clean EEG signals and artifact-included signals.The classification is performed at the feature level,where common space pattern features are extracted with convolutional layers,and these features are later classified with a support vector machine classifier.In the second stage of the work,the artifact signals are decomposed with empirical mode decomposition,and they are then eliminated with the proposed adaptive thresholding mechanism where the threshold value changes for every intrinsic mode decomposition in the iterative mechanism.
基金This work was supported by the Education and Scientific Research Project for Young and Middle-aged Teachers in Fujian Province(No.JAT200581)。
文摘Purpose-Recently,the convolutional neural network(ConvNet)has a wide application in the classification of motor imagery EEG signals,However,the low sigalto-noise electroencephalogram(EEG)signals are ollectede under the interference of noises.However,the conventional ConvNet model cannot directly solve this problem.This study aims to discuss the aforementioned issues.Design/methodology/approach-To solve this problem,this paper adopted a novel residual shrinkage block(RSB)to construct the ComvNet model(RSBConvNet).During the feature extraction from EEG simnals,the proposed RSBConvNet prevented the noise component in EEG signals,and improved the classification accuracy of motor imagery.In the construction of RSBConvNet,the author applied the soft thresholding strategy to prevent the non-related.motor imagery features in EEG sigmals.The soft thresholding was inserted into the residual block(RB),and the suitable threshold for the curent EEG signals distribution can be learned by minimizing the loss function.Therefore,during the feature extraction of motor imagery,the proposed RSBConvNet de noised the EEG signals and improved the discriminative of dassifiation features.Findings-Comparative experiments and ablation studies were done on two public benchumark datasets.Compared with conventionalConvNet models,the proposed RSBConvNet model has olbvious improvements in motor imagery classification accuracy and Kappa officient.Ablation studies have also shown the de noised abilities of the RSBConvNet modeL Morbover,different parameters and computational methods of the RSBConvNet model have been tested om the dassificatiton of motor imagery.Originality/value-Based ou the experimental results,the RSBComvNet constructed in this paper has an excellent reogmition accuracy of M-BCI which can be used for further appications for the online MI-BCI.
