scholarly journals Task Transfer Learning for EEG Classification in Motor Imagery-Based BCI System

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xuanci Zheng ◽  
Jie Li ◽  
Hongfei Ji ◽  
Lili Duan ◽  
Maozhen Li ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Motor Imagery ◽  
Spatial Patterns ◽  
Training Model ◽  
Average Accuracy ◽  
Source Data ◽  
Task Transfer ◽  
Calibration Time ◽  
And Training ◽  
Bci System

The motor-imagery brain-computer interface system (MI-BCI) has a board prospect for development. However, long calibration time and lack of enough MI commands limit its use in practice. In order to enlarge the command set, we add the combinations of traditional MI commands as new commands into the command set. We also design an algorithm based on transfer learning so as to decrease the calibration time for collecting EEG signal and training model. We create feature extractor based on data from traditional commands and transfer patterns through the data from new commands. Through the comparison of the average accuracy between our algorithm and traditional algorithms and the visualization of spatial patterns in our algorithm, we find that the accuracy of our algorithm is much higher than traditional algorithms, especially as for the low-quality datasets. Besides, the visualization of spatial patterns is meaningful. The algorithm based on transfer learning takes the advantage of the information from source data. We enlarge the command set while shortening the calibration time, which is of significant importance to the MI-BCI application.

scholarly journals Across-subject offline decoding of motor imagery from MEG and EEG

2018 ◽  
Author(s):  
Hanna-Leena Halme ◽  
Lauri Parkkonen
Keyword(s):  
Logistic Regression ◽  
Motor Imagery ◽  
Spatial Patterns ◽  
Computer Interfaces ◽  
The Common ◽  
Calibration Time ◽  
Decoding Accuracy ◽  
And Training ◽  
Healthy Participants ◽  
Passive Movements

AbstractLong calibration time hinders the feasibility of brain-computer interfaces (BCI). If other subjects’ data were used for training the classifier, BCI-based neurofeedback practice could start without the initial calibration. Here, we compare methods for inter-subject decoding of left- vs. right-hand motor imagery (MI) from MEG and EEG.Six methods were tested on data involving MEG and EEG measurements of healthy participants. Only subjects with good within-subject accuracies were selected for inter-subject decoding. Three methods were based on the Common Spatial Patterns (CSP) algorithm, and three others on logistic regression with l1 - or l2,1 -norm regularization. The decoding accuracy was evaluated using 1) MI and 2) passive movements (PM) for training, separately for MEG and EEG.When the classifier was trained by MI, the best accuracies across subjects (mean 70.6% for MEG, 67.7% for EEG) were obtained using multi-task learning (MTL) with logistic regression and l2,1-norm regularization. MEG yielded slightly better average accuracies than EEG. When PM were used for training, none of the inter-subject methods yielded above chance level (58.7%) accuracy.In conclusion, MTL and training with other subject’s MI is efficient for inter-subject decoding of MI. Passive movements of other subjects are likely suboptimal for training the MI classifiers.

scholarly journals A transfer learning framework based on motor imagery rehabilitation for stroke

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Fangzhou Xu ◽  
Yunjing Miao ◽  
Yanan Sun ◽  
Dongju Guo ◽  
Jiali Xu ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Motor Imagery ◽  
Stroke Rehabilitation ◽  
Transfer Functions ◽  
Model Parameters ◽  
Transfer Model ◽  
Learning Networks ◽  
Training Time ◽  
Fine Tune ◽  
Bci System

AbstractDeep learning networks have been successfully applied to transfer functions so that the models can be adapted from the source domain to different target domains. This study uses multiple convolutional neural networks to decode the electroencephalogram (EEG) of stroke patients to design effective motor imagery (MI) brain-computer interface (BCI) system. This study has introduced ‘fine-tune’ to transfer model parameters and reduced training time. The performance of the proposed framework is evaluated by the abilities of the models for two-class MI recognition. The results show that the best framework is the combination of the EEGNet and ‘fine-tune’ transferred model. The average classification accuracy of the proposed model for 11 subjects is 66.36%, and the algorithm complexity is much lower than other models.These good performance indicate that the EEGNet model has great potential for MI stroke rehabilitation based on BCI system. It also successfully demonstrated the efficiency of transfer learning for improving the performance of EEG-based stroke rehabilitation for the BCI system.

scholarly journals Local Temporal Correlation Common Spatial Patterns for Single Trial EEG Classification during Motor Imagery

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Rui Zhang ◽  
Peng Xu ◽  
Tiejun Liu ◽  
Yangsong Zhang ◽  
Lanjin Guo ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Spatial Patterns ◽  
Euclidean Distance ◽  
Temporal Correlation ◽  
Extraction Methods ◽  
Covariance Matrices ◽  
Computer Interface ◽  
Single Trial ◽  
Common Spatial Pattern ◽  
Average Accuracy

Common spatial pattern (CSP) is one of the most popular and effective feature extraction methods for motor imagery-based brain-computer interface (BCI), but the inherent drawback of CSP is that the estimation of the covariance matrices is sensitive to noise. In this work, local temporal correlation (LTC) information was introduced to further improve the covariance matrices estimation (LTCCSP). Compared to the Euclidean distance used in a previous CSP variant named local temporal CSP (LTCSP), the correlation may be a more reasonable metric to measure the similarity of activated spatial patterns existing in motor imagery period. Numerical comparisons among CSP, LTCSP, and LTCCSP were quantitatively conducted on the simulated datasets by adding outliers to Dataset IVa of BCI Competition III and Dataset IIa of BCI Competition IV, respectively. Results showed that LTCCSP achieves the highest average classification accuracies in all the outliers occurrence frequencies. The application of the three methods to the EEG dataset recorded in our laboratory also demonstrated that LTCCSP achieves the highest average accuracy. The above results consistently indicate that LTCCSP would be a promising method for practical motor imagery BCI application.

A 1D CNN for high accuracy classification and transfer learning in motor imagery EEG-based brain-computer interface

2021 ◽  
Author(s):  
Francesco Mattioli ◽  
Camillo Porcaro ◽  
Gianluca Baldassarre
Keyword(s):  
Transfer Learning ◽  
Motor Imagery ◽  
Data Augmentation ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Single Individual ◽  
Related Data ◽  
Motor Movement ◽  
Average Accuracy ◽  
Eeg Recordings

Abstract Objective: Brain-computer interface (BCI) aims to establish communication paths between the brain processes and external devices. Different methods have been used to extract human intentions from electroencephalography (EEG) recordings. Those based on motor imagery (MI) seem to have a great potential for future applications. These approaches rely on the extraction of EEG distinctive patterns during imagined movements. Techniques able to extract patterns from raw signals represent an important target for BCI as they do not need labor-intensive data pre-processing. Approach: We propose a new approach based on a 10-layer one-dimensional convolution neural network (1D-CNN) to classify five brain states (four MI classes plus a ‘baseline’ class) using a data augmentation algorithm and a limited number of EEG channels. In addition, we present a transfer learning method used to extract critical features from the EEG group dataset and then to customize the model to the single individual by training its outer layers with only 12-minute individual-related data. Main results: The model tested with the ‘EEG Motor Movement/Imagery Dataset’ outperforms the current state-of-the-art models by achieving a 99.38% accuracy at the group level. In addition, the transfer learning approach we present achieves an average accuracy of 99.46%. Significance: The proposed methods could foster future BCI applications relying on few-channel portable recording devices and individual-based training.

Transfer Learning Based on Regularized Common Spatial Patterns Using Cosine Similarities of Spatial Filters for Motor-Imagery BCI

2019 ◽  
Vol 28 (07) ◽  
pp. 1950123 ◽  
Author(s):  
Yilu Xu ◽  
Qingguo Wei ◽  
Hua Zhang ◽  
Ronghua Hu ◽  
Jizhong Liu ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Motor Imagery ◽  
Spatial Patterns ◽  
Covariance Matrices ◽  
Training Data ◽  
Spatial Filters ◽  
Data Set ◽  
Common Spatial Patterns ◽  
Public Data ◽  
Target Subject

In motor-imagery brain–computer interface (BCI), transfer learning based on the framework of regularized common spatial patterns (RCSP) can make full use of the training data derived from other subjects to reduce calibration time for a new subject. Covariance matrices are commonly used to estimate the difference between subjects. However, the classification performances vary greatly depending on different assumptions of the distribution of covariance matrices. Therefore, to directly observe the variations of the target subject’s features after transferring, we neglect the distribution of covariance matrices and instead compare cosine similarities of spatial filters between the target subject and the composite subject whose data comes from the target subject and a source subject. Two RCSP algorithms based on cosine measure are proposed to use the samples of all source subjects and most useful source subjects, respectively. Experiments on one public data set from BCI competition show that our proposed approaches significantly improve the classification performances compared to the conventional CSP algorithm in almost every case, based on a small training set.

scholarly journals Classification of jujube defects in small data sets based on transfer learning

2021 ◽  
Author(s):  
Jianping Ju ◽  
Hong Zheng ◽  
Xiaohang Xu ◽  
Zhongyuan Guo ◽  
Zhaohui Zheng ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Loss Function ◽  
Training Model ◽  
Parameter Distribution ◽  
Test Accuracy ◽  
Small Data ◽  
Data Sets ◽  
Data Set ◽  
Small Data Sets

AbstractAlthough convolutional neural networks have achieved success in the field of image classification, there are still challenges in the field of agricultural product quality sorting such as machine vision-based jujube defects detection. The performance of jujube defect detection mainly depends on the feature extraction and the classifier used. Due to the diversity of the jujube materials and the variability of the testing environment, the traditional method of manually extracting the features often fails to meet the requirements of practical application. In this paper, a jujube sorting model in small data sets based on convolutional neural network and transfer learning is proposed to meet the actual demand of jujube defects detection. Firstly, the original images collected from the actual jujube sorting production line were pre-processed, and the data were augmented to establish a data set of five categories of jujube defects. The original CNN model is then improved by embedding the SE module and using the triplet loss function and the center loss function to replace the softmax loss function. Finally, the depth pre-training model on the ImageNet image data set was used to conduct training on the jujube defects data set, so that the parameters of the pre-training model could fit the parameter distribution of the jujube defects image, and the parameter distribution was transferred to the jujube defects data set to complete the transfer of the model and realize the detection and classification of the jujube defects. The classification results are visualized by heatmap through the analysis of classification accuracy and confusion matrix compared with the comparison models. The experimental results show that the SE-ResNet50-CL model optimizes the fine-grained classification problem of jujube defect recognition, and the test accuracy reaches 94.15%. The model has good stability and high recognition accuracy in complex environments.

scholarly journals The MindGomoku: An Online P300 BCI Game Based on Bayesian Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1613
Author(s):  
Man Li ◽  
Feng Li ◽  
Jiahui Pan ◽  
Dengyong Zhang ◽  
Suna Zhao ◽  
...  
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
System Control ◽  
Average Accuracy ◽  
Training Samples ◽  
The Disabled ◽  
Limited Training Samples ◽  
The Stability ◽  
Electroencephalogram Eeg ◽  
Bci System

In addition to helping develop products that aid the disabled, brain–computer interface (BCI) technology can also become a modality of entertainment for all people. However, most BCI games cannot be widely promoted due to the poor control performance or because they easily cause fatigue. In this paper, we propose a P300 brain–computer-interface game (MindGomoku) to explore a feasible and natural way to play games by using electroencephalogram (EEG) signals in a practical environment. The novelty of this research is reflected in integrating the characteristics of game rules and the BCI system when designing BCI games and paradigms. Moreover, a simplified Bayesian convolutional neural network (SBCNN) algorithm is introduced to achieve high accuracy on limited training samples. To prove the reliability of the proposed algorithm and system control, 10 subjects were selected to participate in two online control experiments. The experimental results showed that all subjects successfully completed the game control with an average accuracy of 90.7% and played the MindGomoku an average of more than 11 min. These findings fully demonstrate the stability and effectiveness of the proposed system. This BCI system not only provides a form of entertainment for users, particularly the disabled, but also provides more possibilities for games.

scholarly journals Precise Identification of Prostate Cancer from DWI Using Transfer Learning

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3664
Author(s):  
Islam R. Abdelmaksoud ◽  
Ahmed Shalaby ◽  
Ali Mahmoud ◽  
Mohammed Elmogy ◽  
Ahmed Aboelfetouh ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Transfer Learning ◽  
Sensitivity And Specificity ◽  
Benign Tumors ◽  
B Values ◽  
Cad System ◽  
Average Accuracy ◽  
Apparent Diffusion ◽  
Invasive Techniques ◽  
Fine Tune

Background and Objective: The use of computer-aided detection (CAD) systems can help radiologists make objective decisions and reduce the dependence on invasive techniques. In this study, a CAD system that detects and identifies prostate cancer from diffusion-weighted imaging (DWI) is developed. Methods: The proposed system first uses non-negative matrix factorization (NMF) to integrate three different types of features for the accurate segmentation of prostate regions. Then, discriminatory features in the form of apparent diffusion coefficient (ADC) volumes are estimated from the segmented regions. The ADC maps that constitute these volumes are labeled by a radiologist to identify the ADC maps with malignant or benign tumors. Finally, transfer learning is used to fine-tune two different previously-trained convolutional neural network (CNN) models (AlexNet and VGGNet) for detecting and identifying prostate cancer. Results: Multiple experiments were conducted to evaluate the accuracy of different CNN models using DWI datasets acquired at nine distinct b-values that included both high and low b-values. The average accuracy of AlexNet at the nine b-values was 89.2±1.5% with average sensitivity and specificity of 87.5±2.3% and 90.9±1.9%. These results improved with the use of the deeper CNN model (VGGNet). The average accuracy of VGGNet was 91.2±1.3% with sensitivity and specificity of 91.7±1.7% and 90.1±2.8%. Conclusions: The results of the conducted experiments emphasize the feasibility and accuracy of the developed system and the improvement of this accuracy using the deeper CNN.

scholarly journals Learning Common Time-Frequency-Spatial Patterns for Motor Imagery Classification

2021 ◽  
pp. 1-1
Author(s):  
Yangyang Miao ◽  
Jing Jin ◽  
Ian Daly ◽  
Cili Zuo ◽  
Xingyu Wang ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Spatial Patterns ◽  
Time Frequency ◽  
Common Time
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