Identification of EEG features in stroke patients based on common spatial pattern and sparse representation classification

2017 ◽  
Author(s):  
Xuefeng Lei ◽  
Luyun Wang ◽  
Wanzeng Kong ◽  
Yong Peng ◽  
Sanqing Hu ◽  
...  
Keyword(s):  
Spatial Pattern ◽  
Sparse Representation ◽  
Stroke Patients ◽  
Common Spatial Pattern ◽  
Sparse Representation Classification ◽  
Eeg Features

scholarly journals Motor Imagery-Based Brain-Computer Interface Coupled to a Robotic Hand Orthosis Aimed for Neurorehabilitation of Stroke Patients

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Jessica Cantillo-Negrete ◽  
Ruben I. Carino-Escobar ◽  
Paul Carrillo-Mora ◽  
David Elias-Vinas ◽  
Josefina Gutierrez-Martinez
Keyword(s):  
Spatial Pattern ◽  
Motor Imagery ◽  
Healthy Subjects ◽  
Robotic Hand ◽  
Hand Rehabilitation ◽  
Stroke Patients ◽  
Common Spatial Pattern ◽  
Processing Stage ◽  
The Common ◽  
Low Performance

Motor imagery-based brain-computer interfaces (BCI) have shown potential for the rehabilitation of stroke patients; however, low performance has restricted their application in clinical environments. Therefore, this work presents the implementation of a BCI system, coupled to a robotic hand orthosis and driven by hand motor imagery of healthy subjects and the paralysed hand of stroke patients. A novel processing stage was designed using a bank of temporal filters, the common spatial pattern algorithm for feature extraction and particle swarm optimisation for feature selection. Offline tests were performed for testing the proposed processing stage, and results were compared with those computed with common spatial patterns. Afterwards, online tests with healthy subjects were performed in which the orthosis was activated by the system. Stroke patients’ average performance was 74.1 ± 11%. For 4 out of 6 patients, the proposed method showed a statistically significant higher performance than the common spatial pattern method. Healthy subjects’ average offline and online performances were of 76.2 ± 7.6% and 70 ± 6.7, respectively. For 3 out of 8 healthy subjects, the proposed method showed a statistically significant higher performance than the common spatial pattern method. System’s performance showed that it has a potential to be used for hand rehabilitation of stroke patients.

scholarly journals Gesture recognition based on an improved local sparse representation classification algorithm

2017 ◽  
Vol 22 (S5) ◽  
pp. 10935-10946 ◽  
Author(s):  
Yang He ◽  
Gongfa Li ◽  
Yajie Liao ◽  
Ying Sun ◽  
Jianyi Kong ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Gesture Recognition ◽  
Classification Algorithm ◽  
Sparse Representation Classification ◽  
Local Sparse Representation

Integrating Sparse and Collaborative Representation Classifications for Image Classification

2017 ◽  
Vol 17 (02) ◽  
pp. 1750007 ◽  
Author(s):  
Chunwei Tian ◽  
Guanglu Sun ◽  
Qi Zhang ◽  
Weibing Wang ◽  
Teng Chen ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Image Classification ◽  
Linear Combination ◽  
Image Recognition ◽  
Test Sample ◽  
Collaborative Representation ◽  
Training Samples ◽  
Sparse Representation Classification ◽  
The Difference ◽  
Representation Method

Collaborative representation classification (CRC) is an important sparse method, which is easy to carry out and uses a linear combination of training samples to represent a test sample. CRC method utilizes the offset between representation result of each class and the test sample to implement classification. However, the offset usually cannot well express the difference between every class and the test sample. In this paper, we propose a novel representation method for image recognition to address the above problem. This method not only fuses sparse representation and CRC method to improve the accuracy of image recognition, but also has novel fusion mechanism to classify images. The implementations of the proposed method have the following steps. First of all, it produces collaborative representation of the test sample. That is, a linear combination of all the training samples is first determined to represent the test sample. Then, it gets the sparse representation classification (SRC) of the test sample. Finally, the proposed method respectively uses CRC and SRC representations to obtain two kinds of scores of the test sample and fuses them to recognize the image. The experiments of face recognition show that the combination of CRC and SRC has satisfactory performance for image classification.

Sub-Band Target Alignment Common Spatial Pattern in Brain-Computer Interface

2021 ◽  
pp. 106150
Author(s):  
Xianxiong Zhang ◽  
Qingshan She ◽  
Yun Chen ◽  
Wanzeng Kong ◽  
Congli Mei
Keyword(s):  
Spatial Pattern ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Common Spatial Pattern
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