scholarly journals Robust Sparse Representation and Multiclass Support Matrix Machines for the Classification of Motor Imagery EEG Signals

2019 ◽  
Vol 7 ◽  
pp. 1-8 ◽  
Author(s):  
Imran Razzak ◽  
Ibrahim A. Hameed ◽  
Guandong Xu
Keyword(s):  
Sparse Representation ◽  
Motor Imagery ◽  
Eeg Signals ◽  
Support Matrix

scholarly journals Motor Imagery EEG Classification Using Capsule Networks

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2854 ◽  
Author(s):  
Kwon-Woo Ha ◽  
Jin-Woo Jeong
Keyword(s):  
Motor Imagery ◽  
Learning Approaches ◽  
Eeg Signals ◽  
Left Hand ◽  
Computer Interfaces ◽  
Conventional Machine ◽  
Short Time ◽  
Electroencephalogram Eeg ◽  
Target Data

Various convolutional neural network (CNN)-based approaches have been recently proposed to improve the performance of motor imagery based-brain-computer interfaces (BCIs). However, the classification accuracy of CNNs is compromised when target data are distorted. Specifically for motor imagery electroencephalogram (EEG), the measured signals, even from the same person, are not consistent and can be significantly distorted. To overcome these limitations, we propose to apply a capsule network (CapsNet) for learning various properties of EEG signals, thereby achieving better and more robust performance than previous CNN methods. The proposed CapsNet-based framework classifies the two-class motor imagery, namely right-hand and left-hand movements. The motor imagery EEG signals are first transformed into 2D images using the short-time Fourier transform (STFT) algorithm and then used for training and testing the capsule network. The performance of the proposed framework was evaluated on the BCI competition IV 2b dataset. The proposed framework outperformed state-of-the-art CNN-based methods and various conventional machine learning approaches. The experimental results demonstrate the feasibility of the proposed approach for classification of motor imagery EEG signals.

scholarly journals Classification of Motor Imagery Based EEG Signals Using Sparsity Approach

2017 ◽  
pp. 47-59 ◽  
Author(s):  
S. R. Sreeja ◽  
Joytirmoy Rabha ◽  
Debasis Samanta ◽  
Pabitra Mitra ◽  
Monalisa Sarma
Keyword(s):  
Motor Imagery ◽  
Eeg Signals

Classification of EEG Signals Based on Filter Bank and Sparse Representation in Motor Imagery Brain-Computer Interfaces

2019 ◽  
Vol 29 (03) ◽  
pp. 2050034 ◽  
Author(s):  
Jin Wang ◽  
Qingguo Wei
Keyword(s):  
Sparse Representation ◽  
Motor Imagery ◽  
Filter Bank ◽  
Classification Performance ◽  
Brain Computer Interfaces ◽  
Eeg Signals ◽  
Fisher Score ◽  
Common Spatial Pattern ◽  
Signal Processing Algorithm ◽  
Computer Interfaces

To improve the classification performance of motor imagery (MI) based brain-computer interfaces (BCIs), a new signal processing algorithm for classifying electroencephalogram (EEG) signals by combining filter bank and sparse representation is proposed. The broadband EEG signals of 8–30[Formula: see text]Hz are segmented into 10 sub-band signals using a filter bank. EEG signals in each sub-band are spatially filtered by common spatial pattern (CSP). Fisher score combined with grid search is used for selecting the optimal sub-band, the band power of which is employed for designing a dictionary matrix. A testing signal can be sparsely represented as a linear combination of some columns of the dictionary. The sparse coefficients are estimated by [Formula: see text] norm optimization, and the residuals of sparse coefficients are exploited for classification. The proposed classification algorithm was applied to two BCI datasets and compared with two traditional broadband CSP-based algorithms. The results showed that the proposed algorithm provided superior classification accuracies, which were better than those yielded by traditional algorithms, verifying the efficacy of the present algorithm.

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