Sparse representation-based classification with two-dimensional dictionary optimization for motor imagery EEG pattern recognition

Meng, Ming; Yin, Xu; She, Qingshan; Gao, Yunhe; Kong, Wanzeng; Luo, Zhizeng

doi:10.1016/j.jneumeth.2021.109274

Cited by 4 publications

(2 citation statements)

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“…The unoccluded subset of 100 classes is selected as test samples. Some classical algorithms are selected for comparison, including sparse representation-based classification (SRC) [ 16 ], cyclic redundancy check (CRC) [ 17 ], regularized robust coding for face recognition (RRC) [ 18 ], low-rank matrix recovery with structural incoherence (LR) [ 19 ], extended sparse representation-based classification (ESRC) [ 20 ], and discriminative low-rank representation method (DLRR) [ 21 ]. All experiments are carried out on a computer with an Intel(R), Xeon(R), CPU E5-2630 processor, 64G memory, and MATLAB version R2014b [ 22 ].…”

Section: Methodsmentioning

confidence: 99%

Face Recognition Method under Adaptive Image Matching and Dictionary Learning Algorithm

Xue

Wang

2023

Computational Intelligence and Neuroscience

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In this research, a robust face recognition method based on adaptive image matching and a dictionary learning algorithm was proposed. A Fisher discriminant constraint was introduced into the dictionary learning algorithm program so that the dictionary had certain category discrimination ability. The purpose was to use this technology to reduce the influence of pollution, absence, and other factors on face recognition and improve the recognition rate. The optimization method was used to solve the loop iteration to obtain the expected specific dictionary, and the selected specific dictionary was used as the representation dictionary in adaptive sparse representation. In addition, if a specific dictionary was placed in a seed space of the original training data, the mapping matrix can be used to represent the mapping relationship between the specific dictionary and the original training sample, and the test sample could be corrected according to the mapping matrix to remove the contamination in the test sample. Moreover, the feature face method and dimension reduction method were used to process the specific dictionary and the corrected test sample, and the dimensions were reduced to 25, 50, 75, 100, 125, and 150, respectively. In this research, the recognition rate of the algorithm in 50 dimensions was lower than that of the discriminatory low-rank representation method (DLRR), and the recognition rate in other dimensions was the highest. The adaptive image matching classifier was used for classification and recognition. The experimental results showed that the proposed algorithm had a good recognition rate and good robustness against noise, pollution, and occlusion. Health condition prediction based on face recognition technology has the advantages of being noninvasive and convenient operation.

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Section: Methodsmentioning

confidence: 99%

Face Recognition Method under Adaptive Image Matching and Dictionary Learning Algorithm

Xue

Wang

2023

Computational Intelligence and Neuroscience

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“…Classifications based on sparse representations of some EEG feature (SRC) 5 , 6 are increasingly useful to obtain good performance from untrained patterns 7 . This way, allowing a smaller volume of data to be necessary in the training process of a classification model 2 , 4 , 8 seek to improve this approach by focusing on optimizing and learning dictionaries for sparse representation 9 – 11 , in turn, choose to focus on improving the features that will be represented.…”

Section: Introductionmentioning

confidence: 99%

Motor imagery classification using sparse representations: an exploratory study

de Menezes,

Gomes,

de Carvalho Hazin

et al. 2023

Sci Rep

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The non-stationary nature of the EEG signal poses challenges for the classification of motor imagery. sparse representation classification (SRC) appears as an alternative for classification of untrained conditions and, therefore, useful in motor imagery. Empirical mode decomposition (EMD) deals with signals of this nature and appears at the rear of the classification, supporting the generation of features. In this work we evaluate the combination of these methods in a multiclass classification problem, comparing them with a conventional method in order to determine if their performance is regular. For comparison with SRC we use multilayer perceptron (MLP). We also evaluated a hybrid approach for classification of sparse representations with MLP (RSMLP). For comparison with EMD we used filtering by frequency bands. Feature selection methods were used to select the most significant ones, specifically Random Forest and Particle Swarm Optimization. Finally, we used data augmentation to get a more voluminous base. Regarding the first dataset, we observed that the classifiers that use sparse representation have results equivalent to each other, but they outperform the conventional MLP model. SRC and SRMLP achieve an average accuracy of $$75.95\%$$ 75.95 % and $$82.51\%$$ 82.51 % respectively while the MLP is $$72.38\%$$ 72.38 % , representing a gain between $$4.93\%$$ 4.93 % and $$14\%$$ 14 % . The use of EMD in relation to other feature processing techniques is not superior. However, EMD does not influence negatively, there is an opportunity for improvement. Finally, the use of data augmentation proved to be important to obtain relevant results. In the second dataset, we did not observe the same results. Models based on sparse representation (SRC, SRMLP, etc.) have on average a performance close to other conventional models, but without surpassing them. The best sparse models achieve an average accuracy of $$95.43\%$$ 95.43 % among the subjects in the base, while other model reach $$98.33\%$$ 98.33 % . The improvement of self-adaptive mechanisms that respond efficiently to the user’s context is a good way to achieve improvements in motor imagery applications. However, other scenarios should be investigated, since the advantage of these methods was not proven in all datasets studied. There is still room for improvement, such as optimizing the dictionary of sparse representation in the context of motor imagery. Investing efforts in synthetically increasing the training base has also proved important to reduce the costs of this group of applications.

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Classification based on sparse representations of attributes derived from empirical mode decomposition in a multiclass problem of motor imagery in EEG signals

de Menezes,

Gomes,

de Carvalho Hazin

et al. 2023

Health Technol.

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Sparse representation-based classification with two-dimensional dictionary optimization for motor imagery EEG pattern recognition

Cited by 4 publications

References 43 publications

Face Recognition Method under Adaptive Image Matching and Dictionary Learning Algorithm

Face Recognition Method under Adaptive Image Matching and Dictionary Learning Algorithm

Motor imagery classification using sparse representations: an exploratory study

Classification based on sparse representations of attributes derived from empirical mode decomposition in a multiclass problem of motor imagery in EEG signals

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