Distance-based weighted sparse representation to classify motor imagery EEG signals for BCI applications

Sreeja, S. R.; Himanshu, -; Samanta, Debasis

doi:10.1007/s11042-019-08602-0

Cited by 25 publications

(17 citation statements)

References 62 publications

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“…CSP method maximizes the variance of signals for one class while minimizing the variance of the signals for the other. There are several methods to extend the CSP method to improve the classification accuracy, such as a filter bank common spatial pattern (FBCSP) [54] , sparsity approach [55] , [56] , a sparse filter band common spatial pattern (SFBCSP) [57] , temporally constrained sparse group spatial pattern (TSGSP) [58] . Therefore, we used the CSP feature as one feature, as the CSP feature is suitable for MI classification.…”

Section: Methods and Proceduresmentioning

confidence: 99%

Massage Therapy’s Effectiveness on the Decoding EEG Rhythms of Left/Right Motor Imagery and Motion Execution in Patients With Skeletal Muscle Pain

Fan

et al. 2021

IEEE J. Transl. Eng. Health Med.

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Objective: Most of effectiveness assessments of the widely-used Massage therapy were based on subjective routine clinical assessment tools, such as Visual Analogue Scale (VAS) score. However, few studies demonstrated the impact of massage on the Electroencephalograph (EEG) rhythm decoding of Motor imagery (MI) and motion execution (ME) with trunk left/right bending in patients with skeletal muscle pain. Method: We used the sample entropy (SampEn), permutation entropy (PermuEn), common spatial pattern (CSP) features, support vector machine (SVM) and logic regression (LR) classifiers. We also used the convolutional neural network (CNN) and attention-based bi-directional long short-term memory (BiLSTM) for classification. Results: The averaged SampEn and PermuEn values of alpha rhythm decreased in almost fourteen channels for five statuses (quiet, MI with left/right bending, ME with left/right bending). It indicated that massage alleviates the pain for the patients of skeletal pain. Furthermore, compared with the SVM and LR classifiers, the BiLSTM method achieved a better area under curve (AUC) of 0.89 for the classification of MI with trunk left/right bending before massage. The AUC became smaller after massage than that before massage for the classification of MI with trunk left/right bending using CNN and BiLSTM methods. The Permutation direct indicator (PDI) score showed the significant difference for patients in different statuses (before vs after massage, and MI vs ME). Conclusions: Massage not only affects the quiet status, but also affects the MI and ME. Clinical Impact: Massage therapy may affect a bit on the accuracy of MI with trunk left/right bending and it change the topography of MI and ME with trunk left/right bending for the patients with skeletal muscle pain.

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Section: Methods and Proceduresmentioning

confidence: 99%

Massage Therapy’s Effectiveness on the Decoding EEG Rhythms of Left/Right Motor Imagery and Motion Execution in Patients With Skeletal Muscle Pain

Fan

et al. 2021

IEEE J. Transl. Eng. Health Med.

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“…Zhou et al (2012) proposed a method to learn a new dictionary with smaller size and more discriminative ability for the classification, and the experimental results of the EEG classification show that the proposed method outperforms the SRC method. Sreeja and Samanta (2020) proposed a weighted SRC (WSRC) for classifying MI signals to further boost the proficiency of SRC technique, and the experimental results substantiate that WSRC is more efficient and accurate than SRC.However, there is a contradiction between dictionary size and algorithm recognition accuracy.…”

Section: Introductionmentioning

confidence: 93%

An Intelligent EEG Classification Methodology Based on Sparse Representation Enhanced Deep Learning Networks

Huang

Chen

et al. 2020

Front. Neurosci.

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The classification of electroencephalogram (EEG) signals is of significant importance in brain-computer interface (BCI) systems. Aiming to achieve intelligent classification of EEG types with high accuracy, a classification methodology using sparse representation (SR) and fast compression residual convolutional neural networks (FCRes-CNNs) is proposed. In the proposed methodology, EEG waveforms of classes 1 and 2 are segmented into subsignals,and 140 experimental samples were achieved for each type of EEG signal. The common spatial patterns algorithm is used to obtain the features of the EEG signal. Subsequently, the redundant dictionary with sparse representation is constructed based on these features. Finally, the samples of the EEG types were imported into the FCRes-CNN model having fast down-sampling module and residual block structural units to be identified and classified. The datasets from BCI Competition 2005 (dataset IVa) and BCI Competition 2003 (dataset III) were used to test the performance of the proposed deep learning classifier. The classification experiments show that the recognition averaged accuracy of the proposed method is 98.82%. The experimental results show that the classification method provides better classification performance compared with sparse representation classification (SRC) method. The method can be applied successfully to BCI systems where the amount of data is large due to daily recording.

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“…SRC has been used in the compressed sensing (CS) theory; the core concept of CS is that we can represent a huge amount of data with a few data points [ 23 ]. Weighted SRC was applied to EEG-BCI to classify motor imagery, and achieved good classification accuracy results [ 24 ]. Sparse representation-based classification was used to translate the motor imagery of a single index finger classification, with an accuracy of 81.32%; the results were used to construct a BCI-enhanced finger rehabilitation system [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

LASSO Homotopy-Based Sparse Representation Classification for fNIRS-BCI

Gulraiz

Naseer

Nazeer

et al. 2022

Sensors

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Brain-computer interface (BCI) systems based on functional near-infrared spectroscopy (fNIRS) have been used as a way of facilitating communication between the brain and peripheral devices. The BCI provides an option to improve the walking pattern of people with poor walking dysfunction, by applying a rehabilitation process. A state-of-the-art step-wise BCI system includes data acquisition, pre-processing, channel selection, feature extraction, and classification. In fNIRS-based BCI (fNIRS-BCI), channel selection plays a vital role in enhancing the classification accuracy of the BCI problem. In this study, the concentration of blood oxygenation (HbO) in a resting state and in a walking state was used to decode the walking activity and the resting state of the subject, using channel selection by Least Absolute Shrinkage and Selection Operator (LASSO) homotopy-based sparse representation classification. The fNIRS signals of nine subjects were collected from the left hemisphere of the primary motor cortex. The subjects performed the task of walking on a treadmill for 10 s, followed by a 20 s rest. Appropriate filters were applied to the collected signals to remove motion artifacts and physiological noises. LASSO homotopy-based sparse representation was used to select the most significant channels, and then classification was performed to identify walking and resting states. For comparison, the statistical spatial features of mean, peak, variance, and skewness, and their combination, were used for classification. The classification results after channel selection were then compared with the classification based on the extracted features. The classifiers used for both methods were linear discrimination analysis (LDA), support vector machine (SVM), and logistic regression (LR). The study found that LASSO homotopy-based sparse representation classification successfully discriminated between the walking and resting states, with a better average classification accuracy (p < 0.016) of 91.32%. This research provides a step forward in improving the classification accuracy of fNIRS-BCI systems. The proposed methodology may also be used for rehabilitation purposes, such as controlling wheelchairs and prostheses, as well as an active rehabilitation training technique for patients with motor dysfunction.

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Distance-based weighted sparse representation to classify motor imagery EEG signals for BCI applications

Cited by 25 publications

References 62 publications

Massage Therapy’s Effectiveness on the Decoding EEG Rhythms of Left/Right Motor Imagery and Motion Execution in Patients With Skeletal Muscle Pain

Massage Therapy’s Effectiveness on the Decoding EEG Rhythms of Left/Right Motor Imagery and Motion Execution in Patients With Skeletal Muscle Pain

An Intelligent EEG Classification Methodology Based on Sparse Representation Enhanced Deep Learning Networks

LASSO Homotopy-Based Sparse Representation Classification for fNIRS-BCI

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