Sparse Representation-Based Extreme Learning Machine for Motor Imagery EEG Classification

She, Qingshan; Chen, Kang; Ma, Yuliang; Nguyen, Thinh; Zhang, Yingchun

doi:10.1155/2018/9593682

Cited by 25 publications

(18 citation statements)

References 34 publications

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“…For the three subjects, they achieved an average accuracy of 86.5%. The studies in Jin et al (2019), Zhang et al (2018), Selim et al (2018), She et al (2018), Mishuhina & Jiang (2018) also utilised multi-class CSP to extract the feature although they employed a variety of feature selection and classification algorithm. The SVM has been utilised in different studies (Jin et al, 2019;Zhang et al, 2018;Selim et al, 2018) to classify this multi-class MI EEG data and achieved the average classification accuracy of 91.9%, 88.52% and 86.57%, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…The SVM has been utilised in different studies (Jin et al, 2019;Zhang et al, 2018;Selim et al, 2018) to classify this multi-class MI EEG data and achieved the average classification accuracy of 91.9%, 88.52% and 86.57%, respectively. Mishuhina & Jiang (2018) achieved a 90% accuracy to classify the same dataset by using LDA, whereas She et al (2018) utilised FDDL-ELM and achieved the classification accuracy of 87.54%. Our proposed method have achieved a higher classification accuracy (93.19 ± 8.54%) as compared to the other methods.…”

Section: Discussionmentioning

confidence: 99%

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The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN

Rashid

Bari

Hasan

et al. 2021

PeerJ Computer Science

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Brain-computer interface (BCI) is a viable alternative communication strategy for patients of neurological disorders as it facilitates the translation of human intent into device commands. The performance of BCIs primarily depends on the efficacy of the feature extraction and feature selection techniques, as well as the classification algorithms employed. More often than not, high dimensional feature set contains redundant features that may degrade a given classifier’s performance. In the present investigation, an ensemble learning-based classification algorithm, namely random subspace k-nearest neighbour (k-NN) has been proposed to classify the motor imagery (MI) data. The common spatial pattern (CSP) has been applied to extract the features from the MI response, and the effectiveness of random forest (RF)-based feature selection algorithm has also been investigated. In order to evaluate the efficacy of the proposed method, an experimental study has been implemented using four publicly available MI dataset (BCI Competition III dataset 1 (data-1), dataset IIIA (data-2), dataset IVA (data-3) and BCI Competition IV dataset II (data-4)). It was shown that the ensemble-based random subspace k-NN approach achieved the superior classification accuracy (CA) of 99.21%, 93.19%, 93.57% and 90.32% for data-1, data-2, data-3 and data-4, respectively against other models evaluated, namely linear discriminant analysis, support vector machine, random forest, Naïve Bayes and the conventional k-NN. In comparison with other classification approaches reported in the recent studies, the proposed method enhanced the accuracy by 2.09% for data-1, 1.29% for data-2, 4.95% for data-3 and 5.71% for data-4, respectively. Moreover, it is worth highlighting that the RF feature selection technique employed in the present study was able to significantly reduce the feature dimension without compromising the overall CA. The outcome from the present study implies that the proposed method may significantly enhance the accuracy of MI data classification.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN

Rashid

Bari

Hasan

et al. 2021

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…She, Qingshan Et al. [26] security and protection issues in DD are featured. It is extensive and demonstrates that in-band is far superior to out-band with down to earth and mechanical reasons.…”

Section: Related Workmentioning

confidence: 99%

“…The ELM learning algorithm is single hidden feedforwarded neural networks (SLFNs). The ELM algorithm reduces the factor of variance and increases the rate of predication and detection of sample data of motor imagery EEG classification [26].…”

Section: (B) Wavelet Transformmentioning

confidence: 99%

Analysis of Motor Imagery EEG Classification Based on Feature Extraction and Machine Learning Algorithm

Chintamani¹

2021

ITII

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The brain-computer interface provides the excellent potential to address nervous system-related activity. The function of the nervous system work between internal brain control and external human body physical structure. Some parts of the human body cannot generate the signal for the processing of the human brain, cannot recognize and identify human body parts' activity—the motor imagery EEG classification approach helps resolve such types of critical illness cause of death. The dimension and structure of motor imagery-based EEG data are very high and unsupported behaviors. The machine learning and another classification algorithm cannot handle these variants of EEG data directly. For the process of better classification of motor imagery, EEG needs transformation and extraction. The transform-based feature extraction process such as DCT, DWT, SFTF and some other harmonic frequency-based applied. In this paper presents the details analysis of feature extraction and classification algorithms for motor imagery EEG classification. The machine learning provides three types of an algorithm for classification, supervised, unsupervised and semi-supervised. This paper mainly focuses on the supervised machine learning algorithm. For the analysis of machine learning algorithm use BC competition-IV dataset. MATLAB software is used as a tool for the code of algorithms and measures standard parameters such as accuracy, sensitivity and specificity.

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“…Wang et al [24] proposed a novel framework to extract compact and discriminative features from ECG signals for human identification based on sparse representation of local segments, which could capture local and global structural information to characterize the ECG signals more precisely. She et al [25] proposed a novel approach called FDDL-ELM, which combines the discriminative power of extreme learning machine (ELM) with the reconstruction capability of sparse representation and achieve superior performance than the other existing algorithms.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Human Health Monitoring and Time-Frequency Sparse Representation Using EEG and ECG Signals

Geng

et al. 2019

IEEE Access

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In the field of human health monitoring, intelligent diagnostic methods have drawn much attention recently to tackle the health problems and challenges faced by patients. In this paper, an efficient and flexible diagnostic method is proposed, which enables the simultaneous use of a machine learning method and sparsity-based representation technique. Specifically, the proposed method is based on a convolutional neural network (CNN) and generalized minimax-concave (GMC) method. First, measured potential signals, for instance, electroencephalogram (EEG) and electrocardiogram (ECG) signals are directly inputted into the designed network based on CNN for health conditions classification. The designed network adopts small convolution kernels to enhance the performance of feature extraction. In the training process, small batch samples are applied to improve the generalization of the model. Meanwhile, the ''Dropout'' strategy is applied to overcome the overfitting problem in fully connected layers. Then, for a record of the interested EEG or ECG signal, the sparse representation of useful time-frequency features can be estimated via the GMC method. Case studies of seizure detection and arrhythmia signal analysis are adopted to verify the performance of the proposed method. The experimental results demonstrate that the proposed method can effectively identify different health conditions and maximally enhance the sparsity of time-frequency features. INDEX TERMS Feature extraction, convolutional neural network, deep learning, sparse representation, health monitoring.

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Sparse Representation-Based Extreme Learning Machine for Motor Imagery EEG Classification

Cited by 25 publications

References 34 publications

The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN

The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN

Analysis of Motor Imagery EEG Classification Based on Feature Extraction and Machine Learning Algorithm

Simultaneous Human Health Monitoring and Time-Frequency Sparse Representation Using EEG and ECG Signals

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