Classification of multiclass motor imagery EEG signal using sparsity approach

Sreeja, S. R.; Samanta, Debasis

doi:10.1016/j.neucom.2019.08.037

Cited by 36 publications

(16 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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.

View full text Add to dashboard Cite

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.

show abstract

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.

View full text Add to dashboard Cite

show abstract

“…Previous studies have reported that the dominant frequency bands related to motor imagery were µ rhythms (7)(8)(9)(10)(11)(12) and β rhythms (14-30 Hz) [23]- [26]. In this paper, the EEG signal in the range of 4 to 40 Hz selects the sub-band for the corresponding band-pass filtering [9], [10], [27], [28].…”

Section: B Signal Preprocessingmentioning

confidence: 99%

An Approach of One-vs-Rest Filter Bank Common Spatial Pattern and Spiking Neural Networks for Multiple Motor Imagery Decoding

Wang

Tang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Motor imagery (MI) is a typical BCI paradigm and has been widely applied into many aspects (e.g. brain-driven wheelchair and motor function rehabilitation training). Although significant achievements have been achieved, multiple motor imagery decoding is still unsatisfactory. To deal with this challenging issue, firstly, a segment of electroencephalogram was extracted and preprocessed. Secondly, we applied a filter bank common spatial pattern (FBCSP) with one-vs-rest (OVR) strategy to extract the spatio-temporal-frequency features of multiple MI. Thirdly, the F-score was employed to optimise and select these features. Finally, the optimized features were fed to the spiking neural networks (SNN) for classification. Evaluation was conducted on two public multiple MI datasets (Dataset IIIa of the BCI competition III and Dataset IIa of the BCI competition IV). Experimental results showed that the average accuracy of the proposed framework reached up to 90.09% (kappa: 0.868) and 81.33% (kappa: 0.751) on the two public datasets, respectively. The achieved performance (accuracy and kappa) was comparable to the best one of the compared methods. This study demonstrated that the proposed method can be used as an alternative approach for multiple MI decoding and it provided a potential solution for online multiple MI detection. INDEX TERMS Electroencephalogram, motor imagery (MI), filter bank common spatial pattern (FBCSP), spiking neural networks (SNN). I. INTRODUCTION Electroencephalography (EEG) signal is usually used in brain-computer interface (BCI) systems due to its high temporal resolution [1]. Motor imagery (MI)-based BCI is one of classical paradigms and has been employed to restore the communication pathway or movement function for disabled, paralyzed, and stroke patients [2], [3]. Patients are able to The associate editor coordinating the review of this manuscript and approving it for publication was Li He .

show abstract

“…Most of the machine learning and deep learning approaches have been employed to classify the MI activity where support vector machine (SVM) and linear discriminant analysis (LDA) were observed to be the widely used (Padfield et al, 2019). The recent studies on MI EEG signal data classification are based on SVM (Jin et al, 2019), dynamic and self-adaptive algorithm (Belwafi et al, 2019), LDA (Suwannarat, Pan-ngum & Israsena, 2018), functional link neural network (Hettiarachchi et al, 2018), Gaussian mixture model (He et al, 2016), sparsity approach (Sreeja & Samanta, 2019), k-nearest neighbour (k-NN), and Naïve-Bayesian (Bhaduri et al, 2016). Most of these classifiers are primarily constructed for binary classification problems.…”

Section: Introductionmentioning

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

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.

show abstract

Classification of multiclass motor imagery EEG signal using sparsity approach

Cited by 36 publications

References 45 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 Approach of One-vs-Rest Filter Bank Common Spatial Pattern and Spiking Neural Networks for Multiple Motor Imagery Decoding

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

Contact Info

Product

Resources

About