Multiband tangent space mapping and feature selection for classification of EEG during motor imagery

Islam, Rabiul; Tanaka, Tomomi; Molla, Khademul Islam

doi:10.1088/1741-2552/aac313

Cited by 41 publications

(33 citation statements)

References 63 publications

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“…Zhang et al used OVR-FBCSP combined with CNN+LSTM method for MI recognition [53]. Islam et al used TSM to find the precise frequency band associated with the MI mission for MI recognition [41]. In their work, the optimal MI recognition accuracy based on the sub-band selection method of mutual information is 82.6%±13.06, which is lower than our experimental result 90.09% and proves that the effect of FBCSP is feasible for MI recognition.…”

Section: B Different Frequency Range Selectioncontrasting

confidence: 64%

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

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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 .

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Section: B Different Frequency Range Selectioncontrasting

confidence: 64%

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

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“…Let us consider that one has a set of matrices P which have m × m dimensions. As represented in [17], this set can be defined as P(m)={P∈normalS(m)|uTPu>0, ∀normalu∈ Rm, u≠0}, where S(m) indicates the space of all symmetric matrices. Moreover, this creates a manifold ℳ with the dimension m(normalm+1)/2.…”

Section: Related Studiesmentioning

confidence: 99%

Efficient Classification of Motor Imagery Electroencephalography Signals Using Deep Learning Methods

Majidov

Whangbo

2019

Sensors

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Single-trial motor imagery classification is a crucial aspect of brain–computer applications. Therefore, it is necessary to extract and discriminate signal features involving motor imagery movements. Riemannian geometry-based feature extraction methods are effective when designing these types of motor-imagery-based brain–computer interface applications. In the field of information theory, Riemannian geometry is mainly used with covariance matrices. Accordingly, investigations showed that if the method is used after the execution of the filterbank approach, the covariance matrix preserves the frequency and spatial information of the signal. Deep-learning methods are superior when the data availability is abundant and while there is a large number of features. The purpose of this study is to a) show how to use a single deep-learning-based classifier in conjunction with BCI (brain–computer interface) applications with the CSP (common spatial features) and the Riemannian geometry feature extraction methods in BCI applications and to b) describe one of the wrapper feature-selection algorithms, referred to as the particle swarm optimization, in combination with a decision tree algorithm. In this work, the CSP method was used for a multiclass case by using only one classifier. Additionally, a combination of power spectrum density features with covariance matrices mapped onto the tangent space of a Riemannian manifold was used. Furthermore, the particle swarm optimization method was implied to ease the training by penalizing bad features, and the moving windows method was used for augmentation. After empirical study, the convolutional neural network was adopted to classify the pre-processed data. Our proposed method improved the classification accuracy for several subjects that comprised the well-known BCI competition IV 2a dataset.

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“…However, the problem is very challenging due to very different locations of electrodes and subjects-specific nature of epilepsy events. In addition, Islam et al 26 reported that the appropriate selection of operational subbands can significantly improve the system performance due to subject-specific nature of EEG signals. Therefore, the possible extension of this study is to detect the most significant subbands in the high-frequency components, which may further improve our system performance in the future.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, a filter bank, which is an array of bandpass filters, was applied to decompose an EEG signal into a set of analysis signals exhibiting multiple subband frequency components 64,65 . To develop more accurate detection of brain activities related to the specific mental tasks, EEG-based studies proposed the filter-bank method to divide the wide frequency ranges into narrow subbands [24][25][26] . More specific, Higashi et al proposed a filter-bank approach to improve the performance of MI-BCI, which decomposed the 4-40 Hz frequency ranges into 6 subbands with a bandwidth of 6 Hz each 66 .…”

Section: Methodsmentioning

confidence: 99%

“…Ang et al divided the similar frequency ranges (4-40 Hz) into narrow subbands with a bandwidth of 4 Hz each 24 . In signal processing study, the choice of subbands should be as narrow as possible to achieve more accurate detection of automatic system similar to these EEG-BCI [24][25][26]66 . However, the choice of dividing the wide frequency bands into narrow subbands indeed depends on system performance, real-time applications as well as the reduction of system complexity 18,25,67 .…”

Section: Methodsmentioning

confidence: 99%

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Multiband entropy-based feature-extraction method for automatic identification of epileptic focus based on high-frequency components in interictal iEEG

Akter

Islam

Iimura

et al. 2020

Sci Rep

Self Cite

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presurgical investigations for categorizing focal patterns are crucial, leading to localization and surgical removal of the epileptic focus. this paper presents a machine learning approach using information theoretic features extracted from high-frequency subbands to detect the epileptic focus from interictal intracranial electroencephalogram (ieeG). it is known that high-frequency subbands (>80 Hz) include important biomarkers such as high-frequency oscillations (Hfos) for identifying epileptic focus commonly referred to as the seizure onset zone (SoZ). in this analysis, the multi-channel interictal ieeG signals were splitted into segments and each segment was decomposed into multiple high-frequency subbands. The different types of entropy were calculated for each of the subbands and the sparse linear discriminant analysis (sLDA) was applied to select the prominent entropy features. Due to the imbalance of SoZ and non-SoZ channels in ieeG data, the use of machine learning techniques is always tricky. to deal with the imbalanced learning problem, an adaptive synthetic oversampling approach (ADASYn) with radial basis function kernel-based SVM was used to detect the focal segments. finally, the epileptic focus was identified based on detection of focal segments on SOZ and non-SOZ channels. Eight patients were examined to observe the efficiency of the automatic detector. The experimental results and statistical tests indicate that the proposed automatic detector can identify the epileptic focus accurately and efficiently.

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Multiband tangent space mapping and feature selection for classification of EEG during motor imagery

Abstract: The increased classification accuracy of MI tasks with the proposed MTSMS approach can yield effective implementation of BCI. The mutual information-based subband selection method is implemented to tune operation frequency bands to represent actual motor imagery tasks.

Cited by 41 publications

References 63 publications

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

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

Efficient Classification of Motor Imagery Electroencephalography Signals Using Deep Learning Methods

Multiband entropy-based feature-extraction method for automatic identification of epileptic focus based on high-frequency components in interictal iEEG

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