Classification of Motor Imagery EEG Based on Sparsification and Non-negative Matrix Factorization

Su, Jing; Yang, Zuyuan; Wang, Haiping; Han, Wei

doi:10.1051/matecconf/201816007007

“…NMF has been successfully used in the literature for MI classification [38][39][40][44][45][46]. For instance, Lee et al [45] mainly combine time domain and frequency domain features obtained using NMF, whereas Lua et al [38] combined event related potential and event related spectral perturbation features decomposed using modified mixed alternating least square based NMF method.…”

Section: Resultsmentioning

confidence: 99%

Subject-specific EEG channel selection using non-negative matrix factorization for lower-limb motor imagery recognition

Gurve

¹

,

Delisle-Rodríguez

²

,

Romero-Laiseca

³

et al. 2020

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Objective. This study aims to propose and validate a subject-specific approach to recognize two different cognitive neural states (relax and pedaling motor imagery (MI)) by selecting the relevant electroencephalogram (EEG) channels. The main aims of the proposed work are: (i) to reduce the computational complexity of the BCI systems during MI detection by selecting the relevant EEG channels, (ii) to reduce the amount of data overfitting that may arise due to unnecessary channels and redundant features, and (iii) to reduce the classification time for real-time BCI applications. Approach. The proposed method selects subject-specific EEG channels and features based on their MI. In this work, we make use of non-negative matrix factorization to extract the weight of the EEG channels based on their contribution to MI detection. Further, the neighborhood component analysis is used for subject-specific feature selection. Main results. We executed the experiments using EEG signals recorded for MI where ten healthy subjects performed MI movement of the lower limb to generate motor commands. An average accuracy of 96.66%, average true positive rate (TPR) of 97.77%, average false positives rate of 4.44%, and average Kappa of 93.33% were obtained. The proposed subject-specific EEG channel selection based MI recognition system provides 13.20% improvement in detection accuracy, and 27% improvement in Kappa value with less number of EEG channels compared to the results obtained using all EEG channels. Significance. The proposed subject-specific BCI system has been found significantly advantageous compared to the typical approach of using a fixed channel configuration. This work shows that fewer EEG channels not only reduce computational complexity and processing time (two times faster) but also improve the MI detection performance. The proposed method selects EEG locations related to the foot movement, which may be relevant for neuro-rehabilitation using lower-limb movements that may provide a real-time and more natural interface between patient and robotic device.

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“…In another study, H. Tsubakida et al [70] proposed another framework for MI EEG classification which selects the frequency band of interest automatically by using NMF. Furthermore, J. Su et al [71] used NMF to reduce the redundant information of extracted features from mu and beta frequency bands of the EEG signal for MI classification. Similarly, a group NMF based method by Lee et al [72] uses NMF grouping which can effectively analyze and extract the feature from EEG spectrum of different subjects performing repetition imagination of left/right-hand movements and generation of words beginning with the same random letter, based on intra and inter-subject variations and individual EEG characteristics.…”

Section: Nmf For Eeg Signalmentioning

confidence: 99%

Signal Analysis Techniques for Resource Optimization in Brain-Computer Interfaces and Other Wearables

Gurve¹

2023

Preprint

0

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Stroke is a serious neurological illness that often leads to motor dysfunction and affects the daily life activities of an individual. Although stroke is a complex medical issue, there is a way to reduce its impact by post-stroke neural rehabilitation. Brain-computer interfaces (BCIs) establishes a real-time interaction between the patients and rehabilitation devices and help stroke patients to restore their lost motor function. However, the existing BCIs have relatively high power consumption, more computational complexity, and large processing time, and therefore, limiting their effectiveness to enhance a patient’s health. The advances in signal processing and machine learning can help us in developing low-power and low-complexity solutions to address the aforementioned problems. Therefore, this dissertation focuses on proposing advanced signal analysis techniques for resource optimization in brain-computer interfaces and other wearables. The first part of this dissertation focuses on developing a patient-specific electroencephalogram (EEG) channel selection methods for motor-imagery (MI) classification using Nonnegative Matrix Factorization (NMF) which is capable of reducing computational complexity and processing time. In addition, variance activated NMF for EEG channel selection is proposed to further reduce the system complexity and effective localization of cognition during a motor task. In the MI classification framework, the theory of Riemannian geometry is utilized for feature extraction from the reduced set of EEG channels, and the neighborhood component-based feature selection algorithm is employed for feature selection. The second part of this dissertation focuses on proposing the idea of reconstruction-free compressive feature learning for wireless BCIs to leverage the resources of current BCIs by reducing the power consumption by extracting the features directly from the compressed measurements. The developed methods for low-power BCIs are validated using three MI datasets. The experimental results in this dissertation show that the developed solutions are capable of reducing the total processing time up to 95 %, the total power required up to 50 %, and the system complexity up to 75 %. Lastly, it is shown that the NMF and Compressive Sensing (CS)-based approaches developed in this dissertation are also useful for any resource constraint wearable for other low-power and real-time applications.

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“…In another study, H. Tsubakida et al [70] proposed another framework for MI EEG classification which selects the frequency band of interest automatically by using NMF. Furthermore, J. Su et al [71] used NMF to reduce the redundant information of extracted features from mu and beta frequency bands of the EEG signal for MI classification. Similarly, a group NMF based method by Lee et al [72] uses NMF grouping which can effectively analyze and extract the feature from EEG spectrum of different subjects performing repetition imagination of left/right-hand movements and generation of words beginning with the same random letter, based on intra and inter-subject variations and individual EEG characteristics.…”

Section: Nmf For Eeg Signalmentioning

confidence: 99%

Signal Analysis Techniques for Resource Optimization in Brain-Computer Interfaces and Other Wearables

Gurve¹

2023

Preprint

0

View full text Add to dashboard Cite

Stroke is a serious neurological illness that often leads to motor dysfunction and affects the daily life activities of an individual. Although stroke is a complex medical issue, there is a way to reduce its impact by post-stroke neural rehabilitation. Brain-computer interfaces (BCIs) establishes a real-time interaction between the patients and rehabilitation devices and help stroke patients to restore their lost motor function. However, the existing BCIs have relatively high power consumption, more computational complexity, and large processing time, and therefore, limiting their effectiveness to enhance a patient’s health. The advances in signal processing and machine learning can help us in developing low-power and low-complexity solutions to address the aforementioned problems. Therefore, this dissertation focuses on proposing advanced signal analysis techniques for resource optimization in brain-computer interfaces and other wearables. The first part of this dissertation focuses on developing a patient-specific electroencephalogram (EEG) channel selection methods for motor-imagery (MI) classification using Nonnegative Matrix Factorization (NMF) which is capable of reducing computational complexity and processing time. In addition, variance activated NMF for EEG channel selection is proposed to further reduce the system complexity and effective localization of cognition during a motor task. In the MI classification framework, the theory of Riemannian geometry is utilized for feature extraction from the reduced set of EEG channels, and the neighborhood component-based feature selection algorithm is employed for feature selection. The second part of this dissertation focuses on proposing the idea of reconstruction-free compressive feature learning for wireless BCIs to leverage the resources of current BCIs by reducing the power consumption by extracting the features directly from the compressed measurements. The developed methods for low-power BCIs are validated using three MI datasets. The experimental results in this dissertation show that the developed solutions are capable of reducing the total processing time up to 95 %, the total power required up to 50 %, and the system complexity up to 75 %. Lastly, it is shown that the NMF and Compressive Sensing (CS)-based approaches developed in this dissertation are also useful for any resource constraint wearable for other low-power and real-time applications.

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Classification of Motor Imagery EEG Based on Sparsification and Non-negative Matrix Factorization

Cited by 5 publications

References 13 publications

Subject-specific EEG channel selection using non-negative matrix factorization for lower-limb motor imagery recognition

Subject-specific EEG channel selection using non-negative matrix factorization for lower-limb motor imagery recognition

Signal Analysis Techniques for Resource Optimization in Brain-Computer Interfaces and Other Wearables

Signal Analysis Techniques for Resource Optimization in Brain-Computer Interfaces and Other Wearables

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