Quantitative Comparison of EEG Compressed Sensing using Gabor and K-SVD Dictionaries

Dao, Phuong; Li, Xue Jun; Griffin, Anthony

doi:10.1109/icdsp.2018.8631806

“…P. Dao et al [115] evaluated the effect of different time and frequency step sizes in building Gabor atoms [113,114] on EEG signal compression using CS. Furthermore, P. Dao et al [116] also discussed on a quantitative comparison of CS using Gabor and K-singular value decomposition (SVD) dictionaries. Similarly, R. Kus et al [117] also proposed a novel construction of an optimal Gabor dictionary for multichannel and multi trial EEG, which allows a priori assessment of maximum a one-step error of the matching pursuit (MP) algorithm.…”

Section: Sparse Representationmentioning

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.

show abstract

“…P. Dao et al [115] evaluated the effect of different time and frequency step sizes in building Gabor atoms [113,114] on EEG signal compression using CS. Furthermore, P. Dao et al [116] also discussed on a quantitative comparison of CS using Gabor and K-singular value decomposition (SVD) dictionaries. Similarly, R. Kus et al [117] also proposed a novel construction of an optimal Gabor dictionary for multichannel and multi trial EEG, which allows a priori assessment of maximum a one-step error of the matching pursuit (MP) algorithm.…”

Section: Sparse Representationmentioning

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.

show abstract

“…Therefore, dictionary learning plays an important role in data compression based on sparse decomposition. At present, the commonly used dictionaries in compressed sensing include the discrete cosine transform (DCT) dictionary [12,13], the discrete wavelet transform (DWT) dictionary [9,14], and the K-SVD dictionary [15][16][17], Gabor dictionary [15], CDL dictionary [18], etc.…”

Section: ) Sparse Expressionmentioning

confidence: 99%

A Task-Driven Invertible Projection Matrix Learning Algorithm for Hyperspectral Compressed Sensing

Dai

¹

,

Liu

²

,

Wang

³

et al. 2021

View full text Add to dashboard Cite

The high complexity of the reconstruction algorithm is the main bottleneck of the hyperspectral image (HSI) compression technology based on compressed sensing. Compressed sensing technology is an important tool for retrieving the maximum number of HSI scenes on the ground. However, the complexity of the compressed sensing algorithm is limited by the energy and hardware of spaceborne equipment. Aiming at the high complexity of compressed sensing reconstruction algorithm and low reconstruction accuracy, an equivalent model of the invertible transformation is theoretically derived by us in the paper, which can convert the complex invertible projection training model into the coupled dictionary training model. Besides, aiming at the invertible projection training model, the most competitive task-driven invertible projection matrix learning algorithm (TIPML) is proposed. In TIPML, we don’t need to directly train the complex invertible projection model, but indirectly train the invertible projection model through the training of the coupled dictionary. In order to improve the accuracy of reconstructed data, in the paper, the singular value transformation is proposed. It has been verified that the concentration of the dictionary is increased and that the expressive ability of the dictionary has not been reduced by the transformation. Besides, two-loop iterative training is established to improve the accuracy of data reconstruction. Experiments show that, compared with the traditional compressed sensing algorithm, the compressed sensing algorithm based on TIPML has higher reconstruction accuracy, and the reconstruction time is shortened by more than a hundred times. It is foreseeable that the TIPML algorithm will have a huge application prospect in the field of HSI compression.

show abstract

Quantitative Comparison of EEG Compressed Sensing using Gabor and K-SVD Dictionaries

Cited by 7 publications

References 29 publications

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

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

A Task-Driven Invertible Projection Matrix Learning Algorithm for Hyperspectral Compressed Sensing

Contact Info

Product

Resources

About