System considerations for the compressive sampling of EEG and ECoG bio-signals

Gangopadhyay, Daibashish; Allstot, Emily G.; Dixon, Anna M. R.; Allstot, D.J.

doi:10.1109/biocas.2011.6107744

Cited by 10 publications

(8 citation statements)

References 6 publications

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“…Here it used the same block partition as BSBL-BO. The second was an ℓ 1 algorithm used in [4] to recover EEG. The parameters of the two algorithms were tuned for optimal results.…”

Section: A Experiments 1: Compressed Sensing With Dctmentioning

confidence: 99%

“…It has been shown in [3] that compared to wavelet compression, Compressed Sensing (CS), when using sparse binary matrices as its sensing matrices, can reduce energy consumption while achieving competitive data compression ratio. Besides, the use of sparse binary matrices means the device cost can be largely reduced [3], [4]. However, current CS algorithms only work well for sparse signals or signals with sparse representation coefficients in some transformed domains (e.g., the wavelet domain).…”

Section: Introductionmentioning

confidence: 99%

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Compressed Sensing of EEG for Wireless Telemonitoring With Low Energy Consumption and Inexpensive Hardware

Zhang

Jung

Makeig

et al. 2013

IEEE Trans. Biomed. Eng.

234

224

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Telemonitoring of electroencephalogram (EEG) through wireless body-area networks is an evolving direction in personalized medicine. Among various constraints in designing such a system, three important constraints are energy consumption, data compression, and device cost. Conventional data compression methodologies, although effective in data compression, consumes significant energy and cannot reduce device cost. Compressed sensing (CS), as an emerging data compression methodology, is promising in catering to these constraints. However, EEG is nonsparse in the time domain and also nonsparse in transformed domains (such as the wavelet domain). Therefore, it is extremely difficult for current CS algorithms to recover EEG with the quality that satisfies the requirements of clinical diagnosis and engineering applications. Recently, block sparse Bayesian learning (BSBL) was proposed as a new method to the CS problem. This study introduces the technique to the telemonitoring of EEG. Experimental results show that its recovery quality is better than state-of-the-art CS algorithms, and sufficient for practical use. These results suggest that BSBL is very promising for telemonitoring of EEG and other nonsparse physiological signals.

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“…Here it used the same block partition as BSBL-BO. The second was an ℓ 1 algorithm used in [4] to recover EEG. The parameters of the two algorithms were tuned for optimal results.…”

Section: A Experiments 1: Compressed Sensing With Dctmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Compressed Sensing of EEG for Wireless Telemonitoring With Low Energy Consumption and Inexpensive Hardware

Zhang

Jung

Makeig

et al. 2013

IEEE Trans. Biomed. Eng.

234

224

View full text Add to dashboard Cite

show abstract

“…The author argued that chirped Gabor dictionary would be very efficient and it can increase the sparsity of the signals; hence, it improves the performance of CS-based EEG monitoring. On the other hand, Gangopadhyay et al claimed in [14] that wavelet-based dictionaries are more suitable for CS-based EEG compression than the previously mentioned approaches. Author in [11] have provided a detailed performance study for six different sparsifying dictionaries, namely, Gabor, Mexican Hat, cubic Spline, linear Spline, cubic B-Spline, and linear B-Spline.…”

Section: Cs-based Eeg Compressionmentioning

confidence: 99%

“…Thus, a great attention was dedicated to providing dictionaries and basis that render a high sparse representation of EEG signals. Subsequently, several dictionaries have been investigated in the literature such as Slepian basis, Gabor frames, and DWT matrices [12][13][14][15]28]. In [12], Senay et al quantified a CS framework for EEG compression using Slepian functions as a sparsifying dictionary.…”

Section: Cs-based Eeg Compressionmentioning

confidence: 99%

“…Senay et al have quantified the use of Slepian basis as sparsifying basis for EEG [12]; the obtained result shows a low error rate for the reconstructed EEG signal. In addition, Aviyente has presented a CS-based EEG compression system exploiting Gabor frame as dictionary for EEG signals [13], whereas Gangopadhyay et al [14] have found that adopting a wavelet transform for EEG is more efficient in terms of quality of reconstruction. Zhang et al presented in [15] a block sparse Bayesian learning (BSBL) approach to recover EEG raw data that enable both good reconstruction quality and low system complexity by using sparse sensing matrices and wavelet matrices as the sparsifying basis.…”

Section: Introductionmentioning

confidence: 99%

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An Adaptive Joint Sparsity Recovery for Compressive Sensing Based EEG System

Djelouat

Baali

Amira

et al. 2017

Wireless Communications and Mobile Computing

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The last decade has witnessed tremendous efforts to shape the Internet of things (IoT) platforms to be well suited for healthcare applications. These platforms are comprised of a network of wireless sensors to monitor several physical and physiological quantities. For instance, long-term monitoring of brain activities using wearable electroencephalogram (EEG) sensors is widely exploited in the clinical diagnosis of epileptic seizures and sleeping disorders. However, the deployment of such platforms is challenged by the high power consumption and system complexity. Energy efficiency can be achieved by exploring efficient compression techniques such as compressive sensing (CS). CS is an emerging theory that enables a compressed acquisition using well-designed sensing matrices. Moreover, system complexity can be optimized by using hardware friendly structured sensing matrices. This paper quantifies the performance of a CS-based multichannel EEG monitoring. In addition, the paper exploits the joint sparsity of multichannel EEG using subspace pursuit (SP) algorithm as well as a designed sparsifying basis in order to improve the reconstruction quality. Furthermore, the paper proposes a modification to the SP algorithm based on an adaptive selection approach to further improve the performance in terms of reconstruction quality, execution time, and the robustness of the recovery process.

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