Trends in Compressive Sensing for EEG Signal Processing Applications
The tremendous progress of big data acquisition and processing in the field of neural engineering has enabled a better understanding of the patient’s brain disorders with their neural rehabilitation, restoration, detection, and diagnosis. An integration of compressive sensing (CS) and neural enginee...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/13/3703 |
| _version_ | 1797563506733613056 |
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| author | Dharmendra Gurve Denis Delisle-Rodriguez Teodiano Bastos-Filho Sridhar Krishnan |
| author_facet | Dharmendra Gurve Denis Delisle-Rodriguez Teodiano Bastos-Filho Sridhar Krishnan |
| author_sort | Dharmendra Gurve |
| collection | DOAJ |
| description | The tremendous progress of big data acquisition and processing in the field of neural engineering has enabled a better understanding of the patient’s brain disorders with their neural rehabilitation, restoration, detection, and diagnosis. An integration of compressive sensing (CS) and neural engineering emerges as a new research area, aiming to deal with a large volume of neurological data for fast speed, long-term, and energy-saving purposes. Furthermore, electroencephalography (EEG) signals for brain–computer interfaces (BCIs) have shown to be very promising, with diverse neuroscience applications. In this review, we focused on EEG-based approaches which have benefited from CS in achieving fast and energy-saving solutions. In particular, we examine the current practices, scientific opportunities, and challenges of CS in the growing field of BCIs. We emphasized on summarizing major CS reconstruction algorithms, the sparse basis, and the measurement matrix used in CS to process the EEG signal. This literature review suggests that the selection of a suitable reconstruction algorithm, sparse basis, and measurement matrix can help to improve the performance of current CS-based EEG studies. In this paper, we also aim at providing an overview of the reconstruction free CS approach and the related literature in the field. Finally, we discuss the opportunities and challenges that arise from pushing the integration of the CS framework for BCI applications. |
| first_indexed | 2024-03-10T18:43:39Z |
| format | Article |
| id | doaj.art-7f57532462964b92abef26b0523a5e4a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T18:43:39Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-7f57532462964b92abef26b0523a5e4a2023-11-20T05:38:51ZengMDPI AGSensors1424-82202020-07-012013370310.3390/s20133703Trends in Compressive Sensing for EEG Signal Processing ApplicationsDharmendra Gurve0Denis Delisle-Rodriguez1Teodiano Bastos-Filho2Sridhar Krishnan3Department of Electrical, Computer, and Biomedical Engineering, Ryerson University, Toronto, ON M5B 2K3, CanadaPostgraduate Program in Electrical Engineering, Federal University of Espirito Santo, Vitoria 29075-910, BrazilPostgraduate Program in Electrical Engineering, Federal University of Espirito Santo, Vitoria 29075-910, BrazilDepartment of Electrical, Computer, and Biomedical Engineering, Ryerson University, Toronto, ON M5B 2K3, CanadaThe tremendous progress of big data acquisition and processing in the field of neural engineering has enabled a better understanding of the patient’s brain disorders with their neural rehabilitation, restoration, detection, and diagnosis. An integration of compressive sensing (CS) and neural engineering emerges as a new research area, aiming to deal with a large volume of neurological data for fast speed, long-term, and energy-saving purposes. Furthermore, electroencephalography (EEG) signals for brain–computer interfaces (BCIs) have shown to be very promising, with diverse neuroscience applications. In this review, we focused on EEG-based approaches which have benefited from CS in achieving fast and energy-saving solutions. In particular, we examine the current practices, scientific opportunities, and challenges of CS in the growing field of BCIs. We emphasized on summarizing major CS reconstruction algorithms, the sparse basis, and the measurement matrix used in CS to process the EEG signal. This literature review suggests that the selection of a suitable reconstruction algorithm, sparse basis, and measurement matrix can help to improve the performance of current CS-based EEG studies. In this paper, we also aim at providing an overview of the reconstruction free CS approach and the related literature in the field. Finally, we discuss the opportunities and challenges that arise from pushing the integration of the CS framework for BCI applications.https://www.mdpi.com/1424-8220/20/13/3703compressive sensingEEGlow power BCIsneurofeedbackassistive technologysampling |
| spellingShingle | Dharmendra Gurve Denis Delisle-Rodriguez Teodiano Bastos-Filho Sridhar Krishnan Trends in Compressive Sensing for EEG Signal Processing Applications Sensors compressive sensing EEG low power BCIs neurofeedback assistive technology sampling |
| title | Trends in Compressive Sensing for EEG Signal Processing Applications |
| title_full | Trends in Compressive Sensing for EEG Signal Processing Applications |
| title_fullStr | Trends in Compressive Sensing for EEG Signal Processing Applications |
| title_full_unstemmed | Trends in Compressive Sensing for EEG Signal Processing Applications |
| title_short | Trends in Compressive Sensing for EEG Signal Processing Applications |
| title_sort | trends in compressive sensing for eeg signal processing applications |
| topic | compressive sensing EEG low power BCIs neurofeedback assistive technology sampling |
| url | https://www.mdpi.com/1424-8220/20/13/3703 |
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