About this Research Topic
Compressive sensing (CS) is a novel signal processing technique that offers sub-Nyquist acquisition and accurate data reconstruction under sparsity constraint. Areas that require fast data acquisition and data reconstruction, could rapidly benefit from CS. So far CS has proven beneficial in neuroscience research and medical imaging fields. It has been successfully employed for brain activity recovery such as event-related potential, blood oxygenation level-dependent (BOLD), and task-related fMRI. Compressive sensing has been further implemented for sparse brain network recovery, diffusion tensor and spectrum imaging, and cortical mapping in diffuse optical tomography.
Image reconstruction based on compressive sensing has been recently incorporated into a commercially available MRI device, implemented by a decreased (halved) acquisition time, still maintaining image quality.
Sparse-inducing techniques' benefits go beyond data acquisition, extending from compression to pattern classification, and deep learning.
A number of research works adopted compressive sensing for EEG source localization, compression of EEG signals, MRI quantification, fNIRS data denoising, X-ray dose-reduction, motion-artifact mitigation, and ultrasound imaging.
This Research Topic focuses on recent advances in compressive sensing to study brain signals.
The aim is to collect experimental research studies implementing existing methods or suggesting novel techniques for sparse representation of neuronal data. Of particular interest is the use of deep neural networks and compressive sensing for the acquisition, processing, and classification of brain signals.
Our goal is to present new solutions to improve the imaging technologies toward efficiency, accuracy, and user-friendliness, benefited to the patients and clinicians and to introduce open research questions applicable to brain functioning, and diagnosis and treatment of brain disorders. This includes any step from acquisition, pre-processing, to computational tools and brain-computer interfaces, relevant to compressive sensing theory.
Implementation challenges and the application of sparse representation for biosignals’ multi-modal data fusion and analysis are also welcome.
Topics to be addressed include, but are not limited to, compressive sensing and/or dictionary learning for:
• Magnetic resonance spectroscopy (MRS)
• (functional) magnetic resonance imaging f/MRI
• Functional near-infrared spectroscopy (fNIRS)
• Cardiac magnetic resonance (CMR)
• Electromyography and rehabilitation
• Computational neuroscience
• Brain connectivity
• Deep neural network
• Electroencephalography (EEG)
• Electrical impedance tomography
• Brain-computer interface (BCI)
• Spike event detection
• Epileptic and epileptiform events detection
Keywords: Compressive sensing, Sparse representation, Rapid imaging, Brain rhythms, Neural systems
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
