Abstract
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.
Highlights
As the fourth industrial revolution has accelerated, big data is growing in the field of healthcare, and the field of neural engineering is no exception
As discussed in earlier sections, a number of compressive sensing (CS) algorithms are being actively pursued in the field of neural engineering, to satisfy the constraints of many practical brain–computer interfaces (BCIs) applications it is essential to minimize the computational complexity of the CS reconstruction algorithm
The CS framework can help in dealing with many challenges that current BCIs may encounter, which requires the use of fast, long-term, and energy-saving computational approaches
Summary
As the fourth industrial revolution has accelerated, big data is growing in the field of healthcare, and the field of neural engineering is no exception. 20 min of neural activity in a mouse brain recording produces about 500 petabytes of flickering data [3] In another brain study, the brain needed a few terabytes of images to reconstruct 1000 nerve cells (less than 1% of total cells) from the Drosophila brain, and researchers spent a decade to collect data from 60,000 neurons at a rate of 1 gigabyte per cell. Various data compression methods have been used for effective storage and transmission of multichannel electroencephalography (EEG) signals including, discrete cosine transform (DCT) based, discrete wavelet transform matrix (DWT) based, and run-length encoding These methods need two independent steps, which are signal acquisition followed by compression.
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