Extensive use of electroencephalogram (EEG) signals in diversified fields has put in a lot of thrust in research for devices capable of operating at constrained power and storage levels. In this paper, a simple and novel method for compression of multichannel EEG (MCEEG) signal is proposed. Here, wave atom transform of MCEEG data followed by quantization, thresholding, and arithmetic coding of context adaptive residuals and threshold coefficients is performed to achieve compression with good signal quality. The proposed method has been tested on a wide range of publicly available databases and results indicate that the algorithm is able to achieve good signal compression without degrading the signal quality. The proposed system provides an average compression ratio of 14.01 with a percentage root mean square difference of 1.91% across different data sets. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.

Sudhakar M S

School of Electronics Engineering

Vellore Campus

Titus G

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Multidimensional Systems and Signal Processing

Popularisation of electroencephalograph (EEG) signals in diversified fields have increased the need for devices capable of operating at lower power and storage requirements. This has led to a great deal of research in data compression, that can address (a) low latency in the coding of the signal, (b) reduced hardware and software dependencies, (c) quantify the system anomalies, and (d) effectively reconstruct the compressed signal. This paper proposes a computationally simple and novel coding scheme named spatial pseudo codec (SPC), to achieve lossy to near lossless compression of multichannel EEG (MCEEG). In the proposed system, MCEEG signals are initially normalized, followed by two parallel processes: one operating on integer part and the other, on fractional part of the normalized data. The redundancies in integer part are exploited using spatial domain encoder, and the fractional part is coded as pseudo integers. The proposed method has been tested on a wide range of databases having variable sampling rates and resolutions. Results indicate that the algorithm has a good recovery performance with an average percentage root mean square deviation (PRD) of 2.72 for an average compression ratio (CR) of 3.16. Furthermore, the algorithm has a complexity of only O(n) with an average encoding and decoding time per sample of 0.3 ms and 0.04 ms respectively. The performance of the algorithm is comparable with recent methods like fast discrete cosine transform (fDCT) and tensor decomposition methods. The results validated the feasibility of the proposed compression scheme for practical MCEEG recording, archiving and brain computer interfacing systems. © 2017, Australasian College of Physical Scientists and Engineers in Medicine.

Australasian Physical & Engineering Sciences in Medicine

A simple and efficient algorithm operating with linear time for MCEEG data compression

Biomedical Signal Processing and Control

Efficient lossless multi-channel EEG compression based on channel clustering

Multichannel EEG compression based on ICA and SPIHT

Computer Science and Information Systems

Fast DCT algorithms for EEG data compression in embedded systems

IEEE Journal of Biomedical and Health Informatics

Wearable, Wireless EEG Solutions in Daily Life Applications: What are we Missing?

Context adaptive residual coding for efficient compression of MCEEG employing wave atom transforms

Journal	Data powered by TypesetMultidimensional Systems and Signal Processing
Publisher	Data powered by TypesetSpringer Science and Business Media LLC
ISSN	0923-6082
Open Access	No