Wireless EEG System Achieving High Throughput and Reduced Energy Consumption Through Lossless and Near-Lossless Compression

Álvarez, Guillermo Dufort y; Favaro, Federico; Lecumberry, Federico; Martín, Álvaro; Oliver, Juan Pablo; Oreggioni, Julian; Ramírez, Ignacio; Seroussi, G.; Steinfeld, Leonardo

doi:10.1109/tbcas.2017.2779324

Cited by 27 publications

(5 citation statements)

References 34 publications

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“…Lossless compression [14]- [18] guarantees no degradation between the original and reconstructed bioelectric signals. In contrast to lossless compression, nearlossless compression [15,19]- [22] uses quantization, i.e. lossy operation, for efficient compression.…”

Section: A Bioelectric Signal Compressionmentioning

confidence: 99%

“…However, it limits the quantization distortion according to the given error values. The lossless and near-lossless compression schemes can be divided into predictive coding [15,17]- [19,22]- [24] and transform coding [14,21,25]. Predictive coding first fits the measured bioelectric signals to the past signals using a predictor such as Markov chains, linear prediction, or artificial neural networks (ANN).…”

Section: A Bioelectric Signal Compressionmentioning

confidence: 99%

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Graph-Based EEG Signal Compression for Human–Machine Interaction

Fujihashi,

Koike-Akino

2024

IEEE Access

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Communication of bioelectric signals, such as electroencephalography (EEG) signals, will be a key technology for smooth interaction between users and remote robots. The existing solutions use an orthogonal transform for EEG signal compression, such as Discrete Wavelet Transform (DWT) or Discrete Cosine Transform (DCT). This paper proposes a graph-based compression scheme for EEG signals to improve the quality at the given rate. The proposed scheme constructs a graph from the positions of the EEG sensors and adopts parameterized graph shift operators to obtain the graph basis functions for decorrelating the EEG signals. Graph Fourier Transform (GFT) based on the graph basis functions with the combination of quantization and entropy coding can send high quality EEG signals with fewer bits. Evaluations using the EEG signal dataset show that the proposed GFT-based compression can send better quality EEG signals than the existing DCT-based and DWT-based schemes at the same bit rates. In addition, an optimal parameter of the graph shift operator under the given rate is discussed to maximize the reconstruction quality of the graph-based scheme.

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Section: A Bioelectric Signal Compressionmentioning

confidence: 99%

Section: A Bioelectric Signal Compressionmentioning

confidence: 99%

Graph-Based EEG Signal Compression for Human–Machine Interaction

Fujihashi,

Koike-Akino

2024

IEEE Access

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“…Therefore, the non-invasive EEG systems are the most commonly used in BCI-assisted stroke rehabilitation systems that are based on either gel [123][124][125] or dry electrodes [126,127]. However, the current EEG systems [128][129][130][131] are heavy, bulky, and contain rigid hardware components, hence, not suitable for longterm mobile EEG monitoring on a daily basis. Thus, to fill this gap and to make EEG recording comfortable and feasible for day-to-day use, FE has stepped into the field of developing 'Flexible EEG Systems/ Electrodes' .…”

Section: Brain Signals Acquisition Systems/electrodesmentioning

confidence: 99%

Emergence of flexible technology in developing advanced systems for post-stroke rehabilitation: a comprehensive review

et al. 2021

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Objective. Stroke is one of the most common neural disorders, which causes physical disabilities and motor impairments among its survivors. Several technologies have been developed for providing stroke rehabilitation and to assist the survivors in performing their daily life activities. Currently, the use of flexible technology (FT) for stroke rehabilitation systems is on a rise that allows the development of more compact and lightweight wearable systems, which stroke survivors can easily use for long-term activities. Approach. For stroke applications, FT mainly includes the ‘flexible/stretchable electronics’, ‘e-textile (electronic textile)’ and ‘soft robotics’. Thus, a thorough literature review has been performed to report the practical implementation of FT for post-stroke application. Main results. In this review, the highlights of the advancement of FT in stroke rehabilitation systems are dealt with. Such systems mainly involve the ‘biosignal acquisition unit’, ‘rehabilitation devices’ and ‘assistive systems’. In terms of biosignals acquisition, electroencephalography and electromyography are comprehensively described. For rehabilitation/assistive systems, the application of functional electrical stimulation and robotics units (exoskeleton, orthosis, etc) have been explained. Significance. This is the first review article that compiles the different studies regarding FT based post-stroke systems. Furthermore, the technological advantages, limitations, and possible future implications are also discussed to help improve and advance the flexible systems for the betterment of the stroke community.

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“…Shaw et al proposed a compression method for multichannel EEG data with improved storage capacity and efficiency in transmission. A lossless and near-lossless algorithm of EEG compression was presented in [15]. The literature proposed two modes of compression for different platform with individual capability.…”

Section: Literature Reviewmentioning

confidence: 99%

VLSI Implementation of an Efficient Lossless EEG Compression Design for Wireless Body Area Network

Chen

Abu

et al. 2018

Applied Sciences

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Data transmission of electroencephalography (EEG) signals over Wireless Body Area Network (WBAN) is currently a widely used system that comes together with challenges in terms of efficiency and effectivity. In this study, an effective Very-Large-Scale Integration (VLSI) circuit design of lossless EEG compression circuit is proposed to increase both efficiency and effectivity of EEG signal transmission over WBAN. The proposed design was realized based on a novel lossless compression algorithm which consists of an adaptive fuzzy predictor, a voting-based scheme and a tri-stage entropy encoder. The tri-stage entropy encoder is composed of a two-stage Huffman and Golomb-Rice encoders with static coding table using basic comparator and multiplexer components. A pipelining technique was incorporated to enhance the performance of the proposed design. The proposed design was fabricated using a 0.18 μm CMOS technology containing 8405 gates with 2.58 mW simulated power consumption under an operating condition of 100 MHz clock speed. The CHB-MIT Scalp EEG Database was used to test the performance of the proposed technique in terms of compression rate which yielded an average value of 2.35 for 23 channels. Compared with previously proposed hardware-oriented lossless EEG compression designs, this work provided a 14.6% increase in compression rate with a 37.3% reduction in hardware cost while maintaining a low system complexity.

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Wireless EEG System Achieving High Throughput and Reduced Energy Consumption Through Lossless and Near-Lossless Compression

Cited by 27 publications

References 34 publications

Graph-Based EEG Signal Compression for Human–Machine Interaction

Graph-Based EEG Signal Compression for Human–Machine Interaction

Emergence of flexible technology in developing advanced systems for post-stroke rehabilitation: a comprehensive review

VLSI Implementation of an Efficient Lossless EEG Compression Design for Wireless Body Area Network

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