Intelligent Content-Aware Model-Free Low Power Evoked Neural Signal Compression

Chung, Chen Han; Kao, Yu-Chieh Jill; Chen, Liang-Gee; Jaw, Fu‐Shan

doi:10.1007/978-3-540-89796-5_109

Cited by 3 publications

(5 citation statements)

References 7 publications

(9 reference statements)

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“…The prototype is successfully tested on the sampled Rhesus's motor cortex signals, achieving a compression ratio of 17.7% with SNR values 36.6dB and preserving 92% spikes. The result is remarkable compared with other biomedical signal compression methods, which achieves a SNR of 15-26dB and compression ratio of 1%-20% without consideration of the significant spike signal [7], [8], [13].…”

Section: Discussionmentioning

confidence: 87%

“…However, this compensates for the loss of 25% of the spikes, which is not desirable for future analysis. For the same recorded data of rat's S1 response, Chen et al's result achieved Signal to Noise Ratio (SNR) at about 25db with compression ratio larger than 25% [7] by adaptively qualification, in which both compression ratio and signal quality is not guaranteed perfectly. To improve their work in terms of the second point of view, Chen et al [8] take advantage of correlation between channels, achieving 5% compression ratio with SNR at 25db by a video compression method.…”

Section: Introductionmentioning

confidence: 87%

“…For clearer demonstration, this algorithm considers compression applied on one channel, compression between different channels are independent according to the third character exploited in section II. After dividing the signal into blocks, we firstly calculate the Quantization Table (QT ) for a sample(line [3][4][5][6][7][8], where F (i) is the DCT coefficients of X (i) and low (i) is the indices of Low Amplitude Components Lc. Then each block sample is compressed with the Dual-Phase Encoding(Line 9-18).…”

Section: ) Boundary Between Hybrid Lossless Encodingmentioning

confidence: 99%

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High-fidelity compression of extracellular recordings from motor cortex

Zhang

Pan

Wang

et al. 2014

2014 International Joint Conference on Neural Networks (IJCNN)

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In invasive brain-machine interfaces (BMI), the recorded high-quality neural signals produce a large data volume. This calls for effective compression. In this paper, we focus on extracellular recording of motor cortex. First the characteristics of the signals are studied, one of which is that peaks of DCT coefficients at high frequency may correspond to spike firing patterns. Based on these characteristics, we propose a high-fidelity compression framework for these signals. The DCT coefficients of the signal are divided into two parts according to amplitude, rather than frequency. The LowAmplitude-Component (LAC) is encoded by a phase called Symbol Encoding, which helps to reduce overall distortion. The High-Amplitude-Component (HAC), containing major information and spikes, is encoded by another phase called Hybrid Encoding. It combines the Huffman encoding and a novel Zero-Length-Encoding. Experiments show that the algorithm achieves a compression ratio of 18% without obvious distortion. Moreover, spikes are reserved more than 92%, outperforming existing work. Our algorithm enables low-cost storage devices to store long-time neural signals.

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Section: Discussionmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 87%

Section: ) Boundary Between Hybrid Lossless Encodingmentioning

confidence: 99%

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High-fidelity compression of extracellular recordings from motor cortex

Zhang

Pan

Wang

et al. 2014

2014 International Joint Conference on Neural Networks (IJCNN)

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“…The algorithm is based on the algorithm used for the successful compression of single-channel data in a previous work [7].…”

Section: Pcmentioning

confidence: 99%

Multichannel evoked neural signal compression using advanced video compression algorithm

Chung

Chen

Kao

et al. 2009

2009 4th International IEEE/EMBS Conference on Neural Engineering

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Abstract-Multichannel neural recording is one of the most important topics in the field of biomedical engineering. This is because there is a need to considerably reduce large amounts of data without degrading the data quality for easy transfer through wireless transmission. Video compression technology is of considerable importance in the field of signal processing. There are many similarities between multichannel neural signals and video signals. In this study, we propose a signal compression method that employs motion vectors (MVs) to reduce the redundancy between successive video frames and between successive channels. The method shows a signal-to-noise (error) ratio (SNR) of 25 db and data are compressed to 5% of their original size.

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“…This paper presents a novel algorithm that compresses data for multichannel neural signals to only 5% of the original data amount and maintains the complete waveform of the signal at 978-1-4244-2902-8/09/$25.00 ©2009 IEEE low power. The algorithm is based on our successful singlechannel result [7].…”

Section: Introductionmentioning

confidence: 99%

Optimal Transform of Multichannel Evoked Neural Signals Using a Video Compression Algorithm

Chung

Chen

Kao

et al. 2009

2009 3rd International Conference on Bioinformatics and Biomedical Engineering

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One of the most important problems in the field of biomedical engineering is how to record a multichannel neural signal. This problem arises because recording produces a large amount of data that must be reduced to transfer it through wireless transmission, and data reduction must be made without compromising data quality. Video compression technology is very important in the field of signal processing, and there are many similarities between multichannel neural signals and video signals. Therefore, we use motion vectors (MVs) to reduce the redundancy between successive video frames and successive channels. We also test what transform for neural signal compression is best. Our novel signal compression method gives a signal-to-noise ratio (SNR) of 25 db and compresses data to 5% of the original signal.

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Intelligent Content-Aware Model-Free Low Power Evoked Neural Signal Compression

Cited by 3 publications

References 7 publications

High-fidelity compression of extracellular recordings from motor cortex

High-fidelity compression of extracellular recordings from motor cortex

Multichannel evoked neural signal compression using advanced video compression algorithm

Optimal Transform of Multichannel Evoked Neural Signals Using a Video Compression Algorithm

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