Structure reconstruction of correlated neural signals based on inpainting for brain monitoring

Schmale, Sebastian; Knoop, Benjamin; Peters-Drolshagen, Dagmar; Paul, Steffen

doi:10.1109/biocas.2015.7348345

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…The applicability of structure-based inpainting [13] for neural signals has been shown in previous works [14] with suitable results.…”

Section: Introduction and Related Workmentioning

confidence: 79%

“…By comparing the compression/reconstruction schemes for neural raw data in resource-limited systems labeled by CR=Yes, the proposed inpainting approach outperforms CS reference framework in terms of NMSE. In addition, a structure-based inpainting has been applied from [14] consisting of a mask Ω o with overlapping rectangular patches to compress and recover neural raw data. In spite of compression ratio 62% less than 70%, the structure-based inpainting generates more than twice as the mean recovery error NMSE compared to the patch ordering inpainting algorithm.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…For the proposed inpainting based on patch ordering the enhancement of η only effects a small raise of the recovery error as expected, which yields benefits for high data compression ratios. The reconstruction results can be improved by increasing the spatial inter-electrode correlation of the neural signals due to correlation-based sorting of the neural array A in order to increase the image-related structure [4,14]. With respect to traditional data compression schemes like JPEG, 2 Discrete cosine transform (DCT) is used as basis in this paper [18].…”

Section: Simulation Resultsmentioning

confidence: 99%

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Compression and reconstruction methodology for neural signals based on patch ordering inpainting for brain monitoring

Schmale

Lange

Knoop

et al. 2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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The aim of this study is to present the first compression and reconstruction methodology based on patch ordering inpainting algorithm for monitoring neural activity. This novel inpainting approach is especially important for the technical realization of implantable neural measurement systems (NMS) since they are subject to strict resource limitations as area and energy consumption. Intersection masks with center square as well as random-based masks are utilized for suitable neural data compression considering the patch ordering inpainting. The proposed inpainting methodology outperforms the structure-based inpainting algorithm and often applied Compressed Sensing strategy with regard to reconstruction quality of the real measured neural signals. These algorithms focus on complexity reduction according to hardware on implantable NMS. At high degrees of compression, the patch ordering inpainting yields well-suited or equal reconstruction results in contrast to JPEG or JPEG2000, respectively.

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“…The applicability of structure-based inpainting [13] for neural signals has been shown in previous works [14] with suitable results.…”

Section: Introduction and Related Workmentioning

confidence: 79%

Section: Simulation Resultsmentioning

confidence: 99%

Section: Simulation Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Compression and reconstruction methodology for neural signals based on patch ordering inpainting for brain monitoring

Schmale

Lange

Knoop

et al. 2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Inpainting makes every sample count

Schmale

Paul

2017

2017 IEEE Biomedical Circuits and Systems Conference (BioCAS)

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Neurological activity monitoring based on video inpainting

Schmale

Seidel

Thiermann

et al. 2017

2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Inpainting-based compression and reconstruction methodology can be applied to systems with limited resources to enable continuously monitor neurological activity. In this work, an approach based on sparse representations and K-SVD is augmented to a video processing in order to improve the recovery quality. That was mainly achieved by using another direction of spatial correlation and the extraction of cuboids across frames. The implementation of overlapping frames between the recorded data blocks avoids rising errors at the boundaries during the inpainting-based recovery. Controlling the electrode states per frame plays a key role for high data compression and precise recovery. The proposed 3D inpainting approach can compete with common methods like JPEG, JPEG2000 or MPEG-4 in terms of the degree of compression and reconstruction accuracy, which was applied on real measured local field potentials of a human patient.

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Structure reconstruction of correlated neural signals based on inpainting for brain monitoring

Cited by 6 publications

References 11 publications

Compression and reconstruction methodology for neural signals based on patch ordering inpainting for brain monitoring

Compression and reconstruction methodology for neural signals based on patch ordering inpainting for brain monitoring

Inpainting makes every sample count

Neurological activity monitoring based on video inpainting

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