A 665μW silicon photomultiplier-based NIRS/EEG/EIT monitoring asic for wearable functional brain imaging

Xu, Jiawei; Konijnenburg, Mario; Lukita, Budi; Song, Shunfeng; Ha, Hyunsoo; Wegberg, Roland van; Sheikhi, E.; Mazzillo, M.; Fallica, Giorgio; Raedt, W. De; Hoof, Chris Van; Helleputte, Nick Van

doi:10.1109/isscc.2018.8310300

Cited by 10 publications

(1 citation statement)

References 4 publications

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“…How to reduce the power consumption while maintaining high resolution becomes a key issue for wearable NIRS system. As yet, there are a lot of researches in reducing the power consumption of the source, detector and the light-todigital converter circuit of fNIRS system, and it has even been reduced to sub-mW [6]- [9]. These researches focus on reducing the current and voltage of the light source array and photoelectric sensor.…”

Section: Introductionmentioning

confidence: 99%

An Energy Efficient Functional near Infrared Spectroscopy System Employing Spatial Adaptive Sampling Technique

Liu

Zhou

Cheng

et al. 2021

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

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Functional near-infrared spectroscopy (fNIRS) is considered as a non-invasive and effective brain-computer interface technology. Wearable high-resolution fNIRS requires a largescale LED array, which consumes a lot of power, and shorten the battery life. This paper proposes a spatial adaptive sampling (SAS) method that can take advantage of the spatial sparsity of fNIRS devices and greatly reduce the power consumption while maintaining high image quality. To improve the performance of the proposed SAS technique, a low power binary neural network (BNN) is proposed to accurately predict the current brain task. And the optimal dynamic LED pattern for each brain task is investigated. The proposed SAS technique is validate through an off-line experiment, it can reduce the power consumption of the LED array by 62.5% compared to not using SAS technology while maintaining a PSNR (Peak Signal to Noise Ratio) of 33 dB.Index Terms-Functional near-infrared spectroscopy (fNIRS), power consumption, binary neural network (BNN), spatial adaptive sampling (SAS)

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Section: Introductionmentioning

confidence: 99%

An Energy Efficient Functional near Infrared Spectroscopy System Employing Spatial Adaptive Sampling Technique

Liu

Zhou

Cheng

et al. 2021

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

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Wearable EEG and beyond

2019

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The electroencephalogram (EEG) is a widely used non-invasive method for monitoring the brain. It is based upon placing conductive electrodes on the scalp which measure the small electrical potentials that arise outside of the head due to neuronal action within the brain. Historically this has been a large and bulky technology, restricted to the monitoring of subjects in a lab or clinic while they are stationary. Over the last decade much research effort has been put into the creation of "wearable EEG" which overcomes these limitations and allows the long term non-invasive recording of brain signals while people are out of the lab and moving about. This paper reviews the recent progress in this field, with particular emphasis on the electrodes used to make connections to the head and the physical EEG hardware. The emergence of conformal "tattoo" type EEG electrodes is highlighted as a key next step for giving very small and socially discrete units. In addition, new recommendations for the performance validation of novel electrode technologies are given, with standards in this area seen as the current main bottleneck to the wider take up of wearable EEG. The paper concludes by considering the next steps in the creation of next generation wearable EEG units, showing that a wide range of research avenues are present.

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