A Multi-channel NIRS System for Prefrontal Mental Task Classification Employing Deep Forest Algorithm

Wen, Yizhen; Qi, Xiangao; Cui, Shaoyang; Chen, Cheng; Chen, Mingye; Zhao, Jian; Wang, Guoxing

doi:10.1109/biocas.2019.8919082

Cited by 10 publications

(3 citation statements)

References 8 publications

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“…The subjects of the experiment were four male adults aged from 22 to 25. The experiment included four different brain tasks: Mental arithmetic (MA), Digital Span (DS), Semantic (SM) and rest [13]. The modified Beer-Lambert Law (MBLL) will preprocess the original data, and then calculate the concentration changes of oxyhemoglobin (HbO 2 ) and deoxyhemoglobin (Hb) [14].…”

Section: A Binarized Neural Networkmentioning

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: A Binarized Neural Networkmentioning

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)

Self Cite

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“…It has several advantages, including the use of low-cost, silent, transportable/portable instrumentation, and the ability to move freely during the measurements [7][8][9]. Multi-channel and multi-wavelength NIRS is gradually being widely developed and applied due to its advantage of providing more signals and information than other comparable techniques [10][11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach to Evaluating Crosstalk for Near-Infrared Spectrometers

Chen,

Cao,

et al. 2024

Sensors

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Multi-channel and multi-parameter near-infrared spectroscopy (NIRS) has gradually become a new research direction and hot spot due to its ability to provide real-time, continuous, comprehensive indicators of multiple parameters. However, multi-channel and multi-parameter detection may lead to crosstalk between signals. There is still a lack of benchmarks for the evaluation of the reliability, sensitivity, stability and response consistency of the NIRS instruments. In this study, a set of test methods (a human blood model test, ink drop test, multi-channel crosstalk test and multi-parameter crosstalk test) for analyzing crosstalk and verifying the reliability of NIRS was conducted to test experimental verification on a multi-channel (8-channel), multi-parameter (4-parameter) NIRS instrument independently developed by our team. Results show that these tests can be used to analyze the signal crosstalk and verify the reliability, sensitivity, stability and response consistency of the NIRS instrument. This study contributes to the establishment of benchmarks for the NIRS instrument crosstalk and reliability testing. These novel tests have the potential to become the benchmark for NIRS instrument reliability testing.

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“…The proposed algorithm can also effectively solve the classical classification problem [7][8], when it is necessary to determine which previously defined class a pixel belongs to. In this case, the color/hue of a pixel can be considered as a data known part for the classification procedure implementation.…”

Section: Introductionmentioning

confidence: 99%

Improving the performance of the algorithm for distributing numerics by ranges

Strabykin,

Meltsov,

Krutikov

2023

E3S Web Conf.

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The paper considers an accelerated algorithm for distributing numbers over given ranges. The proposed algorithm allows you to quickly distribute integers over predetermined ranges with deterministic boundaries and calculate the number of elements in each of the ranges. If the number of ranges lies in the range from 64 to 256, then the time for solving the problem will be reduced by 9–33 times. The algorithm can be implemented in both software and hardware.

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A Multi-channel NIRS System for Prefrontal Mental Task Classification Employing Deep Forest Algorithm

Cited by 10 publications

References 8 publications

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

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

A Novel Approach to Evaluating Crosstalk for Near-Infrared Spectrometers

Improving the performance of the algorithm for distributing numerics by ranges

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