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• W3158077007 abstract "Functional near-infrared spectroscopy (fNIRS) is considered as a non-invasive and effective brain-computer interface technology. Wearable high-resolution fNIRS requires a large-scale 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." @default.
• W3158077007 created "2021-05-10" @default.
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• W3158077007 date "2021-05-01" @default.
• W3158077007 modified "2023-09-24" @default.
• W3158077007 title "An Energy Efficient Functional near Infrared Spectroscopy System Employing Spatial Adaptive Sampling Technique" @default.
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• W3158077007 doi "https://doi.org/10.1109/iscas51556.2021.9401431" @default.
• W3158077007 hasPublicationYear "2021" @default.
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