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- W4300644791 abstract "Classification of mental workload has always been considered a crucial task in the literature related to brain mem-ory. People perform various tasks and have multiple cognitive workloads. This mental workload can be sensed in a non-intrusive way using Functional near-infrared spectroscopy (fNIRS) sig-nals. fNIRS is a photosensitive brain examining method which uses near-infrared spectroscopy to measure aspects of brain functions and activities. In this work, we focus on classifying segmented mental workload from fNIRS signals. We propose a deep convolutional neural (DCNN) network to classify mental workload. We evaluate our model performance using the publicly available large-scale open-access dataset, “Tufts fNIRS to Mental Workload (fNIRS2MW)” that consists of 68 participants per-forming n-back tasks where increased n represents the intensity of the mental workload. Our proposed deep convolutional neural network (DCNN) comprises six convolutional layers. Our DCNN achieves a performance gain of 28 % and 4 % comparing the state-of-the-art models EEGnet and Deep ConvNet, respectively." @default.
- W4300644791 created "2022-10-03" @default.
- W4300644791 creator A5046195190 @default.
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- W4300644791 date "2022-06-01" @default.
- W4300644791 modified "2023-09-23" @default.
- W4300644791 title "Mental workload classification from non-invasive fNIRs signals through deep convolutional neural network" @default.
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- W4300644791 doi "https://doi.org/10.1109/compsac54236.2022.00230" @default.
- W4300644791 hasPublicationYear "2022" @default.
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