A Study on Sensitive Bands of EEG Data under Different Mental Workloads

Qu, Hongquan; Fan, Zhanli; Cao, Shuqin; Pang, Liping; Wang, Hao; Zhang, Jie

doi:10.3390/a12070145

Cited by 5 publications

(2 citation statements)

References 30 publications

(42 reference statements)

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“…This EEG model of gender difference can also predict gender in other activities. Qu et al [ 243 ] proposed a study in which the sensitive bands of EEG data were analyzed due to different physiological brain loads. ICA has been proposed for data processing.…”

Section: Bci Recent Advancementsmentioning

confidence: 99%

Electroencephalogram based brain-computer interface: Applications, challenges, and opportunities

Yadav

Maini

2023

Multimed Tools Appl

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Brain-Computer Interfaces (BCI) is an exciting and emerging research area for researchers and scientists. It is a suitable combination of software and hardware to operate any device mentally. This review emphasizes the significant stages in the BCI domain, current problems, and state-of-the-art findings. This article also covers how current results can contribute to new knowledge about BCI, an overview of BCI from its early developments to recent advancements, BCI applications, challenges, and future directions. The authors pointed to unresolved issues and expressed how BCI is valuable for analyzing the human brain. Humans' dependence on machines has led humankind into a new future where BCI can play an essential role in improving this modern world.

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Section: Bci Recent Advancementsmentioning

confidence: 99%

Electroencephalogram based brain-computer interface: Applications, challenges, and opportunities

Yadav

Maini

2023

Multimed Tools Appl

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“…That means we have a single electrode in the 'Fpz' location of the brain as denoted by the international 10-20 system for scalp electrode placement for EEG data acquisition [89]. To compute the topographic map, we consider a total EEG bandwidth of 0.5 to 80 Hz while for the five EEG frequency components, we have Delta (d) ¼ 0.5-4 Hz, Theta (h) = 4-8 Hz, Alpha (a) = 8-13 Hz, Beta (b) = 13-40 Hz, and Gamma (c) = 40-80 Hz [90,91]. We combine the EEG signals (ground truth, motion-corrupted, and estimated) from all 23 folds, calculate the bandpower and plot the topographic maps while keeping the same scale for all cases [92].…”

Section: Qualitative Evaluationmentioning

confidence: 99%

MLMRS-Net: Electroencephalography (EEG) motion artifacts removal using a multi-layer multi-resolution spatially pooled 1D signal reconstruction network

Mahmud

Hossain

Chowdhury

et al. 2022

Neural Comput & Applic

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Electroencephalogram (EEG) signals suffer substantially from motion artifacts when recorded in ambulatory settings utilizing wearable sensors. Because the diagnosis of many neurological diseases is heavily reliant on clean EEG data, it is critical to eliminate motion artifacts from motion-corrupted EEG signals using reliable and robust algorithms. Although a few deep learning-based models have been proposed for the removal of ocular, muscle, and cardiac artifacts from EEG data to the best of our knowledge, there is no attempt has been made in removing motion artifacts from motion-corrupted EEG signals: In this paper, a novel 1D convolutional neural network (CNN) called multi-layer multi-resolution spatially pooled (MLMRS) network for signal reconstruction is proposed for EEG motion artifact removal. The performance of the proposed model was compared with ten other 1D CNN models: FPN, LinkNet, UNet, UNet+, UNetPP, UNet3+, AttentionUNet, MultiResUNet, DenseInceptionUNet, and AttentionUNet++ in removing motion artifacts from motion-contaminated single-channel EEG signal. All the eleven deep CNN models are trained and tested using a single-channel benchmark EEG dataset containing 23 sets of motion-corrupted and reference ground truth EEG signals from PhysioNet. Leave-one-out cross-validation method was used in this work. The performance of the deep learning models is measured using three well-known performance matrices viz. mean absolute error (MAE)-based construction error, the difference in the signal-to-noise ratio (ΔSNR), and percentage reduction in motion artifacts (η). The proposed MLMRS-Net model has shown the best denoising performance, producing an average ΔSNR, η, and MAE values of 26.64 dB, 90.52%, and 0.056, respectively, for all 23 sets of EEG recordings. The results reported using the proposed model outperformed all the existing state-of-the-art techniques in terms of average η improvement.

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Study on EEG Channel Selection for Visual Manipulation Tasks

Liu

Pang

et al. 2021

Lecture Notes in Electrical Engineering

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A Study on Sensitive Bands of EEG Data under Different Mental Workloads

Cited by 5 publications

References 30 publications

Electroencephalogram based brain-computer interface: Applications, challenges, and opportunities

Electroencephalogram based brain-computer interface: Applications, challenges, and opportunities

MLMRS-Net: Electroencephalography (EEG) motion artifacts removal using a multi-layer multi-resolution spatially pooled 1D signal reconstruction network

Study on EEG Channel Selection for Visual Manipulation Tasks

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