Improved energy efficient design in software defined wireless electroencephalography sensor networks (WESN) using distributed architecture to remove artifact

Manojprabu, M.; Dhulipala, V. R. Sarma

doi:10.1016/j.comcom.2019.12.056

Cited by 7 publications

(2 citation statements)

References 18 publications

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“…Contrary to conventional algorithms, machine learning-based approaches have gained due attention, particularly for their ability to identify artifacts. To attenuate eye blink artifacts, a multichannel Weighted Weiner filter has been proposed (Manojprabu and Sarma Dhulipala, 2020), where Hierarchical Fully Connected Topology (HFCT) and Ad-hoc Nearest-Neighbor Topology (ANNT) are utilized. The proposed approach provides 5% better results for artifact attenuation when compared with the other existing approaches like PCA and ICA.…”

Section: Hardware Technology For Eeg Signal Acquisitionmentioning

confidence: 99%

Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review

et al. 2020

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Brain-Computer Interface (BCI), in essence, aims at controlling different assistive devices through the utilization of brain waves. It is worth noting that the application of BCI is not limited to medical applications, and hence, the research in this field has gained due attention. Moreover, the significant number of related publications over the past two decades further indicates the consistent improvements and breakthroughs that have been made in this particular field. Nonetheless, it is also worth mentioning that with these improvements, new challenges are constantly discovered. This article provides a comprehensive review of the state-of-the-art of a complete BCI system. First, a brief overview of electroencephalogram (EEG)-based BCI systems is given. Secondly, a considerable number of popular BCI applications are reviewed in terms of electrophysiological control signals, feature extraction, classification algorithms, and performance evaluation metrics. Finally, the challenges to the recent BCI systems are discussed, and possible solutions to mitigate the issues are recommended.

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Section: Hardware Technology For Eeg Signal Acquisitionmentioning

confidence: 99%

Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review

et al. 2020

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“…Such alienated samples make it harder to separate EEG tasks, so they need to be filtered out before the signal is further processed. In the literature, the commonly employed data filtering methods incorporate Bayes filters [5], Weiner filters [6], recursive least square (RLS) strategy [7], principal component analysis (PCA) [8], independent component analysis (ICA) [9], and sparse component analysis (SPA) [10]. However, these approaches have certain drawbacks in their applicability, such as they do not take inter-channel correlated characteristics into account, they have strong parametric dependencies which makes them hard to use for a practical BCI system, they often require a reference signal to denoise the EEG data, and they are sensitive to outliers.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploiting Asymmetric EEG Signals with EFD in Deep Learning Domain for Robust BCI

Huang

Miao

et al. 2022

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Motor imagery (MI) is a domineering paradigm in brain–computer interface (BCI) composition, personifying the imaginary limb motion into digital commandments for neural rehabilitation and automation exertions, while many researchers fathomed myriad solutions for asymmetric MI EEG signals classification, the existence of a robust, non-complex, and subject-invariant system is far-reaching. Thereupon, we put forward an MI EEG segregation pipeline in the deep-learning domain in an effort to curtail the existing limitations. Our method amalgamates multiscale principal component analysis (MSPCA), a novel empirical Fourier decomposition (EFD) signal resolution method with Hilbert transform (HT), followed by four pre-trained convolutional neural networks for automatic feature estimation and segregation. The conceived architecture is validated upon three binary class datasets: IVa, IVb from BCI Competition III, GigaDB from the GigaScience repository, and one tertiary class dataset V from BCI competition III. The average 10-fold outcomes capitulate 98.63%, 96.33%, and 89.96%, the highest classification accuracy for the aforesaid datasets accordingly using the AlexNet CNN model in a subject-dependent context, while in subject-independent cases, the highest success score was 97.69%, outperforming the contemporary studies by a fair margin. Further experiments such as the resolution scale of EFD, comparison with other signal decomposition (SD) methods, deep feature extraction, and classification with machine learning methods also accredits the supremacy of our proposed EEG signal processing pipeline. The overall findings imply that pre-trained models are reliable in identifying EEG signals due to their capacity to maintain the time-frequency structure of EEG signals, non-complex architecture, and their potential for robust classification performance.

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Wireless Body Sensor Networks: Applications, Challenges, Patient Monitoring, Decision Making, and Machine Learning in Medical Applications

Jaber

Idrees

2022

AI and IoT for Sustainable Development in Emerging Countries

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Improved energy efficient design in software defined wireless electroencephalography sensor networks (WESN) using distributed architecture to remove artifact

Cited by 7 publications

References 18 publications

Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review

Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review

Exploiting Asymmetric EEG Signals with EFD in Deep Learning Domain for Robust BCI

Wireless Body Sensor Networks: Applications, Challenges, Patient Monitoring, Decision Making, and Machine Learning in Medical Applications

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