Electrical Signals from the Muscles and Nerves

Park, Kwang Suk

doi:10.1007/978-3-031-20784-6_9

Humans and Electricity 2023

DOI: 10.1007/978-3-031-20784-6_9

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Electrical Signals from the Muscles and Nerves

Kwang Suk Park

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2024

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Cited by 2 publications

References 11 publications

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Bioelectronic Implantable Devices for Physiological Signal Recording and Closed‐Loop Neuromodulation

Oh,

Jekal,

Liu

et al. 2024

Adv Funct Materials

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Bioelectronic implantable devices are adept at facilitating continuous monitoring of health and enabling the early detection of diseases, offering insights into the physiological conditions of various bodily organs. Furthermore, these advanced systems have therapeutic capabilities in neuromodulation, demonstrating their efficacy in addressing diverse medical conditions through the precise delivery of stimuli directly to specific targets. This comprehensive review explores developments and applications of bioelectronic devices within the biomedical field. Special emphasis is placed on the evolution of closed‐loop systems, which stand out for their dynamic treatment adjustments based on real‐time physiological feedback. The integration of Artificial Intelligence (AI) and edge computing technologies is discussed, which significantly bolster the diagnostic and therapeutic functions of these devices. By addressing elemental analyses, current challenges, and future directions in implantable devices, the review aims to guide the pathway for advances in bioelectronic devices.

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Bioelectronic Implantable Devices for Physiological Signal Recording and Closed‐Loop Neuromodulation

Oh,

Jekal,

Liu

et al. 2024

Adv Funct Materials

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Eye Movement Classification using Feature Engineering and Ensemble Machine Learning

Mahmood,

Gharkan,

Jamil

et al. 2024

Eng. Technol. Appl. Sci. Res.

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This paper explores the classification of gaze direction using electrooculography (EOG) signals, integrating signal processing, deep learning, and ensemble learning techniques to enhance accuracy and reliability. A complex technique is proposed in which several feature types are derived from EOG data. Spectral properties generated from power spectral density analysis augment basic statistical characteristics such as mean and standard deviation, revealing the frequency content of the signal. Skewness, kurtosis, and cross-channel correlations are also used to represent intricate nonlinear dynamics and inter-channel interactions. These characteristics are then reformatted into a two-dimensional array imitating picture data, enabling the use of the pre-trained ResNet50 model to extract deep and high-level characteristics. Using these deep features, an ensemble of bagging-trained decision trees classifies gaze directions, lowering model variance and increasing prediction accuracy. The results show that the ensemble deep learning model obtained outstanding performance metrics, with accuracy and sensitivity ratings exceeding 97% and F1-score of 98%. These results not only confirm the effectiveness of the proposed approach in managing challenging EOG signal classification tasks but also imply important consequences for the improvement of Human-Computer Interaction (HCI) systems, especially in assistive technologies where accurate gaze tracking is fundamental.

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Electrical Signals from the Muscles and Nerves

Cited by 2 publications

References 11 publications

Bioelectronic Implantable Devices for Physiological Signal Recording and Closed‐Loop Neuromodulation

Bioelectronic Implantable Devices for Physiological Signal Recording and Closed‐Loop Neuromodulation

Eye Movement Classification using Feature Engineering and Ensemble Machine Learning

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