Anisotropic Diffusion-based Analog CNN Architecture for Continuous EEG Monitoring

Anjum, Khizar; Pompili, Dario

doi:10.1109/mass58611.2023.00031

2023 IEEE 20th International Conference on Mobile Ad Hoc and Smart Systems (MASS) 2023

DOI: 10.1109/mass58611.2023.00031

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Anisotropic Diffusion-based Analog CNN Architecture for Continuous EEG Monitoring

Khizar Anjum,

Dario Pompili

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Cited by 2 publications

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Battery-Less Implantable Continuous EEG Monitoring via Anisotropic Diffusion

Anjum,

Pompili

2024

IEEE J. Select. Areas Commun.

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Battery-Less Implantable Continuous EEG Monitoring via Anisotropic Diffusion

Anjum,

Pompili

2024

IEEE J. Select. Areas Commun.

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Energy-Efficient Deep Neural Networks for EEG Signal Noise Reduction in Next-Generation Green Wireless Networks and Industrial IoT Applications

Kumar,

Chakravarthy,

Nanthaamornphong

2023

Symmetry

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Wireless electroencephalography (EEG) has emerged as a critical interface between human cognitive processes and machine learning technologies in the burgeoning field of sensor communications. This paper presents a comprehensive review of advancements in wireless EEG communication and analysis, with an emphasis on their role in next-generation green wireless networks and industrial IoT. The review explores the efficacy of modulation techniques, such as amplitude-shift keying (ASK) and frequency-shift keying (FSK) in EEG data transmission, and emphasizes the transformative role of deep learning in the joint transmission and restoration of EEG signals. In addition, we propose a novel, energy-efficient approach to deep learning-based EEG analytics, designed to enhance wireless information transfer for industrial IoT applications. By applying an autoencoder to sample the EEG data and incorporating a hidden layer to simulate a noisy communication channel, we assessed the energy efficiency and reliability of the transmission. Our results demonstrate that the chosen network topology and parameters significantly affect not only data fidelity but also energy consumption, thus providing valuable insights for the development of sustainable and efficient wireless EEG systems in industrial IoT environments. A key aspect of our study is related to symmetry. Our results demonstrate that the chosen network topology and parameters significantly impact not only data fidelity but also energy fidelity and energy consumption, thus providing valuable insights for the development of sustainable and efficient wireless EEG systems in industrial IoT environments. Furthermore, we realized that the EEG data showed mildly marked symmetry. Neural networks must also exhibit asymmetric behavior for better performance.

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Anisotropic Diffusion-based Analog CNN Architecture for Continuous EEG Monitoring

Cited by 2 publications

References 21 publications

Battery-Less Implantable Continuous EEG Monitoring via Anisotropic Diffusion

Battery-Less Implantable Continuous EEG Monitoring via Anisotropic Diffusion

Energy-Efficient Deep Neural Networks for EEG Signal Noise Reduction in Next-Generation Green Wireless Networks and Industrial IoT Applications

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