Operating of a Drone Using Human Intent Recognition and Characteristics of an EEG Signal

Shankhdhar, Ashutosh; Mangla, Arushi; Singh, Akhilesh Kumar; Srivastava, Ayushi

doi:10.1109/pdgc50313.2020.9315321

Cited by 6 publications

(2 citation statements)

References 10 publications

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“…Classifying network traffic is a crucial task that helps Internet Service Providers (ISPs) [5] identify the different types of application flows in a network. ISPs or network operators can control a network's overall performance with the aid of network traffic classification [6] .…”

Section: Introductionmentioning

confidence: 99%

An enhanced self-learning-based clustering scheme for real-time traffic data distribution in wireless networks

Jain

Mehrotra

Sisodia

et al. 2023

Heliyon

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Section: Introductionmentioning

confidence: 99%

An enhanced self-learning-based clustering scheme for real-time traffic data distribution in wireless networks

Jain

Mehrotra

Sisodia

et al. 2023

Heliyon

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“…Control Interface: Finally, these classified patterns are translated into actionable BCI commands, such as the control of a wheelchair, robotic arm, or drone [44][45][46].…”

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confidence: 99%

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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Heart Disease Prediction Model using various Supervised Learning Algorithm

Upadhyay

Dwivedi

Verma

et al. 2023

2023 IEEE 12th International Conference on Communication Systems and Network Technologies (CSNT)

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Operating of a Drone Using Human Intent Recognition and Characteristics of an EEG Signal

Cited by 6 publications

References 10 publications

An enhanced self-learning-based clustering scheme for real-time traffic data distribution in wireless networks

An enhanced self-learning-based clustering scheme for real-time traffic data distribution in wireless networks

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

Heart Disease Prediction Model using various Supervised Learning Algorithm

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