An efficient EEG signal classification technique for Brain–Computer Interface using hybrid Deep Learning

Medhi, Kishore; Hoque, Nazrul; Dutta, Sushanta Kabir; Hussain, Md. Iftekhar

doi:10.1016/j.bspc.2022.104005

Cited by 19 publications

(3 citation statements)

References 25 publications

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“…The authors of [14] presented a roadmap for improving telemedicine QoS through SDN. They recommended the deployment of SDN to ensure adequate bandwidth and facilitate real-time medical data transfers [15].…”

Section: Related Workmentioning

confidence: 99%

Revolutionizing Neurostimulator Care: Enhancing Remote Health Monitoring through SDN-Cloud Networks

Antony,

Elangovan

2024

Preprint

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The Internet of Things (IoT) and artificial intelligence (AI) are the fastest-growing technological approaches globally. With the rising urban population, the concept of a smart city isn't new. To effectively integrate IoT and AI into remote healthcare monitoring (RHM) systems within smart cities, we must have a comprehensive grasp of smart city frameworks. Our proposed model focuses on gathering data from an auricular therapy device, a neurostimulator that provides non-invasive stimulation to the outer ear. This device communicates via Bluetooth, allowing data exchange between the patient's and doctor's phones. After collecting the brain signal data, it's processed to eliminate noise and is normalized. This data is then classified using the adaptive fuzzy based Bayesian metasalp neural network (AFBBMNN) combined with levy flight secure offloading analysis in Software Defined Networking (SDN). The results prominently emphasize the need for enhanced healthcare provision. This information is then relayed to doctors via a cloud-SDN module that comprises a communication phase, cloud server, and cloud database where the signals are stored. The proposed method offers promising outcomes, emphasizing its viability as an efficient tool for early neurological disease detection and treatment within a smart city healthcare framework.

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Section: Related Workmentioning

confidence: 99%

Revolutionizing Neurostimulator Care: Enhancing Remote Health Monitoring through SDN-Cloud Networks

Antony,

Elangovan

2024

Preprint

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“…Cost can be a concern when using medically certified devices for non-medical purposes [ 19 ]. In recent years, consumer-grade biometric devices have been extended to non-medical fields, such as measuring pupils’ attention in education [ 20 ], brain–computer interfaces in human–computer interaction [ 21 ], workers’ mental load in human–robot collaboration [ 22 ], and affective computing [ 23 , 24 ]. Some consumer-grade biometric devices have been reported to be as accurate as medical-grade products [ 25 , 26 ] or can be used for health care purposes [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

“…These features make PPG suitable for non-medical activities [ 27 ], such as user experience studies [ 36 ]. In recent years, there has been an extension of the use of PPG to non-medical fields, such as measuring pupils’ attention in education [ 20 ], brain–computer interfaces in human–computer interaction [ 21 , 22 ], and affective computing [ 23 , 24 ]. Some consumer-grade biometric devices have been reported to be as accurate as medical-grade products [ 25 , 26 ] and can be used for health care purposes [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

Building a Low-Cost Wireless Biofeedback Solution: Applying Design Science Research Methodology

Cheng

Lin

2023

Sensors

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In recent years, affective computing has emerged as a promising approach to studying user experience, replacing subjective methods that rely on participants’ self-evaluation. Affective computing uses biometrics to recognize people’s emotional states as they interact with a product. However, the cost of medical-grade biofeedback systems is prohibitive for researchers with limited budgets. An alternative solution is to use consumer-grade devices, which are more affordable. However, these devices require proprietary software to collect data, complicating data processing, synchronization, and integration. Additionally, researchers need multiple computers to control the biofeedback system, increasing equipment costs and complexity. To address these challenges, we developed a low-cost biofeedback platform using inexpensive hardware and open-source libraries. Our software can serve as a system development kit for future studies. We conducted a simple experiment with one participant to validate the platform’s effectiveness, using one baseline and two tasks that elicited distinct responses. Our low-cost biofeedback platform provides a reference architecture for researchers with limited budgets who wish to incorporate biometrics into their studies. This platform can be used to develop affective computing models in various domains, including ergonomics, human factors engineering, user experience, human behavioral studies, and human–robot interaction.

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An Empirical Study on Comparison of Machine Learning Algorithms for Eye-State Classification Using EEG Data

Priyadharshini Jayadurga,

Chandralekha,

Saleem

2024

Lecture Notes in Networks and Systems

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An efficient EEG signal classification technique for Brain–Computer Interface using hybrid Deep Learning

Cited by 19 publications

References 25 publications

Revolutionizing Neurostimulator Care: Enhancing Remote Health Monitoring through SDN-Cloud Networks

Revolutionizing Neurostimulator Care: Enhancing Remote Health Monitoring through SDN-Cloud Networks

Building a Low-Cost Wireless Biofeedback Solution: Applying Design Science Research Methodology

An Empirical Study on Comparison of Machine Learning Algorithms for Eye-State Classification Using EEG Data

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