Brain–computer interface for amyotrophic lateral sclerosis patients using deep learning network

Ramakrishnan, Jayabrabu; Mavaluru, Dinesh; Ramkumar, S.; Alqahtani, Abdulrahman Saad; Mubarakali, Azath; Retnadhas, Mervin

doi:10.1007/s00521-020-05026-y

Cited by 14 publications

(8 citation statements)

References 51 publications

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“…Further innovations include Ubeda et al [7] compact, wireless interface for robotic arm control, and Barea et al's [12] exploration of eye movement angles for wheelchair navigation. The study by Ramakrishnan et al [58] involved an HMI for wheelchair control using cross power spectral density analysis. Additionally, notable in EOG research are the Eyeboard [59] and JINS MEME [60] systems, as well as the semi-dry electrode approach by Moon et al [61].…”

Section: Related Workmentioning

confidence: 99%

Pilot Experiments and Hardware Design of Smart Electrooculographic Headband for People With Muscular Paralysis

Kosnacova,

Horvath,

Stremy

et al. 2024

IEEE Access

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Millions of individuals worldwide suffer from a motor disability, impeding their ability to perform daily tasks independently. Modern technologies, such as electrooculography (EOG), offer tools to enhance their quality of life. EOG, an electrophysiological method, detects DC potentials influenced by eye movements, making it particularly advantageous for individuals with motor impairments who retain ocular mobility. This article describes pilot experiments that includes the basic design of an electrodes configuration for peripheral solutions, their optimization, and the addition of sensors to measure other vital physiological parameters such as photoplethysmography (PPG) and electrocardiography (ECG) on demand. We tested all the EOG methodologies and connections that our device should contain, using the Mindmedia Nexus-10 MKII for EOG, the Analog Devices MAXM86146 optical biosensor module for PPG signals, the Texas Instruments ADS1299 biopotential circuit for ECG. The evaluation and filtering of the collected data were carried out in the MATLAB environment. Finally, we present a hardware concept based on a new approach that, unlike standard EOG, incorporates three large-area conductive fabric electrodes in a headband for practical and comfortable long-term non-manual human-machine interface (HMI). Our measurements are crucial for monitoring the patients' health status, mood variations, and can help prevent complications or speed up treatment. The primary goal of this research was to present our innovative concept and confirm its feasibility, bringing us closer to the realization of a fully developed and functional device.

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Section: Related Workmentioning

confidence: 99%

Pilot Experiments and Hardware Design of Smart Electrooculographic Headband for People With Muscular Paralysis

Kosnacova,

Horvath,

Stremy

et al. 2024

IEEE Access

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“…The proposed framework reduces transmission times between smart gateways and domestic consumers by 16 percent and reduces bandwidth requirements between smart gateways and domestic consumers by 26 percent [12]. According to our study, the flexibility-induced failover inactivity is minimal, and the failover technique results in no overlap of arrangement or matching on the detector.The security framework verifies the identity of a user using the Accessible Identification Specification [13] offered in 2018. The framework ensures the authentication process and secure access to resources through IoT.…”

Section: Literature Reviewmentioning

confidence: 99%

Medical Image Detection & Privacy Management With Elliptic Curve GOPSO Cryptographic Optimization Technique on the Internet of Health Things

B¹,

A²,

Shanthan³

et al. 2021

Preprint

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The advent of the Internet of Things (IoT) is to transform the health care sector and lead to the development of the Internet of Health Things (IoHT). This technology exceeds existing human services mechanically, financially, and socially. This paper used an advanced cryptographic framework that includes optimization strategies to look at IoHT medical image protection. The patient data kept on a cloud server which was detected and sensed from the IoHT Healthcare devices. It's critical to ensure the safety and privacy of patient clinical images in the cloud; here, an enhanced security framework for health information promotes trust. Next, we presented health care providers who could provide the full range of medical facilities for IoHT participants. In the process of encrypting/decrypting elliptical curves, the optimal key is selected using the Grasshopper Particle Swarm Optimization (GOPSO) to increase the security standard of medical images. Medical images are protected within IoHT by using this approach.The implementation results were analyzed and compared with a variety of encryption algorithms and their optimization techniques. The effectiveness of the proposed methods and the results show that the medical image is secure and prevents attacks in IoHT-based health care systems.

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“…Obeidat et al developed a wheelchair for a paralyzed person using Bayesian Linear Discriminant Analysis and obtained an accuracy of 95% from fourteen subjects [ 21 ]. Jayaprbhu et al developed EEG-based BCI for ALS-affected persons using the convolution neural network and cross power spectral density for four subjects from fifteen subjects and obtained the accuracy of 91.18% and 86.88% [ 22 ]. Xiao et al modeled four-state EEG-based BCI for SCI-affected individuals using CWT featured with a hybrid neural network and obtained an accuracy of 93.86% [ 23 ].…”

Section: Background Studymentioning

confidence: 99%

Eye Movement Signal Classification for Developing Human-Computer Interface Using Electrooculogram

Thilagaraj

Ramkumar

Karthikeyan³

et al. 2021

Journal of Healthcare Engineering

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Human-computer interfaces (HCI) allow people to control electronic devices, such as computers, mouses, wheelchairs, and keyboards, by bypassing the biochannel without using motor nervous system signals. These signals permit communication between people and electronic-controllable devices. This communication is due to HCI, which facilitates lives of paralyzed patients who do not have any problems with their cognitive functioning. The major plan of this study is to test out the feasibility of nine states of HCI by using modern techniques to overcome the problem faced by the paralyzed. Analog Digital Instrument T26 with a five-electrode system was used in this method. Voluntarily twenty subjects participated in this study. The extracted signals were preprocessed by applying notch filter with a range of 50 Hz to remove the external interferences; the features were extracted by applying convolution theorem. Afterwards, extracted features were classified using Elman and distributed time delay neural network. Average classification accuracy with 90.82% and 90.56% was achieved using two network models. The accuracy of the classifier was analyzed by single-trial analysis and performances of the classifier were observed using bit transfer rate (BTR) for twenty subjects to check the feasibility of designing the HCI. The achieved results showed that the ERNN model has a greater potential to classify, identify, and recognize the EOG signal compared with distributed time delay network for most of the subjects. The control signal generated by classifiers was applied as control signals to navigate the assistive devices such as mouse, keyboard, and wheelchair activities for disabled people.

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Brain–computer interface for amyotrophic lateral sclerosis patients using deep learning network

Cited by 14 publications

References 51 publications

Pilot Experiments and Hardware Design of Smart Electrooculographic Headband for People With Muscular Paralysis

Pilot Experiments and Hardware Design of Smart Electrooculographic Headband for People With Muscular Paralysis

Medical Image Detection & Privacy Management With Elliptic Curve GOPSO Cryptographic Optimization Technique on the Internet of Health Things

Eye Movement Signal Classification for Developing Human-Computer Interface Using Electrooculogram

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Brain–computer interface for amyotrophic lateral sclerosis patients using deep learning network

Cited by 14 publications

References 51 publications

Pilot Experiments and Hardware Design of Smart Electrooculographic Headband for People With Muscular Paralysis

Pilot Experiments and Hardware Design of Smart Electrooculographic Headband for People With Muscular Paralysis

Medical Image Detection &amp; Privacy Management With Elliptic Curve GOPSO Cryptographic Optimization Technique on the Internet of Health Things

Eye Movement Signal Classification for Developing Human-Computer Interface Using Electrooculogram

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Medical Image Detection & Privacy Management With Elliptic Curve GOPSO Cryptographic Optimization Technique on the Internet of Health Things