Electroencephalograph-Based Wheelchair Controlling System for the People with Motor Disability Using Advanced BrainWear

Jameel, Huda Farooq; Mohammed, Salim Latif; Gharghan, Sadik Kamel

doi:10.1109/dese.2019.00156

Cited by 5 publications

(6 citation statements)

References 40 publications

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“…Starting an in depth analyses on the application fields 30 on 84 researches have medical purposes [83][84][85][86][87][88][89][90][91][92][92][93][94][95][96][97][98][99][100][101][102][103][104][105][106][107][108][109][110][111].…”

Section: Bci Application and Feedbackmentioning

confidence: 99%

“…After having given a brief overview of the synthetic information regarding EEG related devices, it is possible to move to a deeper analysis of how these technologies have been effectively used by the authors according to the table present in the supplementary materials. Fourteen works on 84 prefer reducing the number of electrodes provided by the device producers [84,88,100,102,106,122,138,158] and especially make a selection on the channel placed over the central cortical area, in particular considering only the C{3,4} [135,143], Cz [88,139,144] and C{1,2} [98] electrodes. Moreover, some of the authors clearly specifies the sampling rate used for signal acquisition that goes from 125Hz [109], 128Hz [87,106,118,129,131,135], 250Hz [94,95,108,141,145], 256Hz [131], 500Hz [84,88,89,93,99,113,133,138,143,144,148,160,161] to 512Hz [120].…”

Section: Employed Technologiesmentioning

confidence: 99%

“…Moreover, some sensors have been used together with the EEG devices like the electromyogram [83,88,109,162,163], functional electrical stimulation devices [98], near infrared spectroscopy tools [100], and magnetic resonance imaging devices [146]. Notice that feedback was also provided by considering different tools like exoskeletons [84,88,96,99], wheelchairs and vehicles [94,95,102,104,106,118], rehabilitation and assistive robotic tools [87,93,101,103], eye tracking devices [160], movement tracking devices [160], virtual reality devices [89,97,115], robots [110,113,121,122], and simulators [123].…”

Section: Employed Technologiesmentioning

confidence: 99%

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EEG-based BCIs on Motor Imagery Paradigm Using Wearable Technologies: A Systematic Review

Saibene¹,

Caglioni²,

Corchs³

et al. 2023

Preprint

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In the last decades, the automatic recognition and interpretation of brain waves acquired by electroencephalographic (EEG) technologies have undergone remarkable growth, leading to a consequent rapid development of Brain Computer Interfaces (BCIs). EEG-based BCIs are non-invasive systems that allow communication between a human being and an external device interpreting brain activity directly. Thanks to the advances in neurotechnologies, and especially in the field of wearable devices, BCIs are now also employed outside medical and clinical applications. Within this context, this paper proposes a systematic review of EEG-based BCIs, focusing on one of the most promising paradigms based on Motor Imagery (MI) and limiting the analysis to applications that adopt wearable devices. This review aims to evaluate the maturity levels of these systems, both from the technological and computational points of view. The selection of papers has been performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), leading to 84 publications considered in the last ten years (from 2012 to 2022). Besides technological and computational aspects, this review also aims at systematically list experimental paradigms and available datasets in order to identify benchmarks and guidelines for the development of new applications and computational models.

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Section: Bci Application and Feedbackmentioning

confidence: 99%

Section: Employed Technologiesmentioning

confidence: 99%

Section: Employed Technologiesmentioning

confidence: 99%

See 1 more Smart Citation

EEG-based BCIs on Motor Imagery Paradigm Using Wearable Technologies: A Systematic Review

Saibene¹,

Caglioni²,

Corchs³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…It has 54 digital input/output pins. Note that this number of pins consists of 14 pins used as pulse-width modulation (PWM) outputs, 16 pins used as analog inputs, and four pins used as universal asynchronous receiver transmitter (UART) hardware serial ports [28]- [30].…”

Section: Hardware Unitmentioning

confidence: 99%

Measurement of an electroretinogram signal and display waves on graphical user interface by laboratory virtual instrument engineering workbench

Mahmood

Jameel

Hammed

2022

IJEECS

Self Cite

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The electroretinogram (ERG) is an electrophysiological recording method that measures the retinal electrical potential. The electrical reaction is quantified by electrical interaction of the indicator electrode with the cornea or at various levels inside the retina. However, such ERG systems suffer from certain limitations and challenges, such as high cost, low a/b-wave amplitude, and the outcomes do not provide any information about patients. In this work, we designed and implemented a real-time prototype for an ERG system for measuring eye waves via diode-transistor logic (DTL)- electrode and AD624AD-model. In addition, a graphical user interface (GUI) via virtual instrument engineering workbench (LabVIEW) was used. The developed system achieved high amplitude for ERG a/b-waves of about 100 and 700 mV. In terms of a/b-waves in the system, the findings show that this study has good results for optimizing the measurement of ERG signals. The method showed satisfactory accuracy of about 92.5% for 10 participants aged 20-60 years and comprising both genders

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“…In addition, power supply for this module is 5 or 3.3 V. This SD card is used in several applications such as data logger, audios, videos, and graphics [28], [29]. The microcontroller used in this work is Arduino Uno because used a processor ATmega328 [30]- [32]. The board features digital and analog (I/O) connectors that may be linked to masks and other components on the extension board.…”

mentioning

confidence: 99%

Management patients information based finger print

Fakhri

Jameel

Mahmood

2022

IJEECS

Self Cite

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<span>The fingerprint is <span>certainly one of the distinguishing features of the human body that is easily available and identifies one individual from another. The fingerprint sensor increases this distinctiveness, which is a device that can automatically classify or identify a person. The fingerprint based medical system is a more efficient means of storing clinical data for patients. It makes take advantage of fingerprint recognition technology to quickly and easily for determine the patient's past medical history. The system consists of an Arduino UNO board, a fingerprint sensor, an secure digital (SD) card module, and a micro-SD card. The suggested technology allows the use of a micro-SD card to store patient information as well as send it by internet. When this system was compared to the manual technique, the results indicate that the main advantage is that the proposed device saves a significant amount of time that manual searching and enrolling requires. Patients' information is simply collected and managed with this system, which has enhanced dependability, durability, and efficiency. It provides improved speed and performance, as well as better data security because the data is stored within the device.</span></span>

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Electroencephalograph-Based Wheelchair Controlling System for the People with Motor Disability Using Advanced BrainWear

Cited by 5 publications

References 40 publications

EEG-based BCIs on Motor Imagery Paradigm Using Wearable Technologies: A Systematic Review

EEG-based BCIs on Motor Imagery Paradigm Using Wearable Technologies: A Systematic Review

Measurement of an electroretinogram signal and display waves on graphical user interface by laboratory virtual instrument engineering workbench

Management patients information based finger print

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