EEG Wheelchair for People of Determination

AlAbboudi, Mariam; Majed, Maitha; Hassan, Fatima; Nassif, Ali Bou

doi:10.1109/aset48392.2020.9118340

Cited by 8 publications

(3 citation statements)

References 14 publications

(12 reference statements)

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“…Although there are other ways for noise reduction or signal recovery such as curve fitting, attenuation filter for noisy signal (Luccas et al, 2021), several researchers have sought to overcome these challenges through the use of machine learning (ML) (AlAbboudi et al, 2020, Gayathri et al, 2018, Gemein et al, 2020, Ieracitano et al, 2021, Isa et al, 2019, Mardini et al, 2020, Rashid et al, 2018. Among the various areas within machine learning, deep learning (DL) algorithms have stood out in recent years.…”

Section: Context and Motivationmentioning

confidence: 99%

ASTERI: image-based representation of EEG signals for motor imagery classification

2022

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Electroencephalography (EEG) signals are valuable in the monitoring and investigation of neurological diseases and in the control of brain-machine interfaces (BCI). However, these signals are noisy and are non-linear and non-stationary in nature. Signal analysis is an expensive task and can lead to misdiagnosis. Deep learning can be used to overcome these challenges. The most used deep architectures are based on convolutional neural networks (CNNs). Representing EEG signals as images can be useful to use deep architectures based on CNNs in solutions based on intelligent EEG-based systems. Methods: In this work, we propose the ASTERI method, for representing EEG signals in two dimensions using the backprojection reconstruction method. To validate the proposal, experiments were performed with motor imagery EEG signals from the BCI Competition IV 2b motor imagery database. To extract attributes from the EEG signal windows, we used the ASTERI representation and investigated the pre-trained networks VGG16 and LeNet. To classify the final classification of imagined movements to the right and to the left, we used Random Forests. Results: For the original database, the validation with 100 trees achieved an average accuracy and kappa index of 88.97% and 0.78, respectively. In the case of augmented databases with synthetic instances, the average accuracies were 89.10% and 89.13% for increases of 50% and 150% for each class, respectively. Conclusion: The experimental results showed that the ASTERI method, together with hybrid deep architectures based on CNN and Random Forests, can generate competitive results in solving motor imagery problems. The representation of signals as images can also con tribute to the exploration of new solutions of applied neuroscience problems based on deep architectures.

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Section: Context and Motivationmentioning

confidence: 99%

ASTERI: image-based representation of EEG signals for motor imagery classification

2022

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“…BCI generates a signal that originates in the brain and translates it into a command on the computer [15], [21], [22]. The application of BCI has been carried out in various applications, such as in games [23], rehabilitation [6], [24], [25], implementation of smart homes [26], object recognition [14], detection of emotions in persons with disabilities [27], intelligent robotic exoskeleton [2], robotic hand [21], robotic arm [20], wheelchair [8], [17], control relay module [18], cursor control [19], virtual keyboard [7], [22] and others. The EEG signal is obtained from the voltage variation resulting from the flow of ions across the brain's nerve cell membrane by placing electrodes on the scalp [28].…”

Section: Introductionmentioning

confidence: 99%

Smart Room System for Paralysis Patients with Mindwave EEG Sensor Control

Anandika,

Ferdian,

Eriyandha

et al. 2023

JOIV : Int. J. Inform. Visualization

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Persons with disabilities experience physical, intellectual, mental, or sensory difficulties. One type of disability is paralysis. Paralysis is a condition where there is interference with the nerves that control body movement, causing the limbs to be unable to move. Paralyzed people will find it difficult to move without the help of others. Therefore, research was carried out by creating an intelligent room system to help persons with disabilities manage their own rooms so that they do not always have to be accompanied by a nurse. Paralyzed people can turn lights or fans on and off, and send help messages to their carers via the Telegram bot. This study used the NeuroSky Mindwave EEG headset which detects the user's brain signals with outputs in the form of attention level, relaxation level (meditation), and blink strength level. The resulting signal is processed via a PC and sent via NodeMCU to give commands in the form of turning lights and fans on or off, as well as sending messages to nurses. From this research a system was produced that could turn on the lights based on the value of Attention ≥ 70, turn on the fan based on the Meditation value ≥ 74, then the value of BlinkStrength ≥ 81 which was counted 2 times to turn off the lights, 3 times to turn off the fan, 4 times to turn off the lights and fan, and more than 4 times sending help messages

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“…According to estimates of the World Health Organization, 75 million people in the world were using a wheelchair in 2018 (WHO 2018). Wheelchairs are needed for mobility in many different kinds of disabilities, among which are amputation and spinal cord injury (Archambault et al 2012;Karmarkar et al 2009;Singh et al 2020;van den Akker et al 2020), paralysis (Aarai et al 2011AlAbboudi et al 2020;Fadhil et al 2019;Kuntal et al 2020;Ruzaij et al 2017), cerebral palsy (Alkhateeb et al 2019;Rodby-Bousquet and Hägglund 2010;Rozas Llontop et al 2020), multiple sclerosis (Devitt et al 2004;Revathi et al 2008;Silveira et al 2020), muscular dystrophy (Bayley et al 2020;Richardson and Frank 2009;Xi et al 2019), traumatic brain injury (Quilico et al 2020;Spaeth et al 2008).…”

Section: Introductionmentioning

confidence: 99%

A simulator for both manual and powered wheelchairs in immersive virtual reality CAVE

et al. 2021

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A large number of people in the world need to use a wheelchair because of different disabilities. Driving a wheelchair requires complex physical and cognitive abilities which need to be trained. Virtual training helps users acquire driving skills in a safe environment. The aim of this paper is to describe and technically validate simulation models for both manual (MW) and powered wheelchairs (PW) based on immersive virtual reality CAVE (VR). As VR system, the Gait Real-time Analysis Interactive Lab (GRAIL) was used, a CAVE equipped with a motion platform with two degrees of freedom and an optoelectronic motion capture system. A real wheelchair was positioned onto the motion platform with rear wheels free to turn in MW modality, and a commercial joystick was installed on an armrest to simulate the PW modality. Passive markers were used to track the wheel rotation, the joystick and the user hand motion. Custom D-flow applications were developed to manage virtual scene response to user actions. Overground tests, based on single wheel rotation, were performed to verify the simulation model reliability. Quantitative results demonstrated that the MW simulator kinematics was consistent with a real wheelchair overground in the absence of wheel slip and inertia (median error for MW 0.40 °, no systematic bias p = 0.943, high correlation rho > 0.999, p < 0.01). The proposed solution is flexible and adaptable to different wheelchairs, joysticks and optoelectronic systems. The main limitation is the absence of force feedback. Nevertheless, it is a reliable prototype that can be used to validate new virtual scenarios as well as for wheelchair training. The next steps include the system validation with real end users and assessment of the simulator effectiveness as a training tool.

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EEG Wheelchair for People of Determination

Cited by 8 publications

References 14 publications

ASTERI: image-based representation of EEG signals for motor imagery classification

ASTERI: image-based representation of EEG signals for motor imagery classification

Smart Room System for Paralysis Patients with Mindwave EEG Sensor Control

A simulator for both manual and powered wheelchairs in immersive virtual reality CAVE

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