EEG Controlled Wheelchair

Swee, Sim Kok; Kiang, Kho Desmond Teck; You, Lim Zheng

doi:10.1051/matecconf/20165102011

Cited by 27 publications

(10 citation statements)

References 4 publications

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“…Swee et al [ 27 ] proposed developing an electric wheelchair that can be directly controlled by the brain and that does not require any physical feedback as controlling input from the user. EEG signals, acquired with a commercial headset, are processed and converted into mental commands and a specific implemented algorithm transmitted out the controlling signals wirelessly to the electrical wheelchair.…”

Section: MI Eeg-based Bcis In Wheelchair Movement and Control: Literature Resultsmentioning

confidence: 99%

“…Using Emotiv EPOC headset in the system proposed by Swee et al [ 27 ], the brainwaves in EEG form were translated into the metrics (facial expression, performance metrics, and mental commands) by means of different detection tools. More specifically, the mental command detection suite is used to interpret the user’s mental commands (push, pull, left, and right) in order to control the electrical wheelchair movement.…”

Section: Mi-bcws Performance Evaluationmentioning

confidence: 99%

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Motor-Imagery EEG-Based BCIs in Wheelchair Movement and Control: A Systematic Literature Review

Palumbo

Gramigna

Calabrese

et al. 2021

Sensors

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The pandemic emergency of the coronavirus disease 2019 (COVID-19) shed light on the need for innovative aids, devices, and assistive technologies to enable people with severe disabilities to live their daily lives. EEG-based Brain-Computer Interfaces (BCIs) can lead individuals with significant health challenges to improve their independence, facilitate participation in activities, thus enhancing overall well-being and preventing impairments. This systematic review provides state-of-the-art applications of EEG-based BCIs, particularly those using motor-imagery (MI) data, to wheelchair control and movement. It presents a thorough examination of the different studies conducted since 2010, focusing on the algorithm analysis, features extraction, features selection, and classification techniques used as well as on wheelchair components and performance evaluation. The results provided in this paper could highlight the limitations of current biomedical instrumentations applied to people with severe disabilities and bring focus to innovative research topics.

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Section: MI Eeg-based Bcis In Wheelchair Movement and Control: Literature Resultsmentioning

confidence: 99%

Section: Mi-bcws Performance Evaluationmentioning

confidence: 99%

Motor-Imagery EEG-Based BCIs in Wheelchair Movement and Control: A Systematic Literature Review

Palumbo

Gramigna

Calabrese

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Swee et al [61] proposed developing an electric wheelchair that can be directly controlled by the brain and that does not require any physical feedback as controlling input from the user. The authors anticipated that this system could give a new contribution to physically disabled people to regain their mobility.…”

Section: MI Eeg-based Bcis In Wheelchairs Movement and Control: Literature Resultsmentioning

confidence: 99%

Motor-Imagery EEG-based BCIs in Wheelchairs Movement and Control: A Systematic Literature Review

Palumbo¹,

Gramigna²,

Calabrese³

et al. 2021

Preprint

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The pandemic emergency of the coronavirus disease 2019 (COVID-19) shed light on the need for innovative aids, devices and assistive technologies to enable people with severe disabilities to live their daily lives. EEG-based Brain-Computer Interfaces (BCIs) can lead individuals with significant health challenges to improve their independence, facilitate participation in activities, thus enhancing overall well-being and preventing impairments. This systematic review provides state-of-the-art applications of EEG-based BCIs, particularly those using motor-imagery (MI) data, to wheelchair control and movement. It presents a thorough examination of the different studies conducted since 2010, focusing on the algorithm analysis, features extraction, features selection and classification techniques used, and wheelchair components and performance evaluation. The results provided in this paper could highlight the limitations of current biomedical instrumentations applied to people with severe disabilities in the pandemic context of Covid-19 and bring focus to innovative research topics.

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“…The term ERP defines the measured brain response that is the direct result of a specific sensory, cognitive, or motor event. Zgallai et al [8] and Sim et al [34] proposed an FFT-based methodology from EEG signals obtained from the Emotiv EPOC+ showing good classification performance. Ben Taher et al [35] also utilized EEG signals from the Emotiv EPOC+.…”

Section: Discussionmentioning

confidence: 99%

EEG-Based Eye Movement Recognition Using Brain–Computer Interface and Random Forests

Antoniou

Bozios

Christou

et al. 2021

Sensors

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Discrimination of eye movements and visual states is a flourishing field of research and there is an urgent need for non-manual EEG-based wheelchair control and navigation systems. This paper presents a novel system that utilizes a brain–computer interface (BCI) to capture electroencephalographic (EEG) signals from human subjects while eye movement and subsequently classify them into six categories by applying a random forests (RF) classification algorithm. RF is an ensemble learning method that constructs a series of decision trees where each tree gives a class prediction, and the class with the highest number of class predictions becomes the model’s prediction. The categories of the proposed random forests brain–computer interface (RF-BCI) are defined according to the position of the subject’s eyes: open, closed, left, right, up, and down. The purpose of RF-BCI is to be utilized as an EEG-based control system for driving an electromechanical wheelchair (rehabilitation device). The proposed approach has been tested using a dataset containing 219 records taken from 10 different patients. The BCI implemented the EPOC Flex head cap system, which includes 32 saline felt sensors for capturing the subjects’ EEG signals. Each sensor caught four different brain waves (delta, theta, alpha, and beta) per second. Then, these signals were split in 4-second windows resulting in 512 samples per record and the band energy was extracted for each EEG rhythm. The proposed system was compared with naïve Bayes, Bayes Network, k-nearest neighbors (K-NN), multilayer perceptron (MLP), support vector machine (SVM), J48-C4.5 decision tree, and Bagging classification algorithms. The experimental results showed that the RF algorithm outperformed compared to the other approaches and high levels of accuracy (85.39%) for a 6-class classification are obtained. This method exploits high spatial information acquired from the Emotiv EPOC Flex wearable EEG recording device and examines successfully the potential of this device to be used for BCI wheelchair technology.

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EEG Controlled Wheelchair

Cited by 27 publications

References 4 publications

Motor-Imagery EEG-Based BCIs in Wheelchair Movement and Control: A Systematic Literature Review

Motor-Imagery EEG-Based BCIs in Wheelchair Movement and Control: A Systematic Literature Review

Motor-Imagery EEG-based BCIs in Wheelchairs Movement and Control: A Systematic Literature Review

EEG-Based Eye Movement Recognition Using Brain–Computer Interface and Random Forests

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