Brain Controlled Wheelchair: A Smart Prototype

Awais, Muhammad; Yusoff, Mohd Zuki; Yahya, Norashikin; Ahmed, Sheikh Zeeshan; Qamar, Muhammad Umair

doi:10.1088/1742-6596/1529/4/042075

Cited by 8 publications

(6 citation statements)

References 9 publications

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“…Figures 15(b The test setup of the first Test subject-1 is shown in Figure . 15(a). The subject is wearing the headwear making sure that the frontal EEG electrode is placed precisely at FP1 [17,27]. Before electrode placement, the skin at FP1 is cleaned with cotton to reduce skin impedance.…”

Section: Test Resultsmentioning

confidence: 99%

“…The TGAM module is powered by a battery pack that supplies a cumulative voltage of 4.5V. A single channel non-invasive silver EEG electrode placed at the pre-frontal lobe (FP1) [17,27,28] extracts electrical signals from the scalp of the subject. This particular FP1 placement is used because FP1 and FP2 placements show the greatest deflection of potential in a time-domain EEG waveform [29].…”

Section: Headwearmentioning

confidence: 99%

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Brain-Computer Interfacing for Wheelchair Control by Detecting Voluntary Eye Blinks

Francis

Umayal

Kanimozhi

2021

IJEEI

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The human brain is considered one of the most powerful quantum computers and combining the human brain with technology can even outperform artificial intelligence. Using a Brain-Computer Interface (BCI) system, the brain signals can be analyzed and programmed for specific tasks. This research work employs BCI technology for a medical application that gives the unfortunate paralyzed individuals the capability to interact with their surroundings solely using voluntary eye blinks. This research contributes to the existing technology to be more feasible by introducing a modular design with three physically separated components: headwear, a computer, and a wheelchair. As the signal-to-noise ratio (SNR) of the existing systems is too high to separate the eye blink artifacts from the regular EEG signal, a precise ThinkGear module is used which acquired the raw EEG signal through a single dry electrode. An embedded Bluetooth module acquires and transfers the signals wirelessly to a computer. A MATLAB program captures voluntary eye blink artifacts from the brain waves and commands the movement of a miniature wheelchair via Bluetooth. To distinguish voluntary eye blinks from involuntary eye blinks, blink strength thresholds are determined. A Graphical User Interface (GUI) designed in MATLAB displays the EEG waves in realtime and enables the user to determine the movements of the wheelchair which is specially designed to take commands from the GUI. The findings from the testing phase unveil the advantages of a modular design and the efficacy of using eye blink artifacts as the control element for brain-controlled wheelchairs. The wheelchair attained a command detection and execution accuracy of 96.4% which is an improvement from the existing systems. The work presented here gives a basic understanding of the functionality of a BCI system and provides eye blink-controlled navigation of a wheelchair for patients suffering from severe paralysis.

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Section: Test Resultsmentioning

confidence: 99%

Section: Headwearmentioning

confidence: 99%

Brain-Computer Interfacing for Wheelchair Control by Detecting Voluntary Eye Blinks

Francis

Umayal

Kanimozhi

2021

IJEEI

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“…Different pre-processing techniques including DC offset correction (i.e., high pass filter at 0.1 Hz), electrical interference removal (notch filter at 60 Hz) and bandpass filtration has been applied to clean EEG data. The raw EEG data was bandpass filtered between 7 Hz and 30 Hz to eliminate all the frequency components other than mu and beta as the studies [7][8][9] revealed the occurrence of MI patterns in the stated frequency range.…”

Section: Methodsmentioning

confidence: 99%

Partial Directed Coherence for the Classification of Motor Imagery-Based Brain-Computer Interface

Awais¹,

Yusoff²

2022

Proceedings of the Multimedia University Engineering Conference (MECON 2022)

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In recent years, the research community around the globe has contributed significantly to improve the brain-computer interface based assistive technologies. Electroencephalographic brain-computer interface enables the person to communicate with the outside world by creating an advanced communication protocol between the brain and the computer. Motor imagery-based BCIs aim to predict the specific patterns elicited by imagining some planned movements. Standard BCI systems incorporate the use of spatial features from the motor cortex. However, several researchers claim to have the intercommunication of different brain regions during the motor task. Thus, a unique approach like brain connectivity is essential to extract the intercommunication of brain regions through several electrode channels during a MI task. In this work, brain effective connectivity has been estimated using partial directed coherence, and it has been used as the feature extraction method. An extensive 2-class motor imagery dataset from Physionet database incorporating 91 subjects has been used for the validation purposes. Our proposed work reached the average classification accuracy of 97.45% using an SVM classifier. The findings of this study revealed the significance of brain connectivity features over the conventional features extracted from a single brain region.

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“…A high-pass filter was applied at 0.1 Hz for the purpose of DC offset correction, while a notch filter was used at 60 Hz to eliminate the electrical interference. The raw signal was bandpass filtered between 7 and 32 Hz to exclude all of the frequency components other than mu and beta, as the studies [ 43 , 44 , 45 ] revealed the occurrence of MI patterns in the stated frequency range. However, the artifacts were removed using the EEGLAB-based artifact removal algorithm called artifact subspace reconstruction (ASR).…”

Section: Methodsmentioning

confidence: 99%

Effective Connectivity for Decoding Electroencephalographic Motor Imagery Using a Probabilistic Neural Network

Awais

Yusoff

Khan

et al. 2021

Sensors

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Motor imagery (MI)-based brain–computer interfaces have gained much attention in the last few years. They provide the ability to control external devices, such as prosthetic arms and wheelchairs, by using brain activities. Several researchers have reported the inter-communication of multiple brain regions during motor tasks, thus making it difficult to isolate one or two brain regions in which motor activities take place. Therefore, a deeper understanding of the brain’s neural patterns is important for BCI in order to provide more useful and insightful features. Thus, brain connectivity provides a promising approach to solving the stated shortcomings by considering inter-channel/region relationships during motor imagination. This study used effective connectivity in the brain in terms of the partial directed coherence (PDC) and directed transfer function (DTF) as intensively unconventional feature sets for motor imagery (MI) classification. MANOVA-based analysis was performed to identify statistically significant connectivity pairs. Furthermore, the study sought to predict MI patterns by using four classification algorithms—an SVM, KNN, decision tree, and probabilistic neural network. The study provides a comparative analysis of all of the classification methods using two-class MI data extracted from the PhysioNet EEG database. The proposed techniques based on a probabilistic neural network (PNN) as a classifier and PDC as a feature set outperformed the other classification and feature extraction techniques with a superior classification accuracy and a lower error rate. The research findings indicate that when the PDC was used as a feature set, the PNN attained the greatest overall average accuracy of 98.65%, whereas the same classifier was used to attain the greatest accuracy of 82.81% with the DTF. This study validates the activation of multiple brain regions during a motor task by achieving better classification outcomes through brain connectivity as compared to conventional features. Since the PDC outperformed the DTF as a feature set with its superior classification accuracy and low error rate, it has great potential for application in MI-based brain–computer interfaces.

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Brain Controlled Wheelchair: A Smart Prototype

Cited by 8 publications

References 9 publications

Brain-Computer Interfacing for Wheelchair Control by Detecting Voluntary Eye Blinks

Brain-Computer Interfacing for Wheelchair Control by Detecting Voluntary Eye Blinks

Partial Directed Coherence for the Classification of Motor Imagery-Based Brain-Computer Interface

Effective Connectivity for Decoding Electroencephalographic Motor Imagery Using a Probabilistic Neural Network

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