Design of an EEG-Based Brain Controlled Wheelchair for Quadriplegic Patients

Dev, Antora; Rahman, Md. Asifur; Mamun, Nursadul

doi:10.1109/i2ct.2018.8529751

Cited by 27 publications

(16 citation statements)

References 24 publications

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“…Sensitivity is the proportion of the true positives (desired factor), which is correctly identified from the given test set [20]. The definition of sensitivity can be provided by equation (2), where, TP = True Positive and FN = False Negative.…”

Section: Sensitivity or True Positive Rate (Tpr)mentioning

confidence: 99%

“…To make life easier of the person with quadriplegia and paralyzed patients, many initiatives have been taken, but most of the latest works are eye blink based control systems [2]. But eye blink can not be a good command due to its uneasiness to control.…”

Section: Introductionmentioning

confidence: 99%

“…But eye blink can not be a good command due to its uneasiness to control. In [2], a system is introduced by means of a different range of attention level and double eye blink (both identified by EEG) to ASTESJ ISSN: 2415-6698 make a brain-controlled wheelchair. Four range of attention level as four direction command and double eye blink as on/off command were used at the preliminary level.…”

Section: Introductionmentioning

confidence: 99%

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Development of an EEG Controlled Wheelchair Using Color Stimuli: A Machine Learning Based Approach

Hasan¹,

Hasan²,

Alsubaie³

2021

Adv. sci. technol. eng. syst. j.

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Brain-computer interface (BCI) has extensively been used for rehabilitation purposes.Being in the research phase, the brainwave based wheelchair controlled systems suffer from several limitations, e.g., lack of focus on mental activity, complexity in neural behavior in different conditions, and lower accuracy. Being sensitive to the color stimuli, the EEG signal changes promises a better detection. Utilizing the Electroencephalogram (EEG changes due to different color stimuli, a methodology of wheelchair controlled by brainwaves has been presented in this study. Red, Green, Blue (primary colors) and Yellow (secondary color) were chosen as the color stimuli and utilized in a 2 × 2 color window for four-direction command, namely left and right, forward and stop. Alpha, beta, delta and theta EEG rhythms were analyzed, time and frequency domain features were extracted to find the most influential rhythm and accurate classification model. Four classifiers, namely, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest Classifier (RFC) and Artificial Neural Networks (ANN) were trained and tested for assessing the performance of each of the EEG rhythm, with a five-fold cross-validation. Four different performance measures, i.e. sensitivity, specificity, accuracy and area under the receiver operating characteristic curve were utilized to examine the wholescale performance. The results suggested that Beta EEG rhythm performs the best apart from all the rhythms for the color stimuli based wheelchair control. While comparing the performance of the classifiers, ANN-based classifier shows the best accuracy of 82.5%, which is higher than the performance of the three other classifiers.

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Section: Sensitivity or True Positive Rate (Tpr)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Development of an EEG Controlled Wheelchair Using Color Stimuli: A Machine Learning Based Approach

Hasan¹,

Hasan²,

Alsubaie³

2021

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

show abstract

“…High beta waves become prominent during intensive brain activities and fast information processing. A recent study developed an interface to enable wheelchair control through attentive focus [43]. Another study employed shooting accuracy in archery and coin-stacking height to confirm that human beings' attention in a conscious state far exceeds that in a normal state [44].…”

Section: Related Workmentioning

confidence: 99%

Effects of Symbol Component on the Identifying of Graphic Symbols from EEG for Young Children with and without Developmental Delays

Huang

Lin

2019

Applied Sciences

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Using Augmentative and Alternative Communication (AAC) to improve the communication skills of children with disabilities is generally supported by both domestic and foreign scholars. Graphic symbols that represent individual words or phrases are often used in conjunction with AAC; however, research on the reading and identifying of AAC graphic symbols is scant. Therefore, this study used electroencephalogram (EEG) to investigate the success rates of identifying AAC graphic symbols and brainwave changes of young children with and without developmental delays. The results revealed that the number of symbol components affected participants’ success rates of identifying AAC graphic symbols. The EEG Attention Index between the children with and without developmental delays varied during the test. By contrast, the EEG Relaxation Index exhibited no difference between the children with and without developmental delays. When the participants viewed the single-component animations, the children without developmental delays had a significantly higher Relaxation Index than those with developmental delays did. According to cognitive load theory, the children with developmental delays and low cognitive capacities may feel stressed.

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“…The system that functions to connect humans and machines or computers is called a human-machine interface (HMI) or human-computer interface (HCI). Some types of physiological signals that can be used on HCI systems include voice commands [1], hand movements [2], [3], head movements [4], electroencephalograph signals (EEG) [5]- [7], electrooculography signals (EOG) [8], [9] and electromyography signals (EMG) [9]- [16].…”

Section: Introductionmentioning

confidence: 99%

Wheelchair for Quadriplegic Patient with Electromyography Signal Control Wireless

et al. 2020

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<p class="0abstract">Quadriplegia is a paralysis condition in both arms and legs so that the patient is only able to move his neck and head. Manual or electric wheelchairs with joystick or switch control as a tool for people with paralysis certainly cannot be controlled independently by quadriplegia sufferers. This study aimed to help quadriplegia sufferers not to depend on others in carrying out daily activities by developing electric wheelchairs that can be controlled independently. The bioelectric signal which has only been used for diagnostic purposes can be utilized as an electric wheelchair control system for quadriplegia sufferers. In this study, electric wheelchairs were controlled by electromyography (EMG) signals from muscle contractions that can be driven by quadriplegia sufferers, namely the neck and face muscles. The increase in EMG signal amplitude during the muscle contraction is used as a trigger for the electric motor in a wheelchair to move forward, backward, turn right, and turn left. An electronic circuit for signal conditioning was used to amplify the EMG signal leads and filter frequencies that are not needed by the system before being processed by the microcontroller circuit. The use of wireless systems was developed to reduce the use of cables connecting electrodes to patients with electronic devices that will provide comfort to the user. Based on the results of the data collection on the wheelchair system, the detectability and selectivity values were for the 100% and 94% forward commands, 94.33% and 100% reverse commands, 92.31%, and 96% right turn commands and 97.96% and 94.12% left turn commands. The electric wheelchair system with EMG signal control is expected to help the mobility of quadriplegia sufferers.</p>

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Design of an EEG-Based Brain Controlled Wheelchair for Quadriplegic Patients

Cited by 27 publications

References 24 publications

Development of an EEG Controlled Wheelchair Using Color Stimuli: A Machine Learning Based Approach

Development of an EEG Controlled Wheelchair Using Color Stimuli: A Machine Learning Based Approach

Effects of Symbol Component on the Identifying of Graphic Symbols from EEG for Young Children with and without Developmental Delays

Wheelchair for Quadriplegic Patient with Electromyography Signal Control Wireless

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