EEG-SSVEP based Brain Machine Interface for Controlling of a Wheelchair and Home Appliances with Bluetooth Localization System

Ruhunage, Isuru; Perera, Chamika Janith; Munasinghe, Induwara; Lalitharatne, Thilina Dulantha

doi:10.1109/robio.2018.8664809

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…Xin et al [21] employed the eye-tracking and EEG to control the movement of the wheelchair. Deng et al [22] used a Bayesian shared control strategy based on steady-state visual evoked potential (SSVEP) and BMI for wheelchair navigation; whereas, Ruhunage et al [23] proposed a BMI based on SSVEP of EEG signals to recognize user's intention for controlling the wheelchair and home appliances by using a bluetooth localization system. In [24] the BMI system based on fuzzy neural networks for brain-actuated control of wheelchair.…”

Section: Related Workmentioning

confidence: 99%

A Novel Approach to Shadow Boundary Detection Based on an Adaptive Direction-Tracking Filter for Brain-Machine Interface Applications

Hussain

et al. 2020

Applied Sciences

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In this paper, a Brain-Machine Interface (BMI) system is proposed to automatically control the navigation of wheelchairs by detecting the shadows on their route. In this context, a new algorithm to detect shadows in a single image is proposed. Specifically, a novel adaptive direction tracking filter (ADT) is developed to extract feature information along the direction of shadow boundaries. The proposed algorithm avoids extraction of features around all directions of pixels, which significantly improves the efficiency and accuracy of shadow features extraction. Higher-order statistics (HOS) features such as skewness and kurtosis in addition to other optical features are used as input to different Machine Learning (ML) based classifiers, specifically, a Multilayer Perceptron (MLP), Autoencoder (AE), 1D-Convolutional Neural Network (1D-CNN) and Support Vector Machine (SVM), to perform the shadow boundaries detection task. Comparative results demonstrate that the proposed MLP-based system outperforms all the other state-of-the-art approaches, reporting accuracy rates up to 84.63%.

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Section: Related Workmentioning

confidence: 99%

A Novel Approach to Shadow Boundary Detection Based on an Adaptive Direction-Tracking Filter for Brain-Machine Interface Applications

Hussain

et al. 2020

Applied Sciences

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“…This method yielded an average accuracy of 93.9%. Ruhunage et al [ 40 ] developed an SSVEP stimulus combined with an Electrooculogram (EOG) to control wheelchairs and other home devices. They used visually stimulating LEDs to control wheelchairs and to change modes.…”

Section: Introductionmentioning

confidence: 99%

Brain–Computer Interface Based on Steady-State Visual Evoked Potential Using Quick-Response Code Pattern for Wheelchair Control

Siribunyaphat

Punsawad

2023

Sensors

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Brain–computer interfaces (BCIs) are widely utilized in control applications for people with severe physical disabilities. Several researchers have aimed to develop practical brain-controlled wheelchairs. An existing electroencephalogram (EEG)-based BCI based on steady-state visually evoked potential (SSVEP) was developed for device control. This study utilized a quick-response (QR) code visual stimulus pattern for a robust existing system. Four commands were generated using the proposed visual stimulation pattern with four flickering frequencies. Moreover, we employed a relative power spectrum density (PSD) method for the SSVEP feature extraction and compared it with an absolute PSD method. We designed experiments to verify the efficiency of the proposed system. The results revealed that the proposed SSVEP method and algorithm yielded an average classification accuracy of approximately 92% in real-time processing. For the wheelchair simulated via independent-based control, the proposed BCI control required approximately five-fold more time than the keyboard control for real-time control. The proposed SSVEP method using a QR code pattern can be used for BCI-based wheelchair control. However, it suffers from visual fatigue owing to long-time continuous control. We will verify and enhance the proposed system for wheelchair control in people with severe physical disabilities.

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A portable SSVEP-BCI system for rehabilitation exoskeleton in augmented reality environment

Wang

Wen

et al. 2023

Biomedical Signal Processing and Control

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EEG-SSVEP based Brain Machine Interface for Controlling of a Wheelchair and Home Appliances with Bluetooth Localization System

Cited by 4 publications

References 16 publications

A Novel Approach to Shadow Boundary Detection Based on an Adaptive Direction-Tracking Filter for Brain-Machine Interface Applications

A Novel Approach to Shadow Boundary Detection Based on an Adaptive Direction-Tracking Filter for Brain-Machine Interface Applications

Brain–Computer Interface Based on Steady-State Visual Evoked Potential Using Quick-Response Code Pattern for Wheelchair Control

A portable SSVEP-BCI system for rehabilitation exoskeleton in augmented reality environment

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