Research and Development of a Brain-Controlled Wheelchair for Paralyzed Patients

Khan, Mohammad Monirujjaman; Safa, Shamsun Nahar; Ashik, Minhazul Hoque; Masud, Mehedi; AlZain, Mohammed A.

doi:10.32604/iasc.2021.016077

Cited by 23 publications

(4 citation statements)

References 13 publications

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“…High-quality signal acquisition and advanced data processing techniques enable BCI systems to achieve satisfactory performance in imaging, assisting, augmenting, and rehabilitating human cognitive and sensorimotor functions. Specifically, BCIs can be used to help restore motor function in the following ways [4,50]: (i) real-time feedback, such as the representation of a performed action in VR; (ii) control of training devices causing actual movement with haptic, FES, or robotics feedback to develop a hybrid system during rehabilitation to gain correct movement posture [75]; (iii) control of external devices, such as wheelchair or prosthesis [76,77].…”

Section: Neuroscience and Clinical Applicationsmentioning

confidence: 99%

fNIRS-EEG BCIs for Motor Rehabilitation: A Review

Chen,

Xia,

Zhou

et al. 2023

Bioengineering

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Motor impairment has a profound impact on a significant number of individuals, leading to a substantial demand for rehabilitation services. Through brain–computer interfaces (BCIs), people with severe motor disabilities could have improved communication with others and control appropriately designed robotic prosthetics, so as to (at least partially) restore their motor abilities. BCI plays a pivotal role in promoting smoother communication and interactions between individuals with motor impairments and others. Moreover, they enable the direct control of assistive devices through brain signals. In particular, their most significant potential lies in the realm of motor rehabilitation, where BCIs can offer real-time feedback to assist users in their training and continuously monitor the brain’s state throughout the entire rehabilitation process. Hybridization of different brain-sensing modalities, especially functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), has shown great potential in the creation of BCIs for rehabilitating the motor-impaired populations. EEG, as a well-established methodology, can be combined with fNIRS to compensate for the inherent disadvantages and achieve higher temporal and spatial resolution. This paper reviews the recent works in hybrid fNIRS-EEG BCIs for motor rehabilitation, emphasizing the methodologies that utilized motor imagery. An overview of the BCI system and its key components was introduced, followed by an introduction to various devices, strengths and weaknesses of different signal processing techniques, and applications in neuroscience and clinical contexts. The review concludes by discussing the possible challenges and opportunities for future development.

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Section: Neuroscience and Clinical Applicationsmentioning

confidence: 99%

fNIRS-EEG BCIs for Motor Rehabilitation: A Review

Chen,

Xia,

Zhou

et al. 2023

Bioengineering

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“…In the study by M. A. Khan, A. Hussain, M. Z. Iqbal, S. Ahmad, S. S. Raza, and M. Q. Islam, published in 2019 on pages 47-52 [8], the researchers present a model for a wheelchair that can be controlled by the brain. This model aims to assist patients who are disabled or paralyzed.…”

Section: Literature Surveymentioning

confidence: 99%

Revolutionizing Accessibility: Smart Wheelchair Robot and Mobile Application for Mobility, Assistance, and Home Management

Jayasekara,

Kulathunge,

Premaratne

et al. 2023

Journal of Robotics and Control

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This research aims to advance accessibility and inclusivity for individuals with disabilities. We focus on specific daily challenges facing people with disabilities in communication, mobility, and daily task management and introduce AssistEase, a groundbreaking smart wheelchair solution designed to empower people with disabilities by improving mobility, communication capabilities, and daily task management. AssistEase will contribute to the disabled community around the world by allowing them to manage daily tasks and communicate more easily while ensuring mobility. AssistEase offers control options such as handsfree voice control, traditional manual control, smartphone-based Bluetooth control, or innovative gesture control, designed to cater to different user preferences and needs. This uses technologies such as speech recognition, computer vision, and haptic [92] feedback to help users navigate safely while avoiding obstacles. It integrates technologies like Flutter, TensorFlow, YOLOV8, Global Positioning System (GPS), Bluetooth, and Apple Home Kit, along with hardware components including Arduino and Raspberry PI. Preliminary trials have shown improvements in mobility, communication, and daily tasks for users in need. It achieves 95% precision in guiding wheelchair users while maintaining about 90% accuracy for the robotic arm and 89% for health monitoring and location tracking. Also, it provides a user-friendly app with 90% control accuracy. The communication device has 92% accuracy in facilitating user communication, while hand gesture control achieves 90% accuracy. To advance AssistEase smart wheelchair technology, further research, and development are required to enhance its adaptability for specific disabilities. AssistEase reflects a commitment to creating a more inclusive and thriving society, focusing on innovation and inclusion for individuals of all abilities.

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“…It can realize direct communication between humans and computers by converting neural activities into machine commands, without language or body expression [1]. Since electroencephalogram (EEG) signals are noninvasive, inexpensive and have high temporal resolution, they have been widely used in BCI systems, such as BCI spellers [2,3], brain-controlled mobile robots [4], brain-controlled wheelchairs [5,6], and robotic arms [7].…”

Section: Introductionmentioning

confidence: 99%

Multi-source domain adaptation based tempo-spatial convolution network for cross-subject EEG classification in RSVP task

Wang,

Li,

Lin

et al. 2024

J. Neural Eng.

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Objective. Many subject-dependent methods were proposed for electroencephalogram (EEG) classification in rapid serial visual presentation (RSVP) task, which required a large amount of data from new subject and were time-consuming to calibrate system. Cross-subject classification can realize calibration reduction or zero calibration. However, cross-subject classification in RSVP task is still a challenge. Approach. This study proposed a multi-source domain adaptation based tempo-spatial convolution (MDA-TSC) network for cross-subject RSVP classification. The proposed network consisted of three modules. First, the common feature extraction with multi-scale tempo-spatial convolution was constructed to extract domain-invariant features across all subjects, which could improve generalization of the network. Second, the multi-branch domain-specific feature extraction and alignment was conducted to extract and align domain-specific feature distributions of source and target domains in pairs, which could consider feature distribution differences among source domains. Third, the domain-specific classifier was exploited to optimize the network through loss functions and obtain prediction for the target domain. Main results. The proposed network was evaluated on the benchmark RSVP dataset, and the cross-subject classification results showed that the proposed MDA-TSC network outperformed the reference methods. Moreover, the effectiveness of the MDA-TSC network was verified through both ablation studies and visualization. Significance. The proposed network could effectively improve cross-subject classification performance in RSVP task, and was helpful to reduce system calibration time.

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Research and Development of a Brain-Controlled Wheelchair for Paralyzed Patients

Cited by 23 publications

References 13 publications

fNIRS-EEG BCIs for Motor Rehabilitation: A Review

fNIRS-EEG BCIs for Motor Rehabilitation: A Review

Revolutionizing Accessibility: Smart Wheelchair Robot and Mobile Application for Mobility, Assistance, and Home Management

Multi-source domain adaptation based tempo-spatial convolution network for cross-subject EEG classification in RSVP task

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