A Cloud-based Brain-controlled Wheelchair with Autonomous Indoor Navigation System

Lakas, Abderrahmane; Kharbash, Fekri; Belkacem, Abdelkader Nasreddine

doi:10.1109/iwcmc51323.2021.9498751

Cited by 6 publications

(4 citation statements)

References 24 publications

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“…The system in [ 19 ] only works with two options when at a junction. Although the low-level control logic can rely on visual paradigms only, as in [ 6 , 7 , 14 , 15 , 16 , 17 , 18 ], it is rather impractical to navigate a city with.…”

Section: Discussionmentioning

confidence: 99%

“…P300 is an event-related potential evoked by presenting a rarely occurring stimulus [ 3 ], whereas SSVEP is a periodic response evoked by a fixed-frequency visual stimulus [ 4 ]. They are used to select one of several displayed targets (i.e., corresponding to navigation commands) [ 6 , 7 , 14 , 15 , 16 , 17 , 18 ]; since users are cued by the visual stimuli, they are examples of so-called synchronous BCIs. On the other hand, SMR can be asynchronous, as the changes in mu and beta band power are elicited by self-paced imagined limb movements [ 19 ].…”

Section: Literature Reviewmentioning

confidence: 99%

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Real-Time Navigation in Google Street View® Using a Motor Imagery-Based BCI

Yang

Hulle

2023

Sensors

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Navigation in virtual worlds is ubiquitous in games and other virtual reality (VR) applications and mainly relies on external controllers. As brain–computer interfaces (BCI)s rely on mental control, bypassing traditional neural pathways, they provide to paralyzed users an alternative way to navigate. However, the majority of BCI-based navigation studies adopt cue-based visual paradigms, and the evoked brain responses are encoded into navigation commands. Although robust and accurate, these paradigms are less intuitive and comfortable for navigation compared to imagining limb movements (motor imagery, MI). However, decoding motor imagery from EEG activity is notoriously challenging. Typically, wet electrodes are used to improve EEG signal quality, including a large number of them to discriminate between movements of different limbs, and a cuedbased paradigm is used instead of a self-paced one to maximize decoding performance. Motor BCI applications primarily focus on typing applications or on navigating a wheelchair—the latter raises safety concerns—thereby calling for sensors scanning the environment for obstacles and potentially hazardous scenarios. With the help of new technologies such as virtual reality (VR), vivid graphics can be rendered, providing the user with a safe and immersive experience; and they could be used for navigation purposes, a topic that has yet to be fully explored in the BCI community. In this study, we propose a novel MI-BCI application based on an 8-dry-electrode EEG setup, with which users can explore and navigate in Google Street View®. We pay attention to system design to address the lower performance of the MI decoder due to the dry electrodes’ lower signal quality and the small number of electrodes. Specifically, we restricted the number of navigation commands by using a novel middle-level control scheme and avoided decoder mistakes by introducing eye blinks as a control signal in different navigation stages. Both offline and online experiments were conducted with 20 healthy subjects. The results showed acceptable performance, even given the limitations of the EEG set-up, which we attribute to the design of the BCI application. The study suggests the use of MI-BCI in future games and VR applications for consumers and patients temporarily or permanently devoid of muscle control.

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Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Real-Time Navigation in Google Street View® Using a Motor Imagery-Based BCI

Yang

Hulle

2023

Sensors

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“…An intelligent BCI-based wheelchair for autonomous indoor navigation was developed in ref. [43]. The P300 signal is detected and decoded using the Linear Discriminant Analysis (LDA) algorithm to achieve a real-time classification accuracy of 88.75%.…”

Section: Eeg-based Systemsmentioning

confidence: 99%

BIO‐inspired fuzzy inference system—For physiological signal analysis

Suppiah

Kim

Abidi

et al. 2023

IET Cyber-Syst and Robotics

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When a person's neuromuscular system is affected by an injury or disease, Activities‐for‐Daily‐Living (ADL), such as gripping, turning, and walking, are impaired. Electroencephalography (EEG) and Electromyography (EMG) are physiological signals generated by a body during neuromuscular activities embedding the intentions of the subject, and they are used in Brain–Computer Interface (BCI) or robotic rehabilitation systems. However, existing BCI or robotic rehabilitation systems use signal classification technique limitations such as (1) missing temporal correlation of the EEG and EMG signals in the entire window and (2) overlooking the interrelationship between different sensors in the system. Furthermore, typical existing systems are designed to operate based on the presence of dominant physiological signals associated with certain actions; (3) their effectiveness will be greatly reduced if subjects are disabled in generating the dominant signals. A novel classification model, named BIOFIS is proposed, which fuses signals from different sensors to generate inter‐channel and intra‐channel relationships. It explores the temporal correlation of the signals within a timeframe via a Long Short‐Term Memory (LSTM) block. The proposed architecture is able to classify the various subsets of a full‐range arm movement that performs actions such as forward, grip and raise, lower and release, and reverse. The system can achieve 98.6% accuracy for a 4‐way action using EEG data and 97.18% accuracy using EMG data. Moreover, even without the dominant signal, the accuracy scores were 90.1% for the EEG data and 85.2% for the EMG data. The proposed mechanism shows promise in the design of EEG/EMG‐based use in the medical device and rehabilitation industries.

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“…Average accuracy recorded was more than 67.7% but the the value was not consistent and may be caused users' mental workload. Similar virtual application applied in [13] where BCI used for issuing highlevel commands to the wheelchair, and an autonomous indoor navigation system component that incorporates all elements of course planning, obstacle detection, and avoidance. The multi-class classification achieved 88.75% accuracy in controlling the virtual wheelchair.…”

Section: B Brain-computer Interface (Bci)mentioning

confidence: 99%

Smart Wheelchairs: A Review on Control Methods

Aziz

Khusaini

Mohamed

et al. 2022

2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)

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Recent advancement in research fields involving computer science and Artificial Intelligence has influence the rapid growth in smart wheelchair research. Another factor adding to this growth is the evolution of sensor and computing technology. Basically, a smart wheelchair is a powered wheelchair that has been modified by adding necessary sensors and instruments that able to read, collect and send information that can be used to modify the status of the wheelchair, as well as interacting with the environment or the user. The modification can be done on various part of the wheelchair system, and can be categorized with their input methods, operating system or their navigation system itself. This paper discussing on various input methods used in recent smart wheelchair research and summarizes the ideas presented.

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A Cloud-based Brain-controlled Wheelchair with Autonomous Indoor Navigation System

Cited by 6 publications

References 24 publications

Real-Time Navigation in Google Street View® Using a Motor Imagery-Based BCI

Real-Time Navigation in Google Street View® Using a Motor Imagery-Based BCI

BIO‐inspired fuzzy inference system—For physiological signal analysis

Smart Wheelchairs: A Review on Control Methods

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