Comprehensive review on brain-controlled mobile robots and robotic arms based on electroencephalography signals

Aljalal, Majid; Ibrahim, Sutrisno; Djemal, Ridha; Ko, Wonsuk

doi:10.1007/s11370-020-00328-5

Cited by 47 publications

(46 citation statements)

References 162 publications

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“…MI has been shown to activate the primary motor cortex (M1) and related motor areas and elicits Event Related Desynchronization (ERD) and Event Related Synchronization (ERS), which reflect power changes in the mu band. Several studies have shown that stroke patients can elicit ERD/ERS during MI of their paralyzed hand and during passive movement provided by robotic assistive devices (Pfurtscheller and Aranibar, 1979;Neuper et al, 2006;Aljalal et al, 2020). Furthermore, movement-related neural activity was found to be present both in the contralateral and ipsilateral side depending on movement complexity (unilateral or bilateral) and the proximity of the muscle groups to the sagittal plane of the body (shoulder or hand).…”

Section: Introductionmentioning

confidence: 99%

Brain Computer Interface Treatment for Motor Rehabilitation of Upper Extremity of Stroke Patients—A Feasibility Study

et al. 2020

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Introduction: Numerous recent publications have explored Brain Computer Interfaces (BCI) systems as rehabilitation tools to help subacute and chronic stroke patients recover upper extremity movement. Recent work has shown that BCI therapy can lead to better outcomes than conventional therapy. BCI combined with other techniques such as Functional Electrical Stimulation (FES) and Virtual Reality (VR) allows to the user restore the neurological function by inducing the neural plasticity through improved real-time detection of motor imagery (MI) as patients perform therapy tasks. Methods: Fifty-one stroke patients with upper extremity hemiparesis were recruited for this study. All participants performed 25 sessions with the MI BCI and assessment visits to track the functional changes before and after the therapy. Results: The results of this study demonstrated a significant increase in the motor function of the paretic arm assessed by Fugl-Meyer Assessment (FMA-UE), FMA-UE = 4.68 points, P < 0.001, reduction of the spasticity in the wrist and fingers assessed by Modified Ashworth Scale (MAS), MAS-wrist =-0.72 points (SD = 0.83), P < 0.001, MAS-fingers =-0.63 points (SD = 0.82), P < 0.001. Other significant improvements in the grasp ability were detected in the healthy hand. All these functional improvements achieved during the BCI therapy persisted 6 months after the therapy ended. Results also showed that patients with Motor Imagery accuracy (MI) above 80% increase 3.16 points more in the FMA than patients below this threshold (95% CI; [1.47-6.62], P = 0.003). The functional improvement was not related with the stroke severity or with the stroke stage. Conclusion: The BCI treatment used here was effective in promoting long lasting functional improvements in the upper extremity in stroke survivors with severe, moderate and mild impairment. This functional improvement can be explained by improved neuroplasticity in the central nervous system.

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Section: Introductionmentioning

confidence: 99%

Brain Computer Interface Treatment for Motor Rehabilitation of Upper Extremity of Stroke Patients—A Feasibility Study

et al. 2020

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“…Following the definition given in [13], the wheelchair is classified as a mobile robot that can navigate two dimensions.…”

Section: MI Eeg-based Bcis In Wheelchairs Movement and Control: Literature Resultsmentioning

confidence: 99%

“…Many EEG data acquisition devices are available in commerce, which vary in the number of channels, sampling rate, electrode connection type, headset preparation time, and price [13][66] [67].…”

Section: A Signal Acquisitionmentioning

confidence: 99%

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Motor-Imagery EEG-based BCIs in Wheelchairs Movement and Control: A Systematic Literature Review

Palumbo¹,

Gramigna²,

Calabrese³

et al. 2021

Preprint

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The pandemic emergency of the coronavirus disease 2019 (COVID-19) shed light on the need for innovative aids, devices and assistive technologies to enable people with severe disabilities to live their daily lives. EEG-based Brain-Computer Interfaces (BCIs) can lead individuals with significant health challenges to improve their independence, facilitate participation in activities, thus enhancing overall well-being and preventing impairments. This systematic review provides state-of-the-art applications of EEG-based BCIs, particularly those using motor-imagery (MI) data, to wheelchair control and movement. It presents a thorough examination of the different studies conducted since 2010, focusing on the algorithm analysis, features extraction, features selection and classification techniques used, and wheelchair components and performance evaluation. The results provided in this paper could highlight the limitations of current biomedical instrumentations applied to people with severe disabilities in the pandemic context of Covid-19 and bring focus to innovative research topics.

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“…Other braincomputer interfaces have been applied in varying fields [7]. Commercially, Research Laboratories have been developing a system that operate equipment using EEG signal, for example, moving electric beds and wheelchairs by measuring subtle changes in blood flow in the brain and sends the signal to electronic devices via a network [8], [9]. The Army Combat Capabilities Development Command's Army Research Laboratory has also shown interest in the usage of EEG towards cognition and ergonomics of military operations [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Estimation of the Shoulder Joint Angle using Brainwaves

Sasaki

Iida

Muguro

et al. 2021

AJEEET

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This paper presents the angle of the shoulder joint as basic research for developing a machine interface using EEG. The raw EEG voltage signals and power density spectrum of the voltage value were used as the learning feature. Hebbian learning was used on a multilayer perceptron network for pattern classification for the estimation of joint angles 0o, 90o and 180o of the shoulder joint. Experimental results showed that it was possible to correctly classify up to 63.3% of motion using voltage values of the raw EEG signal with the neural network. Further, with selected electrodes and power density spectrum features, accuracy rose to 93.3% with more stable motion estimation.

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Comprehensive review on brain-controlled mobile robots and robotic arms based on electroencephalography signals

Cited by 47 publications

References 162 publications

Brain Computer Interface Treatment for Motor Rehabilitation of Upper Extremity of Stroke Patients—A Feasibility Study

Brain Computer Interface Treatment for Motor Rehabilitation of Upper Extremity of Stroke Patients—A Feasibility Study

Motor-Imagery EEG-based BCIs in Wheelchairs Movement and Control: A Systematic Literature Review

Estimation of the Shoulder Joint Angle using Brainwaves

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