Restoration of Whole Body Movement: Toward a Noninvasive Brain-Machine Interface System

Contreras-Vidal, José L.; Presacco, Alessandro; Agashe, Harshavardhan A.; Paek, Andrew

doi:10.1109/mpul.2011.2175635

Cited by 27 publications

(14 citation statements)

References 6 publications

(5 reference statements)

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“…It assists stroke patients modifying their thinking behavior to resemble the recorded signals and retraining healthy areas of the brain to take over. Another approach for rehabilitation involves virtual reality through monitoring and controlling avatar movement generated from the outgoing brain waves [51]. Augmented reality represents the third approach in the reality based BCI treatment such as augmented mirror box system which appears as a development for Mirror Box Therapy (MBT).…”

Section: Rehabilitation and Restorationmentioning

confidence: 99%

Brain computer interfacing: Applications and challenges

Abdulkader

Atia

Mostafa

2015

Egyptian Informatics Journal

427

247

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Brain computer interface technology represents a highly growing field of research with application systems. Its contributions in medical fields range from prevention to neuronal rehabilitation for serious injuries. Mind reading and remote communication have their unique fingerprint in numerous fields such as educational, self-regulation, production, marketing, security as well as games and entertainment. It creates a mutual understanding between users and the surrounding systems. This paper shows the application areas that could benefit from brain waves in facilitating or achieving their goals. We also discuss major usability and technical challenges that face brain signals utilization in various components of BCI system. Different solutions that aim to limit and decrease their effects have also been reviewed.Ó 2015 Production and hosting by Elsevier B.V. on behalf

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Section: Rehabilitation and Restorationmentioning

confidence: 99%

Brain computer interfacing: Applications and challenges

Abdulkader

Atia

Mostafa

2015

Egyptian Informatics Journal

427

247

View full text Add to dashboard Cite

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“…One of the most challenging topics in BCI is finding and analyzing the relationships between recorded brain activity and underlying models of the human body, biomechanics, and cognitive processing. As a result, investigation of relationships between EEG signals and upper limb movement, real and imaginary, has become a fascinating area of research in recent years [26,27].…”

Section: Introductionmentioning

confidence: 99%

A comprehensive review of EEG-based brain–computer interface paradigms

et al. 2019

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Advances in brain science and computer technology in the past decade have led to exciting developments in brain–computer interface (BCI), thereby making BCI a top research area in applied science. The renaissance of BCI opens new methods of neurorehabilitation for physically disabled people (e.g. paralyzed patients and amputees) and patients with brain injuries (e.g. stroke patients). Recent technological advances such as wireless recording, machine learning analysis, and real-time temporal resolution have increased interest in electroencephalographic (EEG) based BCI approaches. Many BCI studies have focused on decoding EEG signals associated with whole-body kinematics/kinetics, motor imagery, and various senses. Thus, there is a need to understand the various experimental paradigms used in EEG-based BCI systems. Moreover, given that there are many available options, it is essential to choose the most appropriate BCI application to properly manipulate a neuroprosthetic or neurorehabilitation device. The current review evaluates EEG-based BCI paradigms regarding their advantages and disadvantages from a variety of perspectives. For each paradigm, various EEG decoding algorithms and classification methods are evaluated. The applications of these paradigms with targeted patients are summarized. Finally, potential problems with EEG-based BCI systems are discussed, and possible solutions are proposed.

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“…This will greatly improve efficiency of task performance, and therefore training can be translated to functional task settings. This is an important step to accelerate functional recovery using combined BMI-robotic assisted therapy, because clinical rehabilitation requires adaptive training of functional tasks in real-life contexts or settings [65]. …”

Section: Combining Bmi and Robotic-assisted Rehabilitation: Challengementioning

confidence: 99%

Applications of Brain–Machine Interface Systems in Stroke Recovery and Rehabilitation

Venkatakrishnan

Francisco

Contreras-Vidal

2014

Curr Phys Med Rehabil Rep

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Stroke is a leading cause of disability, significantly impacting the quality of life (QOL) in survivors, and rehabilitation remains the mainstay of treatment in these patients. Recent engineering and technological advances such as brain-machine interfaces (BMI) and robotic rehabilitative devices are promising to enhance stroke neu-rorehabilitation, to accelerate functional recovery and improve QOL. This review discusses the recent applications of BMI and robotic-assisted rehabilitation in stroke patients. We present the framework for integrated BMI and robotic-assisted therapies, and discuss their potential therapeutic, assistive and diagnostic functions in stroke rehabilitation. Finally, we conclude with an outlook on the potential challenges and future directions of these neurotechnologies, and their impact on clinical rehabilitation.

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Restoration of Whole Body Movement: Toward a Noninvasive Brain-Machine Interface System

Cited by 27 publications

References 6 publications

Brain computer interfacing: Applications and challenges

Brain computer interfacing: Applications and challenges

A comprehensive review of EEG-based brain–computer interface paradigms

Applications of Brain–Machine Interface Systems in Stroke Recovery and Rehabilitation

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