Brain-Computer Interfaces in Quadriplegic Patients

Lee, Morgan B.; Kramer, Daniel R.; Peng, Terrance; Barbaro, Michael F.; Liu, Charles Y.; Kellis, Spencer; Lee, Brian

doi:10.1016/j.nec.2018.12.009

Cited by 15 publications

(8 citation statements)

References 37 publications

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“…Its capability of partially mixed encoding in which neurons respond to multiple variables only adds to its strength [80,81]. First studies in tetraplegic patients showed great potential for PPC implants, in line with previous animal studies [82][83][84].…”

Section: Potential Target For Rehabilitationsupporting

confidence: 71%

“…Adding an implant in the somatosensory cortex, which is connected with touch sensors on the prosthetic device, can foresee in feedback to the PPC related to peripersonal space and body position, enabling a more precise control ( Fig. 4B) [79,[84][85][86]. This is also observed in hemiplegic upper limb paralysis due to stroke, where the amount of somatosensory feedback will contribute to the patients rehabilitation outcome via robot training [87].…”

Section: Potential Target For Rehabilitationmentioning

confidence: 99%

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Posterior parietal cortex contributions to cross-modal brain plasticity upon sensory loss

Gilissen

Arckens

2021

Current Opinion in Neurobiology

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Section: Potential Target For Rehabilitationsupporting

confidence: 71%

Section: Potential Target For Rehabilitationmentioning

confidence: 99%

Posterior parietal cortex contributions to cross-modal brain plasticity upon sensory loss

Gilissen

Arckens

2021

Current Opinion in Neurobiology

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“…It was found that by using a novel neural network pattern based on low level commands alone have led to the development of brain signal interface with the system. Research work on quadriplegic patients by Morgan B Lee [7] developed the brain computer interface system that restored the function of quadriplegic patients through virtual prosthetics, FES systems. The outcome was that the motor BCI hardware and software successfully controls the effectors that by using advanced physical prosthetics, FES systems can be able to restore function of the patients own limb.…”

Section: Brain Computer Interfacementioning

confidence: 99%

In situ developmental control of biomedical applications using human brain

S¹

2020

IJATCSE

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This article describes the development of BCI (Brain computer Interface) that allows individuals to record their brain activity and then utilize it for useful application. A virtual reality system is connected with EEG device for smart home application. It paves way for usual way of control and communication. Its an artificial system that sidesteps the neuromuscular output terminals which are efficient pathways of our body. Different brain states resulting due to neural interactions lead to the development of patterns which are characterized by different amplitudes and frequencies. A minute electrical discharge is observed for every interaction between neurons received by brain which is separated into packets which are transmitted through a remoteway of communication (Bluetooth). The wave measuring unit will receive the immediate first information of the brain wave directly through Arduino Uno Controller. Next the instruction is sent to the home section. Taking a notice of the brain action, certain thoughts and eye squinting pattern, relationship were established between exchanging and regulation of certain home appliances.

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“…For severely disabled people, the development of BCIs could be the most important technological breakthrough in decades (2). BCIs, which represent technologies designed…”

Section: Introductionmentioning

confidence: 99%

The combination of brain-computer interfaces and artificial intelligence: applications and challenges

Zhang¹,

Ma²,

Zheng³

et al. 2020

Ann Transl Med

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Brain-computer interfaces (BCIs) have shown great prospects as real-time bidirectional links between living brains and actuators. Artificial intelligence (AI), which can advance the analysis and decoding of neural activity, has turbocharged the field of BCIs. Over the past decade, a wide range of BCI applications with AI assistance have emerged. These "smart" BCIs including motor and sensory BCIs have shown notable clinical success, improved the quality of paralyzed patients' lives, expanded the athletic ability of common people and accelerated the evolution of robots and neurophysiological discoveries. However, despite technological improvements, challenges remain with regard to the long training periods, real-time feedback, and monitoring of BCIs. In this article, the authors review the current state of AI as applied to BCIs and describe advances in BCI applications, their challenges and where they could be headed in the future.

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Brain-Computer Interfaces in Quadriplegic Patients

Cited by 15 publications

References 37 publications

Posterior parietal cortex contributions to cross-modal brain plasticity upon sensory loss

Posterior parietal cortex contributions to cross-modal brain plasticity upon sensory loss

In situ developmental control of biomedical applications using human brain

The combination of brain-computer interfaces and artificial intelligence: applications and challenges

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