Real-time control of a prosthetic hand using human electrocorticography signals

Yanagisawa, Takufumi; Hirata, Masayuki; Saitoh, Youichi; Goto, Tetsu; Kishima, Haruhiko; Fukuma, Ryohei; Yokoi, Hiroshi; Kamitani, Yukiyasu; Yoshimine, Toshiki

doi:10.3171/2011.1.jns101421

Cited by 184 publications

(123 citation statements)

References 31 publications

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“…Takufumi et al used ECoG in stroke patients to decode 3 simple hand movements and revealed that the state and movement type of the patient's hand were predicted with an accuracy of 79.6% (chance 50%) and 68.3% (chance 33.3%), respectively [31]. Using the trained decoders, the onset of the hand movement was detected within 0.37 ± 0.29 seconds of the actual movement.…”

Section: Electrocorticograph (Ecog)mentioning

confidence: 99%

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A Brief Review of Brain Signal Monitoring Technologies for BCI Applications: Challenges and Prospects

Morshed¹

2014

J Bioeng Biomed Sci

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Significant strides have been made since 1940s for monitoring brain activities and utilizing the information for diagnosis, therapy, and control of robotic instruments including prosthetics. Monitoring brain activities with brain computer interfacing (BCI) technologies are of recent interest to due to the immense potential for various medical applications, particularly for many neurological disorder patients, and the emergence of technologies suitable for long duration BCI applications. Recent initiatives are geared towards transforming these clinic centric technologies to patient centric technologies by monitoring brain activities in practical settings. This paper briefly reviews current status of these technologies and relevant challenges. The technologies can be broadly classified into non-invasive (EEG, MEG, MRI) and invasive (Microelectrode, ECoG, MEA). Challenges to resolve include neuronal damage, neurotrophicity, usability and comfort.

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Section: Electrocorticograph (Ecog)mentioning

confidence: 99%

“…#100-167) [31]. Recently, conducting biopolymers are being studied for the use as electrode and not just as a coating material.…”

Section: Other Electrode Typesmentioning

confidence: 99%

A Brief Review of Brain Signal Monitoring Technologies for BCI Applications: Challenges and Prospects

Morshed¹

2014

J Bioeng Biomed Sci

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“…[87][88][89][90][91] An ECoG-based BCI can enable users to control a prosthetic hand or to select characters using motor-imagery or the P300 event-related potential. 12,[92][93][94] Most recently, ECoG signals measured over speech cortex during overt or imag-ined phoneme and word articulation were used for online cursor control 95 and were also accurately decoded off-line for potential application to direct speech synthesis. 96,97 It has been shown that epidural ECoG can provide BCI control, 98 that ECoG-based BCI performance with fixed parameters is stable over at least 5 days, 99 and that motor imagery-based BCI control using locations over motor cortex can produce ECoG changes exceeding those produced by actual movements.…”

Section: Bcis That Use Ecog Activitymentioning

confidence: 99%

Brain-Computer Interfaces in Medicine

Shih

Krusienski

Wolpaw

2012

Mayo Clinic Proceedings

514

266

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Brain-computer interfaces (BCIs) acquire brain signals, analyze them, and translate them into commands that are relayed to output devices that carry out desired actions. BCIs do not use normal neuromuscular output pathways. The main goal of BCI is to replace or restore useful function to people disabled by neuromuscular disorders such as amyotrophic lateral sclerosis, cerebral palsy, stroke, or spinal cord injury. From initial demonstrations of electroencephalography-based spelling and single-neuron-based device control, researchers have gone on to use electroencephalographic, intracortical, electrocorticographic, and other brain signals for increasingly complex control of cursors, robotic arms, prostheses, wheelchairs, and other devices. Brain-computer interfaces may also prove useful for rehabilitation after stroke and for other disorders. In the future, they might augment the performance of surgeons or other medical professionals. Brain-computer interface technology is the focus of a rapidly growing research and development enterprise that is greatly exciting scientists, engineers, clinicians, and the public in general. Its future achievements will depend on advances in 3 crucial areas. Brain-computer interfaces need signal-acquisition hardware that is convenient, portable, safe, and able to function in all environments. Brain-computer interface systems need to be validated in long-term studies of real-world use by people with severe disabilities, and effective and viable models for their widespread dissemination must be implemented. Finally, the day-to-day and moment-to-moment reliability of BCI performance must be improved so that it approaches the reliability of natural muscle-based function. U ntil recently, the dream of being able to control one's environment through thoughts had been in the realm of science fiction. However, the advance of technology has brought a new reality: Today, humans can use the electrical signals from brain activity to interact with, influence, or change their environments. The emerging field of brain-computer interface (BCI) technology may allow individuals unable to speak and/or use their limbs to once again communicate or operate assistive devices for walking and manipulating objects. Brain-computer interface research is an area of high public awareness. Videos on YouTube as well as news reports in the lay media indicate intense curiosity and interest in a field that hopefully one day soon will dramatically improve the lives of many disabled persons affected by a number of different disease processes.This review seeks to provide the general medical community with an introduction to BCIs. We define BCI and then review some of the seminal discoveries in this rapidly emerging field, the brain signals used by BCIs, the essential components of a BCI system, current BCI systems, and the key issues now engaging researchers. Challenges are inherent in translating any new technology to practical and useful clinical applications, and BCIs are no exception. We discuss the potent...

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“…144,145 Typically ECoG is used for mapping seizure foci and eloquent cortical areas in patients with intractable epilepsy prior to surgical resection of cortical tissue, although limited trials of ECoG BCI have been conducted in people with SCI and other motor impairments. [146][147][148] Some of these studies have used custom…”

Section: Ecog and Intracortical Microelectrode Bcismentioning

confidence: 99%

Neuroprosthetic technology for individuals with spinal cord injury

Collinger¹,

Foldes

Bruns

et al. 2013

The Journal of Spinal Cord Medicine

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Context: Spinal cord injury (SCI) results in a loss of function and sensation below the level of the lesion. Neuroprosthetic technology has been developed to help restore motor and autonomic functions as well as to provide sensory feedback. Findings: This paper provides an overview of neuroprosthetic technology that aims to address the priorities for functional restoration as defined by individuals with SCI. We describe neuroprostheses that are in various stages of preclinical development, clinical testing, and commercialization including functional electrical stimulators, epidural and intraspinal microstimulation, bladder neuroprosthesis, and cortical stimulation for restoring sensation. We also discuss neural recording technologies that may provide command or feedback signals for neuroprosthetic devices. Conclusion/clinical relevance: Neuroprostheses have begun to address the priorities of individuals with SCI, although there remains room for improvement. In addition to continued technological improvements, closing the loop between the technology and the user may help provide intuitive device control with high levels of performance.

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Real-time control of a prosthetic hand using human electrocorticography signals

Abstract: The present integrated BMI system successfully decoded the hand movements of a poststroke patient and controlled a prosthetic hand in real time. This success paves the way for the restoration of the patient's motor function using a prosthetic arm controlled by a BMI using ECoG signals.

Cited by 184 publications

References 31 publications

A Brief Review of Brain Signal Monitoring Technologies for BCI Applications: Challenges and Prospects

A Brief Review of Brain Signal Monitoring Technologies for BCI Applications: Challenges and Prospects

Brain-Computer Interfaces in Medicine

Neuroprosthetic technology for individuals with spinal cord injury

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