A BMI-based occupational therapy assist suit: asynchronous control by SSVEP

Sakurada, Takeshi; Kawase, Toshihiro; Takano, Kouji; Komatsu, Tomoaki; Kansaku, Kenji

doi:10.3389/fnins.2013.00172

Cited by 54 publications

(44 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…To the best of our knowledge, few research groups have attempted control of a prosthetic or a robotic arm using scalp EEG based BCIs. A variety of control signals, including sensorimotor rhythms18, steady state visual evoked potentials1920, hybrid systems21, real movement or attempted movement2223, have been used for these initial studies to control the robotic or prosthetic arm. Such previous efforts have primarily constrained the BCI control system to be discrete in one dimension or a plane without exploring the full possibility of controls in three-dimensional space.…”

mentioning

confidence: 99%

Noninvasive Electroencephalogram Based Control of a Robotic Arm for Reach and Grasp Tasks

Meng

Zhang

Bekyo

et al. 2016

Sci Rep

372

292

View full text Add to dashboard Cite

Brain-computer interface (BCI) technologies aim to provide a bridge between the human brain and external devices. Prior research using non-invasive BCI to control virtual objects, such as computer cursors and virtual helicopters, and real-world objects, such as wheelchairs and quadcopters, has demonstrated the promise of BCI technologies. However, controlling a robotic arm to complete reach-and-grasp tasks efficiently using non-invasive BCI has yet to be shown. In this study, we found that a group of 13 human subjects could willingly modulate brain activity to control a robotic arm with high accuracy for performing tasks requiring multiple degrees of freedom by combination of two sequential low dimensional controls. Subjects were able to effectively control reaching of the robotic arm through modulation of their brain rhythms within the span of only a few training sessions and maintained the ability to control the robotic arm over multiple months. Our results demonstrate the viability of human operation of prosthetic limbs using non-invasive BCI technology.

show abstract

mentioning

confidence: 99%

Noninvasive Electroencephalogram Based Control of a Robotic Arm for Reach and Grasp Tasks

Meng

Zhang

Bekyo

et al. 2016

Sci Rep

372

292

View full text Add to dashboard Cite

show abstract

“…In non-invasive BMIs, six types of brain signals have been tested: 1. sensori-motor rhythms (SMR,(8)(9)(10)(11)(12)(13)(14)(15) Hz, also termed rolandic alpha or mu-rhythm depending on the context) (McFarland et al, 1993(McFarland et al, , 2006Pfurtscheller et al, 2006;Soekadar et al, 2011a, in press-a), 2. slow cortical potentials (SCP) (Birbaumer et al, 1999), 3. event-related potentials (ERPs) (Farwell and Donchin, 1988) and 4. steady-state visually or auditory evoked potentials (SSVEP/SSAEP) (Sakurada et al, 2013), 5. blood-oxygenation level dependent (BOLD)-contrast imaging using functional MRI (Weiskopf et al, 2003), and 6. concentration changes of oxy/deoxy hemoglobin using functional near-infrared spectroscopy (fNIRS; Sitaram et al, 2009;Mihara et al, 2013;Rea et al, 2014). Implantable BMIs, in contrast, require surgical implantation of epidural, subdural, or intracortical electrode arrays.…”

Section: Brain-machine Interfaces (Bmis)mentioning

confidence: 99%

Brain–machine interfaces in neurorehabilitation of stroke

Soekadar

Birbaumer

Slutzky

et al. 2015

Neurobiology of Disease

262

180

View full text Add to dashboard Cite

Stroke is among the leading causes of long-term disabilities leaving an increasing number of people with cognitive, affective and motor impairments depending on assistance in their daily life. While function after stroke can significantly improve in the first weeks and months, further recovery is often slow or non-existent in the more severe cases encompassing 30-50% of all stroke victims. The neurobiological mechanisms underlying recovery in those patients are incompletely understood. However, recent studies demonstrated the brain's remarkable capacity for functional and structural plasticity and recovery even in severe chronic stroke. As all established rehabilitation strategies require some remaining motor function, there is currently no standardized and accepted treatment for patients with complete chronic muscle paralysis. The development of brain-machine interfaces (BMIs) that translate brain activity into control signals of computers or external devices provides two new strategies to overcome stroke-related motor paralysis. First, BMIs can establish continuous high-dimensional brain-control of robotic devices or functional electric stimulation (FES) to assist in daily life activities (assistive BMI). Second, BMIs could facilitate neuroplasticity, thus enhancing motor learning and motor recovery (rehabilitative BMI). Advances in sensor technology, development of non-invasive and implantable wireless BMI-systems and their combination with brain stimulation, along with evidence for BMI systems' clinical efficacy suggest that BMI-related strategies will play an increasing role in neurorehabilitation of stroke.

show abstract

“…In non-invasive BMIs, six types of brain signals have been tested: sensori-motor rhythms (SMRs, 8–15 Hz, i.e., rolandic alpha or mu rhythm) (Pfurtscheller et al, 1992, 2006; Wolpaw and McFarland, 1994; McFarland et al, 2006), event-related potentials (ERPs) (Farwell and Donchin, 1988), SCPs (Birbaumer et al, 1999), steady-state visually or auditory evoked potentials (SSVEPs/SSAEPs) (Sakurada et al, 2013), concentration changes of oxy/deoxy hemoglobin using functional near-infrared spectroscopy (fNIRS) (Sitaram et al, 2009; Mihara et al, 2013; Rea et al, 2014), and blood-oxygenation level dependent (BOLD)-contrast imaging using functional MRI (Weiskopf et al, 2003). Implantable BMIs utilize epidural, subdural, or intracortical electrodes.…”

Section: The State Of the Art In Neural Prostheses And Brain-machine mentioning

confidence: 99%

Enhancing Nervous System Recovery through Neurobiologics, Neural Interface Training, and Neurorehabilitation

et al. 2016

View full text Add to dashboard Cite

After an initial period of recovery, human neurological injury has long been thought to be static. In order to improve quality of life for those suffering from stroke, spinal cord injury, or traumatic brain injury, researchers have been working to restore the nervous system and reduce neurological deficits through a number of mechanisms. For example, neurobiologists have been identifying and manipulating components of the intra- and extracellular milieu to alter the regenerative potential of neurons, neuro-engineers have been producing brain-machine and neural interfaces that circumvent lesions to restore functionality, and neurorehabilitation experts have been developing new ways to revitalize the nervous system even in chronic disease. While each of these areas holds promise, their individual paths to clinical relevance remain difficult. Nonetheless, these methods are now able to synergistically enhance recovery of native motor function to levels which were previously believed to be impossible. Furthermore, such recovery can even persist after training, and for the first time there is evidence of functional axonal regrowth and rewiring in the central nervous system of animal models. To attain this type of regeneration, rehabilitation paradigms that pair cortically-based intent with activation of affected circuits and positive neurofeedback appear to be required—a phenomenon which raises new and far reaching questions about the underlying relationship between conscious action and neural repair. For this reason, we argue that multi-modal therapy will be necessary to facilitate a truly robust recovery, and that the success of investigational microscopic techniques may depend on their integration into macroscopic frameworks that include task-based neurorehabilitation. We further identify critical components of future neural repair strategies and explore the most updated knowledge, progress, and challenges in the fields of cellular neuronal repair, neural interfacing, and neurorehabilitation, all with the goal of better understanding neurological injury and how to improve recovery.

show abstract

A BMI-based occupational therapy assist suit: asynchronous control by SSVEP

Cited by 54 publications

References 58 publications

Noninvasive Electroencephalogram Based Control of a Robotic Arm for Reach and Grasp Tasks

Noninvasive Electroencephalogram Based Control of a Robotic Arm for Reach and Grasp Tasks

Brain–machine interfaces in neurorehabilitation of stroke

Enhancing Nervous System Recovery through Neurobiologics, Neural Interface Training, and Neurorehabilitation

Contact Info

Product

Resources

About