Learning to move machines with the mind

Green, Andrea M.; Kalaska, John

doi:10.1016/j.tins.2010.11.003

Cited by 126 publications

(94 citation statements)

References 125 publications

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“…Indeed, the hybrid signal produced is a measure of neurovascular coupling and could have enhanced value when measured for subjects who have suffered stroke or similar cerebro-vascular damage. Fusion approaches such as the above are part of the next phase of BCI development [4,17,20] which will require enhanced, robust performance outside the laboratory setting.…”

Section: Resultsmentioning

confidence: 99%

“…In ERS/ERD analysis, a baseline "reference" period of EEG data is recorded before the event and then compared to an "activity" period of EEG data, recorded during or following the event. ERD is known to occur in the frequency range (8-12 Hz) on movement onset and ERS is known to occur in theˇfrequency range (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) following movement offset.…”

Section: Combined Eeg-fnirs Measurements In Overt and Imagined Movemementioning

confidence: 99%

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Hybrid Optical–Electrical Brain Computer Interfaces, Practices and Possibilities

Ward

2012

Towards Practical Brain-Computer Interfaces

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In this chapter we present an overview of the area of electroencephalographyfunctional near infrared spectroscopy (EEG-fNIRS) measurement as an activity monitoring technology for brain computer interfacing applications. Our interest in this compound neural interfacing technology is motivated by a need for a motor cortical conditioning technology suitable for use in a neurorehabilitation setting [15,50]. Specifically we seek BCI technology that allows a patient with a paretic limb (as a consequence of stroke) to engage in movement-based rehabilitation exercises which will, we hope, encourage neuroplastic processes in the brain so that recovery and function is ultimately restored [38]. As we are interested in rehabilitation following stroke haemodynamic signatures of motor cortical activity coupled with the corresponding direct measures of the electrical activity of the neurons involved could be a rich source of new information on the recovering brain areas. While most neural engineers will be familiar with the concepts underpinning the electroencephalogram (EEG), the same cannot be said for fNIRS. Consequently this chapter will discuss much of the foundational concepts underlying this measurement before describing an EEG-fNIRS probe and early experiments which illustrate the concept and highlight aspects of the utility of this hybrid BCI approach. The Underlying Physiological Origins of EEG and fNIRSIt is appropriate at this juncture to consider the physical basis of the measurements generated during both electroencephalography and fNIRS. While both measurement

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Section: Resultsmentioning

confidence: 99%

Section: Combined Eeg-fnirs Measurements In Overt and Imagined Movemementioning

confidence: 99%

Hybrid Optical–Electrical Brain Computer Interfaces, Practices and Possibilities

Ward

2012

Towards Practical Brain-Computer Interfaces

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“…In the first model, the monkey's contralateral arm is restrained and monitored for task-modulated muscle activity 1 . The absence of this muscle activity does not require enforcing electromyography (EMG) silence, but rather that EMG activity not change within or across behavioral trials.…”

Section: A Arm Restrained No Emg Modulationmentioning

confidence: 99%

“…This model has been employed by Schwartz and colleagues, where it was used in a control experiment to see if neural cursor control could be maintained without arm movements or EMG modulation [11]. 1 The contralateral arm with respect to the electrode-array implant. The ipsilateral arm is also often restrained since motor cortical activity is also related to ipsilateral arm movements (e.g., [10]) However the ipsilateral arm's EMG activity is seldom if ever measured.…”

Section: A Arm Restrained No Emg Modulationmentioning

confidence: 99%

“…BMIs are a new class of electronic medical systems that aim to improve the quality of life for disabled patients. These systems interface with the central nervous system and use neural signals from the brain to control prosthetic devices (e.g., [1]). In recent years, first-generation BMIs developed with rhesus monkey animal models have translated from the laboratory into an FDA Phase-I clinical trial focused on tetraplegics and ALS patients (BrainGate I & II, [2][3][4][5][6][7]).…”

Section: Introductionmentioning

confidence: 99%

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Monkey models for brain-machine interfaces: The need for maintaining diversity

Nuyujukian

Fan

Gilja

et al. 2011

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-Brain-machine interfaces (BMIs) aim to help disabled patients by translating neural signals from the brain into control signals for guiding prosthetic arms, computer cursors, and other assistive devices. Animal models are central to the development of these systems and have helped enable the successful translation of the first generation of BMIs. As we move toward next-generation systems, we face the question of which animal models will aid broader patient populations and achieve even higher performance, robustness, and functionality. We review here four general types of rhesus monkey models employed in BMI research, and describe two additional, complementary models. Given the physiological diversity of neurological injury and disease, we suggest a need to maintain the current diversity of animal models and to explore additional alternatives, as each mimic different aspects of injury or disease.

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Motor Output from the Brain and Spinal Cord

Berkowitz

2012

Encyclopedia of Life Sciences

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All skeletal muscles are controlled by activation and inhibition of motor neurons in the spinal cord and brainstem. Motor neurons integrate information from a wide variety of sources in the brain and spinal cord. Simple limb reflexes and basic patterns of limb movements, including locomotion, are coordinated by spinal cord interneurons and sensory neurons. Descending inputs from the reticular formation, the vestibular nuclei and the superior colliculus in the brainstem are important in the control of automatic, axial, postural and locomotor movements. Descending inputs from several areas of the cerebral cortex and from the red nucleus are important in the control of voluntary, distal limb and fine movements. The basal ganglia in the basal forebrain and the cerebellum in the hindbrain are involved in the modification and learning of movements. These structures act indirectly on motor output, via outputs through the thalamus to widespread areas of the cerebral cortex. Key Concepts: All voluntary movement involves motor neurons in the spinal cord and brainstem that cause contraction of skeletal muscles. Neurons in many areas of the brain and spinal cord are active at overlapping times to control voluntary movements. The spinal cord can produce coordinated movements of the limbs, such as locomotion, as well as simple reflexes. The superior colliculus can trigger orienting movements, especially of the eyes and head. Voluntary limb movements involve neurons in several parts of the cerebral cortex, including especially the frontal lobe, which project axons to the spinal cord. Cortical movement control signals are modified by loops to and from the basal ganglia and the cerebellum, going through the thalamus. The basal ganglia modify the speed and amplitude of movements and contribute to learning new movement sequences. The cerebellum adapts movements to altered sensory feedback and may also contribute to a wide variety of other functions, including memory, language and emotion. Patients with movement disorders may in the future be able to control a limb or prosthetic device with ‘their thoughts’, via a brain–machine interface that monitors the activity of some of their cortical neurons.

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Learning to move machines with the mind

Cited by 126 publications

References 125 publications

Hybrid Optical–Electrical Brain Computer Interfaces, Practices and Possibilities

Hybrid Optical–Electrical Brain Computer Interfaces, Practices and Possibilities

Monkey models for brain-machine interfaces: The need for maintaining diversity

Motor Output from the Brain and Spinal Cord

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