Training in Use of Brain–Machine Interface-Controlled Robotic Hand Improves Accuracy Decoding Two Types of Hand Movements

✓ Brain–machine interface (BMI) is the latest solution to a lack of control for paralyzed or prosthetic limbs. In this paper the authors focus on the design of anatomical robotic hands that use BMI as a critical intervention in restorative neurosurgery and they justify the requirement for lower-level neuromusculoskeletal details (relating to biomechanics, muscles, peripheral nerves, and some aspects of the spinal cord) in both mechanical and control systems. A person uses his or her hands for intimate contact and dexterous interactions with objects that require the user to control not only the finger endpoint locations but also the forces and the stiffness of the fingers. To recreate all of these human properties in a robotic hand, the most direct and perhaps the optimal approach is to duplicate the anatomical musculoskeletal structure. When a prosthetic hand is anatomically correct, the input to the device can come from the same neural signals that used to arrive at the muscles in the original hand. The more similar the mechanical structure of a prosthetic hand is to a human hand, the less learning time is required for the user to recreate dexterous behavior. In addition, removing some of the nonlinearity from the relationship between the cortical signals and the finger movements into the peripheral controls and hardware vastly simplifies the needed BMI algorithms. (Nonlinearity refers to a system of equations in which effects are not proportional to their causes. Such a system could be difficult or impossible to model.) Finally, if a prosthetic hand can be built so that it is anatomically correct, subcomponents could be integrated back into remaining portions of the user's hand at any transitional locations. In the near future, anatomically correct prosthetic hands could be used in restorative neurosurgery to satisfy the user's needs for both aesthetics and ease of control while also providing the highest possible degree of dexterity.

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Missing kinaesthesia challenges precise naturalistic cortical prosthetic control

10.1101/004861 ◽

2014 ◽

Author(s):

Ferran Galán ◽

Mark R Baker ◽

Kai Alter ◽

Stuart N Baker

Keyword(s):

Hand Movements ◽

Brain Machine Interface ◽

Neural Pathways ◽

Prosthetic Control ◽

Motor Commands ◽

Cortical Motor ◽

Machine Interface ◽

Ischemic Nerve Block ◽

Kinaesthetic Feedback ◽

Imagined Movement

A major assumption of brain-machine interface (BMI) research is that patients with disconnected neural pathways can still volitionally recall precise motor commands that could be decoded for naturalistic prosthetic control. However, the disconnected condition of these patients also blocks kinaesthetic feedback from the periphery, which has been shown to regulate centrally generated output responsible for accurate motor control. Here we tested how well motor commands are generated in the absence of kinaesthetic feedback by decoding hand movements from human scalp electroencephalography (EEG) in three conditions: unimpaired movement, imagined movement, and movement attempted during temporary disconnection of peripheral afferent and efferent nerves by ischemic nerve block. Our results suggest that the recall of cortical motor commands is impoverished in absence of kinaesthetic feedback, challenging the possibility of precise naturalistic cortical prosthetic control.

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S109 Magnetoencephalographic-based brain–machine interface robotic hand for controlling sensorimotor cortical plasticity and phantom limb pain

Clinical Neurophysiology ◽

10.1016/j.clinph.2017.07.120 ◽

2017 ◽

Vol 128 (9) ◽

pp. e214

Author(s):

Takufumi Yanagisawa ◽

Ryohei Fukuma ◽

Ben Seymour ◽

Kouichi Hosomi ◽

Haruhiko Kishima ◽

...

Keyword(s):

Cortical Plasticity ◽

Phantom Limb Pain ◽

Phantom Limb ◽

Brain Machine Interface ◽

Robotic Hand ◽

Limb Pain ◽

Machine Interface

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Evaluation of a P300-Based Brain-Machine Interface for a Robotic Hand-Orthosis Control

Frontiers in Neuroscience ◽

10.3389/fnins.2020.589659 ◽

2020 ◽

Vol 14 ◽

Cited By ~ 1

Author(s):

Jonathan Delijorge ◽

Omar Mendoza-Montoya ◽

Jose L. Gordillo ◽

Ricardo Caraza ◽

Hector R. Martinez ◽

...

Keyword(s):

Information Transfer ◽

Brain Machine Interface ◽

Robotic Hand ◽

Calibration Data ◽

Maximum Information ◽

Flexion Extension ◽

Machine Interface ◽

Robotic Devices ◽

Eeg Recordings ◽

Als Patients

This work presents the design, implementation, and evaluation of a P300-based brain-machine interface (BMI) developed to control a robotic hand-orthosis. The purpose of this system is to assist patients with amyotrophic lateral sclerosis (ALS) who cannot open and close their hands by themselves. The user of this interface can select one of six targets, which represent the flexion-extension of one finger independently or the movement of the five fingers simultaneously. We tested offline and online our BMI on eighteen healthy subjects (HS) and eight ALS patients. In the offline test, we used the calibration data of each participant recorded in the experimental sessions to estimate the accuracy of the BMI to classify correctly single epochs as target or non-target trials. On average, the system accuracy was 78.7% for target epochs and 85.7% for non-target trials. Additionally, we observed significant P300 responses in the calibration recordings of all the participants, including the ALS patients. For the BMI online test, each subject performed from 6 to 36 attempts of target selections using the interface. In this case, around 46% of the participants obtained 100% of accuracy, and the average online accuracy was 89.83%. The maximum information transfer rate (ITR) observed in the experiments was 52.83 bit/min, whereas that the average ITR was 18.13 bit/min. The contributions of this work are the following. First, we report the development and evaluation of a mind-controlled robotic hand-orthosis for patients with ALS. To our knowledge, this BMI is one of the first P300-based assistive robotic devices with multiple targets evaluated on people with ALS. Second, we provide a database with calibration data and online EEG recordings obtained in the evaluation of our BMI. This data is useful to develop and compare other BMI systems and test the processing pipelines of similar applications.

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Synchronization of Slow Cortical Rhythms During Motor Imagery-Based Brain–Machine Interface Control

International Journal of Neural Systems ◽

10.1142/s0129065718500454 ◽

2019 ◽

Vol 29 (05) ◽

pp. 1850045 ◽

Cited By ~ 1

Author(s):

Juan A. Barios ◽

Santiago Ezquerro ◽

Arturo Bertomeu-Motos ◽

Marius Nann ◽

Fco. Javier Badesa ◽

...

Keyword(s):

Motor Imagery ◽

Cognitive Processing ◽

Phase Synchronization ◽

Phase Locking ◽

Sensory Modality ◽

Brain Machine Interface ◽

Robotic Hand ◽

Imagery Task ◽

Cortical Rhythms ◽

Machine Interface

Modulation of sensorimotor rhythm (SMR) power, a rhythmic brain oscillation physiologically linked to motor imagery, is a popular Brain–Machine Interface (BMI) paradigm, but its interplay with slower cortical rhythms, also involved in movement preparation and cognitive processing, is not entirely understood. In this study, we evaluated the changes in phase and power of slow cortical activity in delta and theta bands, during a motor imagery task controlled by an SMR-based BMI system. In Experiment I, EEG of 20 right-handed healthy volunteers was recorded performing a motor-imagery task using an SMR-based BMI controlling a visual animation, and during task-free intervals. In Experiment II, 10 subjects were evaluated along five daily sessions, while BMI-controlling same visual animation, a buzzer, and a robotic hand exoskeleton. In both experiments, feedback received from the controlled device was proportional to SMR power (11–14[Formula: see text]Hz) detected by a real-time EEG-based system. Synchronization of slow EEG frequencies along the trials was evaluated using inter-trial-phase coherence (ITPC). Results: cortical oscillations of EEG in delta and theta frequencies synchronized at the onset and at the end of both active and task-free trials; ITPC was significantly modulated by feedback sensory modality received during the tasks; and ITPC synchronization progressively increased along the training. These findings suggest that phase-locking of slow rhythms and resetting by sensory afferences might be a functionally relevant mechanism in cortical control of motor function. We propose that analysis of phase synchronization of slow cortical rhythms might also improve identification of temporal edges in BMI tasks and might help to develop physiological markers for identification of context task switching and practice-related changes in brain function, with potentially important implications for design and monitoring of motor imagery-based BMI systems, an emerging tool in neurorehabilitation of stroke.

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