On the asynchronously continuous control of mobile robot movement by motor cortical spiking activity

Xu, Zhiming; So, Rosa; Toe, Kyaw Kyar; Ang, Kai Keng; Guan, Cuntai

doi:10.1109/embc.2014.6944266

Cited by 4 publications

(5 citation statements)

References 23 publications

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“…The monkeys were not seated in the wheelchair in this study. More recently, Xu et al trained one monkey to steer while seated in a wheelchair using a hand-held joystick to generate discrete but not continuous navigation commands 11 . These authors also demonstrated a BMI version of this joystick control, where hand movements were decoded from M1 ensemble activity to produce steering commands 11 .…”

Section: Discussionmentioning

confidence: 99%

“…More recently, Xu et al trained one monkey to steer while seated in a wheelchair using a hand-held joystick to generate discrete but not continuous navigation commands 11 . These authors also demonstrated a BMI version of this joystick control, where hand movements were decoded from M1 ensemble activity to produce steering commands 11 . This group, however, did not attempt to translate cortical activity directly into whole-body navigation, without using hand movements as an intermediary.…”

Section: Discussionmentioning

confidence: 99%

“…Up to date, noninvasive BMI approaches, mostly based on electroencephalography (EEG), have been dominant in assistive devices that enable direct brain control over navigation in a powered wheelchair 2 3 4 5 . Although intracranial BMIs hold promise to offer a superior performance over noninvasive systems 6 7 and approximately 70% of paralyzed patients are willing to accept surgically implanted electrodes in their brains to gain control over their assistive devices 8 9 , only a few studies have previously attempted to apply intracranial BMIs to wheelchair control 10 11 .…”

mentioning

confidence: 99%

“…One design for an intracranial BMI that controls wheelchair movements is based on joystick movement. This design was previously introduced by our laboratory 10 and by Xu et al 11 . In our experiments, rhesus monkeys were housed in a home cage equipped with a joystick.…”

mentioning

confidence: 99%

“…In some BMI experiments the monkeys continued to assist movement with the joystick, in the others the joystick was removed and the monkeys controlled the wheelchair without producing overt arm movements. Recently, Xu et al described how one monkey seated in a wheelchair 11 was trained to move a joystick in one of four directions to set the direction of wheelchair movement. Under BMI control, this monkey continued to move the joystick, but the control signal was derived from a discrete classifier that extracted joystick position from cortical activity.…”

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confidence: 99%

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Wireless Cortical Brain-Machine Interface for Whole-Body Navigation in Primates

Rajangam

Tseng

Yin

et al. 2016

Sci Rep

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Several groups have developed brain-machine-interfaces (BMIs) that allow primates to use cortical activity to control artificial limbs. Yet, it remains unknown whether cortical ensembles could represent the kinematics of whole-body navigation and be used to operate a BMI that moves a wheelchair continuously in space. Here we show that rhesus monkeys can learn to navigate a robotic wheelchair, using their cortical activity as the main control signal. Two monkeys were chronically implanted with multichannel microelectrode arrays that allowed wireless recordings from ensembles of premotor and sensorimotor cortical neurons. Initially, while monkeys remained seated in the robotic wheelchair, passive navigation was employed to train a linear decoder to extract 2D wheelchair kinematics from cortical activity. Next, monkeys employed the wireless BMI to translate their cortical activity into the robotic wheelchair’s translational and rotational velocities. Over time, monkeys improved their ability to navigate the wheelchair toward the location of a grape reward. The navigation was enacted by populations of cortical neurons tuned to whole-body displacement. During practice with the apparatus, we also noticed the presence of a cortical representation of the distance to reward location. These results demonstrate that intracranial BMIs could restore whole-body mobility to severely paralyzed patients in the future.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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confidence: 99%

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Wireless Cortical Brain-Machine Interface for Whole-Body Navigation in Primates

Rajangam

Tseng

Yin

et al. 2016

Sci Rep

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Place Cell-Like Activity in the Primary Sensorimotor and Premotor Cortex During Monkey Whole-Body Navigation

Yin

Tseng

Rajangam

et al. 2018

Sci Rep

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Primary motor (M1), primary somatosensory (S1) and dorsal premotor (PMd) cortical areas of rhesus monkeys previously have been associated only with sensorimotor control of limb movements. Here we show that a significant number of neurons in these areas also represent body position and orientation in space. Two rhesus monkeys (K and M) used a wheelchair controlled by a brain-machine interface (BMI) to navigate in a room. During this whole-body navigation, the discharge rates of M1, S1, and PMd neurons correlated with the two-dimensional (2D) room position and the direction of the wheelchair and the monkey head. This place cell-like activity was observed in both monkeys, with 44.6% and 33.3% of neurons encoding room position in monkeys K and M, respectively, and the overlapping populations of 41.0% and 16.0% neurons encoding head direction. These observations suggest that primary sensorimotor and premotor cortical areas in primates are likely involved in allocentrically representing body position in space during whole-body navigation, which is an unexpected finding given the classical hierarchical model of cortical processing that attributes functional specialization for spatial processing to the hippocampal formation.

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Stop state classification in intracortical brain-machine-interface

Koh

Libedinsky

Guan

et al. 2017

2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Invasive brain-machine-interface (BMI) has the prospect to empower tetraplegic patients with independent mobility through the use of brain-controlled wheelchairs. For the practical and long-term use of such control systems, the system has to distinguish between stop and movement states and has to be robust to overcome non-stationarity in the brain signals. In this work, we investigates the non-stationarity of the stop state on neural data collected from a macaque trained to control a robotic platform to stop and move in left, right, forward directions We then propose a hybrid approach that employs both random forest and linear discriminant analysis (LDA). Using this approach, we performed offline decoding on 8 days of data collected over the course of three months during joystick control of the robotic platform. We compared the results of using the proposed approach with the use of LDA alone to perform direct classifications of stop, left, right and forward. The results showed an average performance increment of 22.7% using the proposed hybrid approach. The results yielded significant improvements during sessions where LDA showed a heavy bias towards the stop state. This suggests that the proposed hybrid approach addresses the non-stationarity in the stop state and subsequently facilitates a more accurate decoding of the movement states.

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On the asynchronously continuous control of mobile robot movement by motor cortical spiking activity

Cited by 4 publications

References 23 publications

Wireless Cortical Brain-Machine Interface for Whole-Body Navigation in Primates

Wireless Cortical Brain-Machine Interface for Whole-Body Navigation in Primates

Place Cell-Like Activity in the Primary Sensorimotor and Premotor Cortex During Monkey Whole-Body Navigation

Stop state classification in intracortical brain-machine-interface

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