Brain-controlled exoskeleton robot for BMI rehabilitation

Noda, Tomoyuki; Sugimoto, Norikazu; Furukawa, Jun-ichiro; Sato, Masaaki; Hyon, Sang-Ho; Morimoto, Jun

doi:10.1109/humanoids.2012.6651494

Cited by 54 publications

(45 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We developed DecNef by spatial Figure 14. EEG-exoskeleton experiment [44]. Subject controlled EEG-exoskeleton system to follow the stand-up/sit-down directions indicated on the display using motor imagery.…”

Section: Discussion: Integrating the Three Fields By Decoded Neurofeementioning

confidence: 99%

“…We updated the covariance matrix of the processed EEG signal with a fixed period. According to the classifier's output, the stand-up or sit-down movements of the exoskeleton robot were generated by the torque control method for the exoskeleton robot [44]. Figure 14 shows the results of the EEG-exoskeleton control.…”

Section: Electroencephalogram-exoskeleton Systemmentioning

confidence: 99%

“…For BMI rehabilitation, we have developed an EEGexoskeleton robot system, where an exoskeleton robot is connected to an EEG measurement device that users can control with their brain activities [44]. To activate an exoskeleton robot based on the user's brain activity, we simultaneously need to consider the influence of the exoskeleton robot movement to the brain activity related to somatosensory inputs as there is physical interaction between the robot and the user.…”

Section: Interacting With Brain Through Brainmachine Interface Exoskementioning

confidence: 99%

“…For the brainmotivated humanoid-motor-learning studies, we explored the creating the brain approach by developing a series of humanoid robots [36,41], including CB-i, a human-sized humanoid robot (figure 2a) [42]. As a new generation of humanoids, we are developing exoskeleton robots that can physically interact with the human body to assist user movements (figure 2b) [43,44]. An important technical element of DecNef is the real-time neurofeedback of decoded information to the brain.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Creating the brain and interacting with the brain: an integrated approach to understanding the brain

Morimoto¹,

Kawato

2015

J. R. Soc. Interface.

Self Cite

View full text Add to dashboard Cite

In the past two decades, brain science and robotics have made gigantic advances in their own fields, and their interactions have generated several interdisciplinary research fields. First, in the 'understanding the brain by creating the brain' approach, computational neuroscience models have been applied to many robotics problems. Second, such brain-motivated fields as cognitive robotics and developmental robotics have emerged as interdisciplinary areas among robotics, neuroscience and cognitive science with special emphasis on humanoid robots. Third, in brain-machine interface research, a brain and a robot are mutually connected within a closed loop. In this paper, we review the theoretical backgrounds of these three interdisciplinary fields and their recent progress. Then, we introduce recent efforts to reintegrate these research fields into a coherent perspective and propose a new direction that integrates brain science and robotics where the decoding of information from the brain, robot control based on the decoded information and multimodal feedback to the brain from the robot are carried out in real time and in a closed loop.

show abstract

Section: Discussion: Integrating the Three Fields By Decoded Neurofeementioning

confidence: 99%

Section: Electroencephalogram-exoskeleton Systemmentioning

confidence: 99%

Section: Interacting With Brain Through Brainmachine Interface Exoskementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Creating the brain and interacting with the brain: an integrated approach to understanding the brain

Morimoto¹,

Kawato

2015

J. R. Soc. Interface.

Self Cite

View full text Add to dashboard Cite

show abstract

“…One of the widely used approaches for monitoring the human intention relies on the use of brain machine interfaces such as in XoR [5]. These systems are efficient in monitoring the user intention, because a real displacement of the joint position is not needed to initiate the gait.…”

Section: Introductionmentioning

confidence: 99%

User Intention Driven Adaptive Gait Assistance Using a Wearable Exoskeleton

Rajasekaran

Aranda

Casals

2015

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

Abstract-A user intention based rehabilitation strategy for a lower-limb wearable robot is proposed and evaluated. The control strategy, which involves monitoring the human-orthosis interaction torques, determines the gait initiation instant and modifies orthosis operation for gait assistance, when needed. Orthosis operation is classified as assistive or resistive in function of its evolution with respect to a normal gait pattern. The control algorithm relies on the adaptation of the joints' stiffness in function of their interaction torques and their deviation from the desired trajectories. An average of recorded gaits obtained from healthy subjects is used as reference input. The objective of this work is to develop a control strategy that can trigger the gait initiation from the user's intention and maintain the dynamic stability, using an efficient real-time stiffness adaptation for multiple joints, simultaneously maintaining their synchronization. The algorithm has been tested with five healthy subjects showing its efficient behavior in initiating the gait and maintaining the equilibrium while walking in presence of external forces. The work is performed as a preliminary study to assist patients suffering from incomplete Spinal cord injury and Stroke.

show abstract