An adaptive brain-computer interface for humanoid robot control

Bryan, Matthew; Green, Joshua; Chung, Moon Kee; Chang, Lillian Y.; Scherer, Reinhold; Smith, Joshua R.; Rao, Rajesh P. N.

doi:10.1109/humanoids.2011.6100901

Cited by 57 publications

(34 citation statements)

References 12 publications

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“…An early system was presented in [5] using a toy-like humanoid robot. Recent systems include a BMI for the PR2 robot, where the user teaches the robot to execute trajectories [6], one for the HRP-2 robot in a navigation task [7] and one where a NAO robot is navigated through a simple maze [8]. These systems rely on different EEG components: the P300 potential, steady-state visually evoked potentials and motor imagery (MI).…”

Section: A Related Workmentioning

confidence: 99%

Towards multi-user brain-robot interfaces for humanoid robot control

Finke

Rudgalwis

Jakusch

et al. 2012

2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012)

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Brain-controlled robots as a "surrogate presence" for humans may appear only a vision to date. However, the field of brain-robot interfacing is making rapid progress. Humanoid robots are ideal candidates for providing such a "surrogate presence", because they have the same embodiment as humans. Telepresence scenarios, such as "virtual" meetings, imply that not only one robot controlled by one human user is present, but that several users interact with each other mediated by their robots. Inspired by this scenario, we present a multi-user brain-robot interface, where currently two users control one humanoid robot each and interact with each other in a shared space. Brain-control is based on an asynchronous, dynamic and hybrid EEG-based brain-robot interface. We investigated two types of interaction: collaboration and competition. System performance was evaluated in a user study with 12 participants. Our results show that all users are capable of controlling the robots in the complex tasks. The complexity, however, imposes a high cognitive load that hampers focusing on the interaction with the other user.

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Section: A Related Workmentioning

confidence: 99%

Towards multi-user brain-robot interfaces for humanoid robot control

Finke

Rudgalwis

Jakusch

et al. 2012

2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012)

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“…Rather than enumerating the transitions, we refer the reader to figure 3, which depicts the transition model graphically. In future work, we intend to utilize more sophisticated transition models for hierarchical task modelling (see, e.g., [29,31,32]). …”

Section: Pomdp Specificationmentioning

confidence: 99%

Probabilistic co-adaptive brain–computer interfacing

et al. 2013

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Objective. Brain-computer interfaces (BCIs) are confronted with two fundamental challenges: (a) the uncertainty associated with decoding noisy brain signals, and (b) the need for co-adaptation between the brain and the interface so as to cooperatively achieve a common goal in a task. We seek to mitigate these challenges. Approach. We introduce a new approach to brain-computer interfacing based on partially observable Markov decision processes (POMDPs). POMDPs provide a principled approach to handling uncertainty and achieving co-adaptation in the following manner: (1) Bayesian inference is used to compute posterior probability distributions ('beliefs') over brain and environment state, and (2) actions are selected based on entire belief distributions in order to maximize total expected reward; by employing methods from reinforcement learning, the POMDP's reward function can be updated over time to allow for co-adaptive behaviour. Main results. We illustrate our approach using a simple non-invasive BCI which optimizes the speed-accuracy trade-off for individual subjects based on the signal-to-noise characteristics of their brain signals. We additionally demonstrate that the POMDP BCI can automatically detect changes in the user's control strategy and can co-adaptively switch control strategies on-the-fly to maximize expected reward. Significance. Our results suggest that the framework of POMDPs offers a promising approach for designing BCIs that can handle uncertainty in neural signals and co-adapt with the user on an ongoing basis. The fact that the POMDP BCI maintains a probability distribution over the user's brain state allows a much more powerful form of decision making than traditional BCI approaches, which have typically been based on the output of classifiers or regression techniques. Furthermore, the co-adaptation of the system allows the BCI to make online improvements to its behaviour, adjusting itself automatically to the user's changing circumstances.

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“…An example is [44], where subjects started performing low-level actions through SSVEP. Once a series of commands had been validated, it was included as a high-level action.…”

Section: Robotsmentioning

confidence: 99%

BCI-Based Navigation in Virtual and Real Environments

Velasco-Álvarez

Ron-Angevín

López-Gordo

2013

Lecture Notes in Computer Science

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Abstract. A Brain-Computer Interface (BCI) is a system that enables people to control an external device with their brain activity, without the need of any muscular activity. Researchers in the BCI field aim to develop applications to improve the quality of life of severely disabled patients, for whom a BCI can be a useful channel for interaction with their environment. Some of these systems are intended to control a mobile device (e. g. a wheelchair). Virtual Reality is a powerful tool that can provide the subjects with an opportunity to train and to test different applications in a safe environment. This technical review will focus on systems aimed at navigation, both in virtual and real environments.

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An adaptive brain-computer interface for humanoid robot control

Cited by 57 publications

References 12 publications

Towards multi-user brain-robot interfaces for humanoid robot control

Towards multi-user brain-robot interfaces for humanoid robot control

Probabilistic co-adaptive brain–computer interfacing

BCI-Based Navigation in Virtual and Real Environments

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