Adaptive Shared Control of a Brain-Actuated Simulated Wheelchair

Philips, Johan; Millán, José del R.; Vanacker, G.; Lew, Eileen; Galán, Ferran; Ferrez, Pierre W.; Brussel, H. Van; Nuttin, Marnix

doi:10.1109/icorr.2007.4428457

Cited by 138 publications

(77 citation statements)

References 8 publications

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“…In this paper we have described one of the first implementations of a shared-control BCI telepresence robot that is related to our previous work with wheelchairs [11], [12], [13], [14]. In the current paper, however, shared control only deals with lowlevel obstacle avoidance and it relies on very simple infrared sensors (as opposed to expensive laser range finders on the wheelchairs).…”

Section: Resultsmentioning

confidence: 99%

“…Thus, for them, a semi-autonomous approach is more suitable for a BCIcontrolled telepresence robot, where the intelligent system helps the human user to cope with problematic situations such as obstacle detection and avoidance [9]. Such a semiautonomous framework has been largely explored for assistive wheelchairs [4], [10], including brain-controlled wheelchairs [11], [12], [13], [14].…”

Section: Introductionmentioning

confidence: 99%

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The role of shared-control in BCI-based telepresence

Tonin

Leeb

Tavella

et al. 2010

2010 IEEE International Conference on Systems, Man and Cybernetics

100

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Abstract-This paper discusses and evaluates the role of shared control approach in a BCI-based telepresence framework. Driving a mobile device by using human brain signals might improve the quality of life of people suffering from severely physical disabilities. By means of a bidirectional audio/video connection to a robot, the BCI user is able to interact actively with relatives and friends located in different rooms. However, the control of robots through an uncertain channel as a BCI may be complicated and exhaustive. Shared control can facilitate the operation of brain-controlled telepresence robots, as demonstrated by the experimental results reported here. In fact, it allows all subjects to complete a rather complex task, driving the robot in a natural environment along a path with several targets and obstacles, in shorter times and with less number of mental commands.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The role of shared-control in BCI-based telepresence

Tonin

Leeb

Tavella

et al. 2010

2010 IEEE International Conference on Systems, Man and Cybernetics

100

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“…3 depicts a schematic representation of the shared control architecture of the brain-actuated wheelchair. See Philips et al (2007) and Vanacker et al (2007) for a detailed description. As for the BCI, it has two components: a feature extractor and a Gaussian classifier.…”

Section: System Descriptionmentioning

confidence: 99%

A brain-actuated wheelchair: Asynchronous and non-invasive Brain–computer interfaces for continuous control of robots

Galán¹,

Nuttin²,

Lew³

et al. 2008

Clinical Neurophysiology

592

331

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Objective: To assess the feasibility and robustness of an asynchronous and non-invasive EEG-based Brain-Computer Interface (BCI) for continuous mental control of a wheelchair. Methods: In experiment 1 two subjects were asked to mentally drive both a real and a simulated wheelchair from a starting point to a goal along a pre-specified path. Here we only report experiments with the simulated wheelchair for which we have extensive data in a complex environment that allows a sound analysis. Each subject participated in five experimental sessions, each consisting of 10 trials. The time elapsed between two consecutive experimental sessions was variable (from 1 h to 2 months) to assess the system robustness over time. The pre-specified path was divided into seven stretches to assess the system robustness in different contexts. To further assess the performance of the brain-actuated wheelchair, subject 1 participated in a second experiment consisting of 10 trials where he was asked to drive the simulated wheelchair following 10 different complex and random paths never tried before. Results: In experiment 1 the two subjects were able to reach 100% (subject 1) and 80% (subject 2) of the final goals along the pre-specified trajectory in their best sessions. Different performances were obtained over time and path stretches, what indicates that performance is time and context dependent. In experiment 2, subject 1 was able to reach the final goal in 80% of the trials. Conclusions: The results show that subjects can rapidly master our asynchronous EEG-based BCI to control a wheelchair. Also, they can autonomously operate the BCI over long periods of time without the need for adaptive algorithms externally tuned by a human operator to minimize the impact of EEG non-stationarities. This is possible because of two key components: first, the inclusion of a shared control system between the BCI system and the intelligent simulated wheelchair; second, the selection of stable user-specific EEG features that maximize the separability between the mental tasks. Significance: These results show the feasibility of continuously controlling complex robotics devices using an asynchronous and noninvasive BCI.

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“…However, the subject might lose the feeling of continuous control with such a controller. The loss of independence is undesirable and therefore, shared control between the user and the controller is more suitable [35]. IBCI's design for such a system has three basic elements [35] [41], 1) adaptive shared controller that fuses the human and wheelchair decisions in a Bayesian way for better steering commands , 2) context information from the model of environment for filtering out unlikely decisions taken by the classifier and 3) assistive behaviors (collision avoidance (A0) obstacle avoidance(A1), and orientation recovery (A2)) based on the model of environment (e.g., openings in a corridor).…”

Section: Brain Actuated Wheelchairmentioning

confidence: 99%

Constructing Ambient Intelligence

Ferscha¹,

Aitenbichler²,

Mühlhäuser³

2008

Communications in Computer and Information Science

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Abstract. This paper is aimed to introduce IDIAP Brain Computer Interface (IBCI) research that successfully applied Ambience Intelligence (AmI) principles in designing intelligent brain-machine interactions. We proceed through IBCI applications describing how machines can decode and react to the human mental commands, cognitive and emotive states. We show how effective human-machine interaction for brain computer interfacing (BCI) can be achieved through, 1) asynchronous and spontaneous BCI, 2) shared control between the human and machine, 3) online learning and 4) the use of cognitive state recognition. Identifying common principles in BCI research and ambiance intelligence (AmI) research, we discuss IBCI applications. With the current studies on recognition of human cognitive states, we argue for the possibility of designing empathic environments or devices that have a better human like understanding directly from brain signals. MotivationBrain Computer Interfacing (BCI) or Brain Machine Interfacing (BMI) refers to interaction with devices, where user's intentions represented as several brain states are deciphered and translated into actions without requiring any physical action [44] [25] [21]. There is a growing interest in the use of brain signals for communicating and operating devices, which is facilitated by the advances in the the measurement technologies in the past decades. As BCI bypasses the classical neuromuscular communication channels, this technology is intended to use for rehabilitation of tetraplegic or paraplegic patients to improve their communication, mobility and independence. The BCI research also opens up new possibilities in natural interaction for able-bodied people (e.g., for space applications, where environment is inherently hostile and dangerous for astronauts, who could greatly benefit from direct mental teleoperation of external semi-automatic manipulators [26], and for entertainment applications like multimedia gaming [20]). Typical applications of BCI are communication aids such as spelling devices [5] [31] [25] and mobility aids such as wheelchair [41]. In the current paper, we Gangadhar.Garipelli@idiap.ch

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Adaptive Shared Control of a Brain-Actuated Simulated Wheelchair

Cited by 138 publications

References 8 publications

The role of shared-control in BCI-based telepresence

The role of shared-control in BCI-based telepresence

A brain-actuated wheelchair: Asynchronous and non-invasive Brain–computer interfaces for continuous control of robots

Constructing Ambient Intelligence

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