Brain-coupled interaction for semi-autonomous navigation of an assistive robot

Perrin, Xavier; Chavarriaga, Ricardo; Colas, Francis; Siegwart, Roland; Millán, José del R.

doi:10.1016/j.robot.2010.05.010

Cited by 98 publications

(47 citation statements)

References 12 publications

(27 reference statements)

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“…Nevertheless, we have found similar ERP waveforms in a study where visual feedback was always displayed at the same location during teleoperation of a mobile robot [29], [30]. This suggests that these signals are mainly related to the cognitive monitoring process rather than spatial visual attention, although is not yet clear whether these waveform generalize to other modalities of feedback.…”

Section: Discussionsupporting

confidence: 50%

Learning From EEG Error-Related Potentials in Noninvasive Brain-Computer Interfaces

Chavarriaga

Millán

2010

IEEE Trans. Neural Syst. Rehabil. Eng.

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Abstract-We describe error-related potentials generated while a human user monitors the performance of an external agent and discuss their use for a new type of brain-computer interaction. In this approach, single trial detection of error-related electroencephalography (EEG) potentials is used to infer the optimal agent behavior by decreasing the probability of agent decisions that elicited such potentials. Contrasting with traditional approaches, the user acts as a critic of an external autonomous system instead of continuously generating control commands. This sets a cognitive monitoring loop where the human directly provides information about the overall system performance that, in turn, can be used for its improvement. We show that it is possible to recognize erroneous and correct agent decisions from EEG (average recognition rates of 75.8% and 63.2%, respectively), and that the elicited signals are stable over long periods of time (from 50 to 600 days).Moreover, these performances allow to infer the optimal behavior of a simple agent in a brain-computer interaction paradigm after a few trials.Index Terms-Brain-computer interface, electroencephalography (EEG), error-related potentials, reinforcement learning.

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Section: Discussionsupporting

confidence: 50%

Learning From EEG Error-Related Potentials in Noninvasive Brain-Computer Interfaces

Chavarriaga

Millán

2010

IEEE Trans. Neural Syst. Rehabil. Eng.

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219

296

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“…Further work is being performed to test online classification of these signals in simulated and real devices [17], [18]. In addition, source localization and connectivity methods will be explored to assess the information flow from sensory specific areas towards unified error-monitoring structures (e.g.…”

Section: Discussionmentioning

confidence: 99%

Anticipation- and error-related EEG signals during realistic human-machine interaction: A study on visual and tactile feedback

Chavarriaga¹,

Perrin

Siegwart

et al. 2012

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-The exploitation of EEG signatures of cognitive processes can provide valuable information to improve interaction with brain actuated devices. In this work we study these correlates in a realistic situation simulated in a virtual reality environment. We focus on cortical potentials linked to the anticipation of future events (i.e. the contingent negative variation, CNV) and error-related potentials elicited by both visual and tactile feedback. Experiments with 6 subjects show brain activity consistent with previous studies using simpler stimuli, both at the level of ERPs and single trial classification. Moreover, we observe comparable signals irrespective of whether the subject was required to perform motor actions. Altogether, these results support the possibility of using these signals for practical brain machine interaction.

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“…Following the gating approach for shared control, a semiautonomous navigation strategy for a mobile robot has been introduced where the proposed actions by the robot may be denied or approved via error-related EEG potentials (ErrP) [33]. In this study, the robotic system determines the possible decision points (e.g.…”

Section: B Contextual Gatingmentioning

confidence: 99%

Long-Term Stable Control of Motor-Imagery BCI by a Locked-In User Through Adaptive Assistance

Saeedi

Chavarriaga

Millán

2017

IEEE Trans. Neural Syst. Rehabil. Eng.

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Abstract-Performance variation is one of the main challenges that BCIs are confronted with, when being used over extended periods of time. Shared control techniques could partially cope with such a problem. In this paper, we propose a taxonomy of shared control approaches used for BCIs and we review some of the recent studies at the light of these approaches. We posit that the level of assistance provided to the BCI user should be adjusted in real time in order to enhance BCI reliability over time. This approach has not been extensively studied in the recent literature on BCIs.In addition, we investigate the effectiveness of providing online adaptive assistance in a motor-imagery BCI for a tetraplegic enduser with an incomplete locked-in syndrome in a longitudinal study lasting 11 months. First, we report a reliable estimation of the BCI performance (in terms of command delivery time) using only a window of 1 s in the beginning of trials (AUC≈ 0.8). Second, we demonstrate how adaptive shared control can exploit the output of the performance estimator to adjust online the level of assistance in a BCI game by regulating its speed. In particular, online adaptive assistance was superior to a fixed condition in terms of success rate (p < 0.01). Remarkably, the results exhibited a stable performance over several months without recalibration of the BCI classifier or the performance estimator.

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Brain-coupled interaction for semi-autonomous navigation of an assistive robot

Cited by 98 publications

References 12 publications

Learning From EEG Error-Related Potentials in Noninvasive Brain-Computer Interfaces

Learning From EEG Error-Related Potentials in Noninvasive Brain-Computer Interfaces

Anticipation- and error-related EEG signals during realistic human-machine interaction: A study on visual and tactile feedback

Long-Term Stable Control of Motor-Imagery BCI by a Locked-In User Through Adaptive Assistance

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