Goal-recognition-based adaptive brain-computer interface for navigating immersive robotic systems

Abu-Alqumsan, Mohammad; Ebert, Felix; Peer, Angelika

doi:10.1088/1741-2552/aa66e0

Cited by 8 publications

(2 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, since the decoding control commands U H could not accurately represent the intent of subject, the intended control commands H (that the subject really want to generate) should be considered. Therefore, the user model should be formulated as follow [45,47]:…”

Section: Predictionmentioning

confidence: 99%

“…Here, we use Score of U H,k as an approximation to P(U H,k |H k ) at time instant k, and the probability estimation of P(H k |G k ) is sampled from the Gaussian distribution based on the distance between H k and G k . Note that, ⟨2⟩ is based on the assumption that knowledge about intended control command H k makes knowledge of the intent G k superfluous [47].…”

Section: Predictionmentioning

confidence: 99%

See 1 more Smart Citation

Self-adaptive shared control with brain state evaluation network for human-wheelchair cooperation

Deng

Lin

et al. 2020

J. Neural Eng.

View full text Add to dashboard Cite

Objective. For the shared control systems, how to trade off the control weight between robot autonomy and human operator is an important issue, especially for BCI-based systems. However, most of existing shared controllers have paid less attention to the effects caused by subjects with different levels of brain control ability. Approach. In this paper, a brain state evaluation network, termed BSE-NET, is proposed to evaluate subjects’ brain control ability online based on quantized attention-gated kernel reinforcement learning. With the output of BSE-NET (confidence score), a shared controller is designed to dynamically adjust the control weight between robot autonomy and human operator. Main results. The experimental results show that most of subjects achieved high and stable experimental success rate of approximately 90%. Furthermore, for subjects with different accuracy on EEG decoding, a proper confidence score can be dynamically generated to reflect their levels of brain control ability, and the proposed system can effectively adjust the control weight in all-time shared control. Significance. We discuss how our proposed method shows promise for BCI applications that can evaluate subjects’ brain control ability online as well as provide a method for the research on self-adaptive shared control to adaptively balance control weight between subject’s instruction and robot autonomy.

show abstract

Section: Predictionmentioning

confidence: 99%

Section: Predictionmentioning

confidence: 99%