The Expectation Based Eye-Brain-Computer Interface

Nuzhdin, Yuri O.; Shishkin, Sergei L.; Fedorova, Anastasia A.; Trofimov, Alexander G.; Svirin, Evgeniy; Kozyrskiy, Bogdan L.; Medyntsev, Alexei A.; Dubynin, Ignat A.; Velichkovsky, Boris M.

doi:10.1145/3038439.3038446

Cited by 5 publications

(1 citation statement)

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“…In subsequent studies, we found that the EEG marker for the gaze dwells intentionally used for control does not depend on gaze direction (Korsun et al, 2017) and demonstrated that initially chosen approaches to construct feature sets and the classifier can be further improved (Shishkin et al, 2016a). Preliminary attempts to classify the 500 ms gaze dwells online using the passive expectationbased BCI (Nuzhdin et al, 2017a;Nuzhdin et al, 2017 in press) so far have not shown a significant improvement compared to gaze alone. This could be related to a suboptimal classifier and/or inadequate choice of tests, because in the game we used, actions were quickly automated at the beginning of our research, while SPN amplitude is likely to decrease precisely under such conditions.…”

Section: Selection Of a Moving Target From A Swarmmentioning

confidence: 98%

Gaze-and-brain-controlled interfaces for human-computer and human-robot interaction

Shishkin¹,

Zhao²,

Isachenko³

et al. 2017

Psych. Rus.

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Background. Human-machine interaction technology has greatly evolved during the last decades, but manual and speech modalities remain single output channels with their typical constraints imposed by the motor system's information transfer limits. Will braincomputer interfaces (BCIs) and gaze-based control be able to convey human commands or even intentions to machines in the near future? We provide an overview of basic approaches in this new area of applied cognitive research.objective. We test the hypothesis that the use of communication paradigms and a combination of eye tracking with unobtrusive forms of registering brain activity can improve human-machine interaction. methods and Results. Three groups of ongoing experiments at the Kurchatov Institute are reported. First, we discuss the communicative nature of human-robot interaction, and approaches to building a more efficient technology. Specifically, "communicative" patterns of interaction can be based on joint attention paradigms from developmental psychology, including a mutual "eye-to-eye" exchange of looks between human and robot. Further, we provide an example of "eye mouse" superiority over the computer mouse, here in emulating the task of selecting a moving robot from a swarm. Finally, we demonstrate a passive, noninvasive BCI that uses EEG correlates of expectation. This may become an important filter to separate intentional gaze dwells from non-intentional ones.conclusion. The current noninvasive BCIs are not well suited for human-robot interaction, and their performance, when they are employed by healthy users, is critically dependent on the impact of the gaze on selection of spatial locations. The new approaches discussed show a high potential for creating alternative output pathways for the

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Section: Selection Of a Moving Target From A Swarmmentioning

confidence: 98%