Gaze-based input to machines utilizes the ability of eye-gaze to serve as a user’s “output”. However, gaze should also support information flow in the opposite direction, namely, “input” to the user’s visual system from a machine’s output. The two functions can be easily separated in some tasks, like eye typing, but more complex scenarios typically require users to perform additional actions to avoid misinterpreting their intent. In this study, we modeled a free-behavior interaction with rapid transitions between visual search, decision-making, and gaze-based input operations through an engaging game called EyeLines. When playing the game, 15 volunteers selected screen objects using a 500 ms dwell time without additional actions for intention confirmation. By applying machine learning algorithms to gaze features and action context information, we achieved a threefold reduction in false positives, improved the quality of in-game decisions, and increased participant satisfaction with system ergonomics. To our knowledge, this is the first study that demonstrates the effectiveness of machine learning applied to gaze features in enhancing gaze-based interaction within visually challenging environments.

Quasi-movements (QM) are observed when an individual minimizes a movement to an extent that no related muscle activation is detected. Likewise to imaginary movements (IM) and overt movements, QMs are accompanied by the event-related desynchronization (ERD) of EEG sensorimotor rhythms. Stronger ERD was observed under QMs compared to IMs in some studies. However, the difference could be caused by the remaining muscle activation in QMs that could escape detection. Here, we re-examined the relation between the electromyography (EMG) signal and ERD in QM using sensitive data analysis procedures. More trials with signs of muscle activation were observed in QMs compared with a visual task and IMs. However, the rate of such trials was not correlated with subjective estimates of actual movement. Contralateral ERD did not depend on the EMG but still was stronger in QMs compared with IMs. These results suggest that brain mechanisms are common for QMs in the strict sense and “quasi-quasi-movements” (attempts to perform the same task accompanied by detectable EMG elevation) but differ between them and IMs. QMs could be helpful in research aimed at better understanding motor action and at modeling the use of attempted movements in the brain-computer interfaces with healthy participants.

Mobile robots have many applications in the modern world. The autonomy of robots is increasing, but critical cases like search and rescue missions must involve the possibility of human intervention for ethical reasons and safety. To achieve effective human–robot interaction, the operator needs to have a sense of agency (SoA) over the activities of the robot. One possible way to increase one's SoA in remote control could be the use of VR technology. The remote control situation has some important features, so indicators of SoA need to be reproduced there independently. In our study, participants controlled a mobile robot using either a monitor or a VR-headset as an output device. In both cases, active control was contrasted with passive observation of the robot's movement. In each trial, participants estimated the distance traveled by the robot—a putative implicit indicator of SoA. A significant difference between subjective distance estimates was found in the active and passive conditions with the monitor, but not in the active and passive conditions with VR. The effect obtained in the monitor conditions suggests that distance estimates can be used as an implicit indicator of SoA in robot remote control. We believe that the lack of difference between the active and passive conditions in VR was caused by motion sickness due to a mismatch of visual and vestibular sensory cues, leading to a weakened SoA.