Objective. The use of an electroencephalogram (EEG) anticipation-related component, the expectancy wave (E-wave), in brain–machine interaction was proposed more than 50 years ago. This possibility was not explored for decades, but recently it was shown that voluntary attempts to select items using eye fixations, but not spontaneous eye fixations, are accompanied by the E-wave. Thus, the use of the E-wave detection was proposed for the enhancement of gaze interaction technology, which has a strong need for a mean to decide if a gaze behavior is voluntary or not. Here, we attempted at estimating whether this approach can be used in the context of moving object selection through smooth pursuit eye movements. Approach. Eighteen participants selected, one by one, items which moved on a computer screen, by gazing at them. In separate runs, the participants performed tasks not related to voluntary selection but also provoking smooth pursuit. A low-cost consumer-grade eye tracker was used for item selection. Main results. A component resembling the E-wave was found in the averaged EEG segments time-locked to voluntary selection events of every participant. Linear discriminant analysis with shrinkage regularization classified the intentional and spontaneous smooth pursuit eye movements, using single-trial 300 ms long EEG segments, significantly above chance in eight participants. When the classifier output was averaged over ten subsequent data segments, median group ROC AUC of 0.75 was achieved. Significance. The results suggest the possible usefulness of the E-wave detection in the gaze-based selection of moving items, e.g. in video games. This technique might be more effective when trial data can be averaged, thus it could be considered for use in passive interfaces, for example, in estimating the degree of the user’s involvement during gaze-based interaction.

Voice- and gaze-based hands-free input are increasingly used in human-machine interaction. Attempts to combine them into a hybrid technology typically employ the voice channel as an information-rich channel. Voice seems to be “overqualified” to serve simply as a substitute of a computer mouse click, to confirm selections made by gaze. It could be expected that the user would feel discomfort if they had to frequently make “clicks” using their voice, or easily get bored, which also could lead to low performance. To test this, we asked 23 healthy participants to select moving objects with smooth pursuit eye movements. Manual confirmation of selection was faster and rated as more convenient than voice-based confirmation. However, the difference was not high, especially when voice was used to pronounce objects’ numbers (speech recognition was not applied): Score of convenience (M ± SD) was 9.2 ± 1.1 for manual and 8.0 ± 2.1 for voice confirmation, and time spent per object was 1269 ± 265 ms and 1626 ± 331 ms, respectively. We conclude that “voice-as-click” can be used to confirm selection in gaze-based interaction with computers as a substitute for the computer mouse click when manual confirmation cannot be used.

Demonstration of faster target selection by gaze compared to computer mouse so far was limited to targets attracting attention due to their visual saliency. This task, however, can be performed much faster with modern computer vision systems. Can gaze be faster than mouse in a more “intentional” selection task: when targets and non-targets do not significantly differ by their visual features? We propose that this may be the case when targets are moving at speeds beneficial for smooth pursuit eye movements. 16 healthy participants were asked to select 20 balls numbered 1 to 20 in numerical order. Balls were moving linearly at a screen in different directions at 12°/s speed. We compared selection made using a consumer grade eye tracker and a simple smooth pursuit detection algorithm with selection made using a computer mouse, either with clicks or pursuit. Compared to both mouse selection techniques, gaze selection was significantly faster and was experienced as more convenient by all participants.

This paper addresses several critical aspects of using optically pumped magnetometers (OPMs), focusing on both metrological issues and the enhancement of signal quality. We present a quantitative methodology for OPM measurements standardization and quality evaluation, which is crucial in biomedical applications like magnetoencephalography (MEG). Additionally, we introduce a novel, cost-effective portable active magnetic shielding system - digital adaptive suppression system (DASS) – that represents a significant advancement over traditional analog active shielding. Through comprehensive experimental research, we evaluate first-and third-generation commercial OPMs from QuSpin Inc. in two distinct magnetic environments. Our results demonstrate that the DASS ensures optimal and reliable OPM performance, even in noisy urban settings, surpassing the effectiveness of conventional analog shielding. These findings highlight the need for advanced magnetic shielding solutions to enhance the accuracy and reproducibility of OPM measurements.

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.