Interacting with multiple objects simultaneously makes us fast. A pre-step to this interaction is to select the objects, i.e., multi-object selection, which is enabled through two steps: (1) toggling multi-selection mode — mode-switching — and then (2) selecting all the intended objects — subselection. In extended reality (XR), each step can be performed with the eyes, hands, and voice. To examine how design choices affect user performance, we evaluated four mode-switching (SemiPinch, FullPinch, DoublePinch, and Voice) and three subselection techniques (Gaze+Dwell, Gaze+Pinch, and Gaze+Voice) in a user study. Results revealed that while DoublePinch paired with Gaze+Pinch yielded the highest overall performance, SemiPinch achieved the lowest performance. Although Voice-based mode-switching showed benefits, Gaze+Voice subselection was less favored, as the required repetitive vocal commands were perceived as tedious. Overall, these findings provide empirical insights and inform design recommendations for multi-selection techniques in XR.

Constant-and Dual-Threshold Dwell keyboards

Any investigation of learning unfamiliar text entry systems is affected by the need to train participants on multiple new components simultaneously, such as novel interaction techniques and layouts. The Guided Evaluation Method (GEM) addresses this challenge by bypassing the need to learn layout-specific skills for text entry. However, a gap remains as the GEM's performance has not been assessed in situations where users are unfamiliar with the interaction technique involved, here eye-gaze-based dwell. To address this, we trained participants on only the eye-gaze-based interaction technique over eight days with QWERTY and then evaluated their performance on the OPTI layout with the GEM. Results showed that the unfamiliar OPTI layout outperformed QWERTY, with QWERTY's speed aligning with previous findings, suggesting that interaction technique expertise significantly impacts performance outcomes. Importantly, we also identified that for scenarios where the familiarity with the involved interaction technique(s) is the same, the GEM analyzes the performance of keyboard layouts effectively and quickly identifies the best option.

In eye-gaze-based selection, dwell suffers from several issues, e.g., the Midas Touch problem. Here we investigate saccade-based selection techniques as an alternative to dwell. First, we designed a novel user interface (UI) for Actigaze and used it with (goal-crossing) saccades for confirming the selection of small targets (i.e., < 1.5-2°). We compared it with three other variants of Actigaze (with button press, dwell, and target reverse crossing) and two variants of target magnification (with button press and dwell). Magnification-dwell exhibited the most promising performance. For Actigaze, goal-crossing was the fastest option but suffered the most errors. We then evaluated goal-crossing as a primary selection technique for normal-sized targets (≥ 2°) and implemented a novel UI for such interaction. Results revealed that dwell achieved the best performance. Yet, we identified goal-crossing as a good compromise between dwell and button press. Our findings thus identify novel options for gaze-only interaction.

Previous research on eye-hand coordination training systems has investigated user performance on a wall, 2D touchscreens, and in Virtual Reality (VR). In this paper, we designed an eye-hand coordination reaction test to investigate and compare user performance in three different virtual environments (VEs) – VR, Augmented Reality (AR), and a 2D touchscreen. VR and AR conditions also included two feedback conditions – mid-air and passive haptics. Results showed that compared to AR, participants were significantly faster and made fewer errors both in 2D and VR. However, compared to VR and AR, throughput performance of the participants was significantly higher in the 2D touchscreen condition. No significant differences were found between the two feedback conditions. The results show the importance of assessing precision and accuracy in eye-hand coordination training and suggest that it is currently not advisable to use AR headsets in such systems.

Pre-processing, Feature Extraction, Feature Selection and Classification are the four sub modules of the Signal Processing module of a typical BCI system. Pattern recognition is mainly involved in this Signal Processing module and in this paper, we experimented with different state-of-the-art algorithms for each of these submodules on two separate datasets we acquired using Emotiv EPOC and the Muse headband from 38 college-aged young adults. For our experiment, we used two artefact removal techniques, namely Stationary Wavelet Transform (SWT) based denoising technique and an extended SWT technique (SWTSD). We found SWTSD improves average classification accuracy up to 7.2 % and performs better than SWT. However, that does not state that SWTSD will outperform SWT when implemented on other BCI paradigms or on other EEG-based applications. In our study, the highest average accuracy achieved by the data of the Muse headband and Emotiv EPOC were 77.7% and 66.7% respectively and from our results we conclude that, the performance of different BCI systems depends on several different factors including artefact removal techniques, filters, feature extraction and selection algorithms, classifiers, etc. and appropriate choice and usage of such methods can have a significant positive impact on the end results.