Objective . The biggest advantage of steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI) lies in its large command set and high information transfer rate (ITR). Almost all current SSVEP–BCIs use a computer screen (CS) to present flickering visual stimuli, which limits its flexible use in actual scenes. Augmented reality (AR) technology provides the ability to superimpose visual stimuli on the real world, and it considerably expands the application scenarios of SSVEP–BCI. However, whether the advantages of SSVEP–BCI can be maintained when moving the visual stimuli to AR glasses is not known. This study investigated the effects of the stimulus number for SSVEP–BCI in an AR context. Approach. We designed SSVEP flickering stimulation interfaces with four different numbers of stimulus targets and put them in AR glasses and a CS to display. Three common recognition algorithms were used to analyze the influence of the stimulus number and stimulation time on the recognition accuracy and ITR of AR–SSVEP and CS–SSVEP. Main results. The amplitude spectrum and signal-to-noise ratio of AR–SSVEP were not significantly different from CS–SSVEP at the fundamental frequency but were significantly lower than CS–SSVEP at the second harmonic. SSVEP recognition accuracy decreased as the stimulus number increased in AR–SSVEP but not in CS–SSVEP. When the stimulus number increased, the maximum ITR of CS–SSVEP also increased, but not for AR–SSVEP. When the stimulus number was 25, the maximum ITR (142.05 bits min ⁻¹ ) was reached at 400 ms. The importance of stimulation time in SSVEP was confirmed. When the stimulation time became longer, the recognition accuracy of both AR–SSVEP and CS–SSVEP increased. The peak value was reached at 3 s. The ITR increased first and then slowly decreased after reaching the peak value. Significance . Our study indicates that the conclusions based on CS–SSVEP cannot be simply applied to AR–SSVEP, and it is not advisable to set too many stimulus targets in the AR display device.

Steady-state visual evoked potentials-based brain-computer interfaces (SSVEP-BCI) has the advantage of high information transfer rate (ITR) and little user training, and it has a high application value in the field of disability assistance and human-computer interaction. Generally SSVEP-BCI requires a personal computer screen (PC) to display several repetitive visual stimuli for inducing the SSVEP response, which reduces its portability and flexibility. Using augmented reality (AR) glasses worn on the head to display the repetitive visual stimuli could solve the above drawbacks, but whether it could achieve the same accuracy as PC screen in the case of reduced brightness and increased interference is unknown. In current study, we firstly designed 4 stimulus layouts and displayed them with Microsoft HoloLens (AR-SSVEP) glasses, comparison analysis showed that the classification accuracies are influenced by the stimulus layout when the stimulus duration is less than 3s. When the stimulus duration exceeds 3s, there is no significant accuracy difference between the 4 layouts. Then we designed a similar experimental paradigm on PC screen (PC-SSVEP) based on the best layout of AR. Classification results showed that AR-SSVEP achieved similar accuracy with PC-SSVEP when the stimulus duration is more than 3s, but when the stimulus duration is less than 2s, the accuracy of AR-SSVEP is lower than PC-SSVEP. Brain topological analysis indicated that the spatial distribution of SSVEP responses is similar, both of which are strongest in the occipital region. Current study indicated that stimulus layout is a key factor when building SSVEP-BCI with AR glasses, especially when the stimulation time is short.

Satellite-based remote sensing technologies are utilized extensively to investigate urban thermal environment under rapid urban expansion. Current Moderate Resolution Imaging Spectroradiometer (MODIS) data are, however, unable to adequately represent the spatially detailed information because of its relatively coarser spatial resolution, while Landsat data cannot explore the temporally continued analysis due to the lower temporal resolution. Combining MODIS and Landsat data, “Landsat-like” data were generated by using the Flexible Spatiotemporal Data Fusion method (FSDAF) to measure land surface temperature (LST) variations, and Landsat-like data including Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built Index (NDBI) were generated to analyze LST dynamic driving forces. Results show that (1) the estimated “Landsat-like” data are capable of measuring the LST variations; (2) with the urban expansion from 2013 to 2016, LST increases ranging from 1.80 °C to 3.92 °C were detected in areas where the impervious surface area (ISA) increased, while LST decreases ranging from −3.52 °C to −0.70 °C were detected in areas where ISA decreased; (3) LST has a significant negative correlation with the NDVI and a strong positive correlation with NDBI in summer. Our findings can provide information useful for mitigating undesirable thermal conditions and for long-term urban thermal environmental management.

Because an augmented-reality-based brain-computer interface (AR-BCI) is easily disturbed by external factors, the traditional electroencephalograph (EEG) classification algorithms fail to meet the real-time processing requirements with a large number of stimulus targets or in a real environment. We propose a multi-target fast classification method for augmented-reality-based steady-state visual evoked potential (AR-SSVEP), using a convolutional neural network (CNN). To explore the availability and accuracy of high-efficiency multi-target classification methods in AR-SSVEP with a short stimulation duration, a similar stimulus layout was used for a computer screen (PC) and an optical see-through head-mounted display (OST-HMD) device (HoloLens). The experiment included nine flicker stimuli of different frequencies, and a multi-target fast classification method based on a CNN was constructed to complete nine classification tasks, for which the average accuracy of AR-BCI in our CNN model at 0.5- and 1-s stimulus duration was 67.93% and 80.83%, respectively. These results verified the efficacy of the proposed model for processing multi-target classification in AR-BCI.