Imagine you’re trying to spot objects while driving at night, when visibility is low. In this study, scientists developed a method to improve the performance of a task called rapid serial visual presentation (RSVP) using electroencephalography (EEG) signals. They combined the power of human and computer vision by fusing the two together. This fusion, called dynamic probability integration (DPI), employed a novel method that considered the classification abilities of different information sources and generated weights based on these abilities. To decode the EEG signals, they also designed a spatial-temporal hybrid algorithm. The results showed that DPI outperformed individual detection methods and two other fusion methods, achieving higher average area under the curve (AUC) values and balanced accuracy. Additionally, the spatial-temporal hybrid algorithm demonstrated superior performance compared to two baseline methods. These findings indicate that fusing human and computer vision can significantly improve object detection in RSVP tasks, even in challenging conditions. If you want to delve deeper into this exciting research, check out the full article!

BackgroundRapid serial visual presentation (RSVP) has become a popular target detection method by decoding electroencephalography (EEG) signals, owing to its sensitivity and effectiveness. Most current research on EEG-based RSVP tasks focused on feature extraction algorithms developed to deal with the non-stationarity and low signal-to-noise ratio (SNR) of EEG signals. However, these algorithms cannot handle the problem of no event-related potentials (ERP) component or miniature ERP components caused by the attention lapses of human vision in abnormal conditions. The fusion of human-computer vision can obtain complementary information, making it a promising way to become an efficient and general way to detect objects, especially in attention lapses.MethodsDynamic probability integration (DPI) was proposed in this study to fuse human vision and computer vision. A novel basic probability assignment (BPA) method was included, which can fully consider the classification capabilities of different heterogeneous information sources for targets and non-targets and constructs the detection performance model for the weight generation based on classification capabilities. Furthermore, a spatial-temporal hybrid common spatial pattern-principal component analysis (STHCP) algorithm was designed to decode EEG signals in the RSVP task. It is a simple and effective method of distinguishing target and non-target using spatial-temporal features.ResultsA nighttime vehicle detection based on the RSVP task was performed to evaluate the performance of DPI and STHCP, which is one of the conditions of attention lapses because of its decrease in visual information. The average AUC of DPI was 0.912 ± 0.041 and increased by 11.5, 5.2, 3.4, and 1.7% compared with human vision, computer vision, naive Bayesian fusion, and dynamic belief fusion (DBF), respectively. A higher average balanced accuracy of 0.845 ± 0.052 was also achieved using DPI, representing that DPI has the balanced detection capacity of target and non-target. Moreover, STHCP obtained the highest AUC of 0.818 ± 0.06 compared with the other two baseline methods and increased by 15.4 and 23.4%.ConclusionExperimental results indicated that the average AUC and balanced accuracy of the proposed fusion method were higher than individual detection methods used for fusion, as well as two excellent fusion methods. It is a promising way to improve detection performance in RSVP tasks, even in abnormal conditions.

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