Semantic image sorting method for RSVP presentation

• Published in: Journal of neural engineering Vol. 21; no. 3; pp. 36018 - 36036
• Main Authors: Wu, Hao, Li, Fu, Chu, Wenlong, Li, Yang, Niu, Yi, Shi, Guangming, Zhang, Lijian, Chen, Yuanfang
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 01.06.2024
• Subjects: Adult; Algorithms; EEG; Electroencephalography - methods; ERP; Evoked Potentials, Visual - physiology; Female; Humans; Male; object detection; P300; Pattern Recognition, Visual - physiology; Photic Stimulation - methods; RSVP; Semantics; Young Adult; ERP; object detection; RSVP; EEG; P300
• ISSN: 1741-2560; 1741-2552
• DOI: 10.1088/1741-2552/ad4593

The rapid serial visual presentation (RSVP) paradigm, which is based on the electroencephalogram (EEG) technology, is an effective approach for object detection. It aims to detect the event-related potentials (ERP) components evoked by target images for rapid identification. However, the object detection performance within this paradigm is affected by the visual disparity between adjacent images in a sequence. Currently, there is no objective metric to quantify this visual difference. Consequently, a reliable image sorting method is required to ensure the generation of a smooth sequence for effective presentation. . In this paper, we propose a novel semantic image sorting method for sorting RSVP sequences, which aims at generating sequences that are perceptually smoother in terms of the human visual experience. . We conducted a comparative analysis between our method and two existing methods for generating RSVP sequences using both qualitative and quantitative assessments. A qualitative evaluation revealed that the sequences generated by our method were smoother in subjective vision and were more effective in evoking stronger ERP components than those generated by the other two methods. Quantitatively, our method generated semantically smoother sequences than the other two methods. Furthermore, we employed four advanced approaches to classify single-trial EEG signals evoked by each of the three methods. The classification results of the EEG signals evoked by our method were superior to those of the other two methods. . In summary, the results indicate that the proposed method can significantly enhance the object detection performance in RSVP-based sequences.