Asynchronous Video Target Detection Based on Single-Trial EEG Signals

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 28; no. 9; pp. 1931 - 1943
• Main Authors: Song, Xiyu, Yan, Bin, Tong, Li, Shu, Jun, Zeng, Ying
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Alignment; Asynchronous detection; Brain; brain-computer interface (BCI); Computer applications; Computer vision; EEG; Electroencephalography; event-related potential (ERP) alignment; Event-related potentials; False alarms; Human-computer interface; Implants; Internet; Iterative methods; Jitter; Latency; Methods; Object detection; Streaming media; Target detection; Task analysis; temporal variability; Unmanned aerial vehicles; Vibration
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2020.3009978

Event-related potentials (ERPs) are widely used in brain-computer interface (BCI) systems to detect sensitive targets. However, asynchronous BCI systems based on video-target-evoked ERPs can pose a challenge in real-world applications due to the absence of an explicit target onset time and the time jitter of the detection latency. To address this challenge, we developed an asynchronous detection framework for video target detection. In this framework, an ERP alignment method based on the principle of iterative minimum distance square error (MDSE) was proposed for constructing an ERP template and aligning signals on the same base to compensate for possible time jitter. Using this method, ERP response characteristics induced by video targets were estimated. Online video target detection results indicated that alignment methods reduced the false alarm more effectively than non-alignment methods. The false alarm of the proposed Aligned-MDSE method was one-third lower than that of existing alignment methods under the same right hit level using limited individual samples. Furthermore, cross-subject results indicated that untrained subjects could directly perform online detection tasks and achieve excellent performance by a general model trained from more than 10 subjects. The proposed asynchronous video target detection framework can thus have a significant impact on real-world BCI applications.