TY - JOUR
T1 - Low-Quality Video Target Detection Based on EEG Signal Using Eye Movement Alignment
AU - Shi, Jianting
AU - Bi, Luzheng
AU - Xu, Xinbo
AU - Feleke, Aberham Genetu
AU - Fei, Weijie
N1 - Publisher Copyright:
© 2024 Jianting Shi et al.
PY - 2024
Y1 - 2024
N2 - The target detection based on electroencephalogram (EEG) signals is a new target detection method. This method recognizes the target by decoding the specific neural response when an operator observes the target, which has important theoretical and application values. This paper focuses on the EEG detection of low-quality video targets, which breaks through the limitation of previous target detection based on EEG signals only for high-quality video targets. We first design an experimental paradigm for EEG-based low-quality video target detection and propose an epoch extraction method based on eye movement signals to solve the asynchronous problem faced by low-quality video target detection. Then, the neural representation in the process of operator recognition is analyzed based on the time domain, frequency domain, and source space domain, respectively. We design the time-frequency features based on continuous wavelet transform according to the neural representation and obtain an average decoding test accuracy of 84.56%. The research results of this paper lay the foundation for the development of a video target detection system based on EEG signals in the future.
AB - The target detection based on electroencephalogram (EEG) signals is a new target detection method. This method recognizes the target by decoding the specific neural response when an operator observes the target, which has important theoretical and application values. This paper focuses on the EEG detection of low-quality video targets, which breaks through the limitation of previous target detection based on EEG signals only for high-quality video targets. We first design an experimental paradigm for EEG-based low-quality video target detection and propose an epoch extraction method based on eye movement signals to solve the asynchronous problem faced by low-quality video target detection. Then, the neural representation in the process of operator recognition is analyzed based on the time domain, frequency domain, and source space domain, respectively. We design the time-frequency features based on continuous wavelet transform according to the neural representation and obtain an average decoding test accuracy of 84.56%. The research results of this paper lay the foundation for the development of a video target detection system based on EEG signals in the future.
UR - http://www.scopus.com/inward/record.url?scp=85199673001&partnerID=8YFLogxK
U2 - 10.34133/cbsystems.0121
DO - 10.34133/cbsystems.0121
M3 - Review article
AN - SCOPUS:85199673001
SN - 2097-1087
VL - 5
JO - Cyborg and Bionic Systems
JF - Cyborg and Bionic Systems
ER -