학술논문
EEG Peak Detection in Cognitive Conflict Processing Using Summit Navigator and Clustering-Based Ranking
Document Type
Periodical
Author
Source
IEEE Transactions on Neural Systems and Rehabilitation Engineering IEEE Trans. Neural Syst. Rehabil. Eng. Neural Systems and Rehabilitation Engineering, IEEE Transactions on. 30:1548-1556 2022
Subject
Language
ISSN
1534-4320
1558-0210
1558-0210
Abstract
Correct detection of peaks in electroencephalogram (EEG) signals is of essence due to the significant correlation of those potentials with cognitive performance and disorders. This paper proposes a novel and non-parametric approach to detect prediction error negativity (PEN) in cognitive conflict processing. The PEN candidates are first located from the input signal via an adaptation of a recent effective method for local maxima extraction, processed in a multi-scale manner. The found candidates are then fused and ranked based on their shape and location-based features. False positives caused by candidates’ magnitude are eliminated by rotating the sorted candidate list where the one with the second-best ranking score will be identified as PEN. The EEG data collected from a 3D object selection task have been used to verify the efficacy of the proposed approach. Compared with the state-of-the-art peak detection techniques, the proposed method shows an improvement of at least 2.67% in accuracy and 6.27% in sensitivity while requires only about 4 ms to process an epoch. The accuracy and computational efficiency of the proposed technique in the detection of PEN in cognitive conflict processing would lead to promising applications in performance improvement of brain-computer interfaces (BCIs).