RT Journal Article
T1 Enhanced average for event-related potential analysis using dynamic time warping
A1 Molina-Molina, Mario
A1 Tardón-García, Lorenzo José
A1 Barbancho-Pérez, Ana María
A1 De Torres García, Irene
A1 Barbancho-Pérez, Isabel
K1 Biomedicina
K1 Electroencefalografía
K1 Procesado de señales
AB Electroencephalography (EEG) provides a way to understand, and evaluate neurotransmission. In this context, time-locked EEG activity or event-related potentials (ERPs) are often used to capture neural activity related to specific mental processes. Normally, they are considered on the basis of averages across a number of trials. However, there exist notable variability in latency jitter, jitter, and amplitude, across trials, and, also, across users; this causes the average ERP waveform to blur, and, furthermore, diminish the amplitude of underlying waves. For these reasons, a strategy is proposed for obtaining ERP waveforms based on dynamic time warping (DTW) to adapt, and adjust individual trials to the averaged ERP, previously calculated, to build an enhanced average by making use of these warped signals. At the sight of the experiments carried out on the behaviour of the proposed scheme using publicly available datasets, this strategy reduces the attenuation in amplitude of ERP components thanks to the reduction of the influence of variability of latency and jitter, and, thus, improves the averaged ERP waveforms.
PB Elsevier
YR 2023
FD 2023-10-05
LK https://hdl.handle.net/10630/27903
UL https://hdl.handle.net/10630/27903
LA eng
NO Mario Molina, Lorenzo J. Tardón, Ana M. Barbancho, Irene De-Torres, Isabel Barbancho, Enhanced average for event-related potential analysis using dynamic time warping, Biomedical Signal Processing and Control, Volume 87, Part B, 2024, 105531, ISSN 1746-8094, https://doi.org/10.1016/j.bspc.2023.105531. (https://www.sciencedirect.com/science/article/pii/S1746809423009643)
NO This publication is part of project PID2021-123207NB-I00, fundedby MCIN/AEI /10.13039/501100011033 / FEDER, UE. This work waspartially funded by Junta de Andalucía, Proyectos de I+D+i, in theframework of Plan Andaluz de Investigación, Desarrollo e Innovación(PAIDI 2020), under Project No. PY20_00237. Funding for open accesscharge: Universidad de Málaga/CBUA. This work was done atUniversidad de Málaga, Campus de Excelencia Internacional AndaluciaTech.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026