Assessing the influence of latency variability on EEG classifiers - a case study of face repetition priming

Aniyan AK, Philip NS, Samar VJ, Desjardins JA, Segalowitz SJ (2014) A wavelet based algorithm for the identification of oscillatory event-related potential components. J Neurosci Methods 233:63–72. https://doi.org/10.1016/j.jneumeth.2014.06.004

Article  PubMed  Google Scholar 

Bridwell DA, Cavanagh JF, Collins AGE, Nunez MD, Srinivasan R, Stober S, Calhoun VD (2018) Moving beyond ERP components: a selective review of approaches to integrate EEG and behavior. Front Hum Neurosci 12:106. https://doi.org/10.3389/fnhum.2018.00106

Article  PubMed  PubMed Central  Google Scholar 

Carlson T, Tovar DA, Alink A, Kriegeskorte N (2013) Representational dynamics of object vision: the first 1000 ms. J Vis 13(10):1. https://doi.org/10.1167/13.10.1

Article  PubMed  Google Scholar 

Cerutti S, Bersani V, Carrara A, Liberati D (1987) Analysis of visual evoked potentials through Wiener filtering applied to a small number of sweeps. J Biomed Eng 9(1):3–12. https://doi.org/10.1016/0141-5425(87)90093-8

Article  CAS  PubMed  Google Scholar 

Elman JL (1990) Finding structure in time. Cogn Sci 14(2):179–211. https://doi.org/10.1207/s15516709cog1402_1

Article  Google Scholar 

Garrett DD, Samanez-Larkin GR, MacDonald SWS, Lindenberger U, McIntosh AR, Grady CL (2013) Moment-to-moment brain signal variability: a next frontier in human brain mapping? Neurosci Biobehavioral Reviews 37(4):610–624. https://doi.org/10.1016/j.neubiorev.2013.02.015

Article  Google Scholar 

Gu X, Cao Z, Jolfaei A, Xu P, Wu D, Jung T-P, Lin C-T (2021) EEG-based brain-computer interfaces (BCIs): a survey of recent studies on signal sensing technologies and computational intelligence approaches and their applications. IEEE/ACM Trans Comput Biol Bioinf 18(5):1645–1666. https://doi.org/10.1109/TCBB.2021.3052811

Article  Google Scholar 

Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning. Springer. https://doi.org/10.1007/978-0-387-84858-7

Herzmann G, Sommer W (2007) Memory-related ERP components for experimentally learned faces and names: characteristics and parallel-test reliabilities. Psychophysiology 44(2):262–276. https://doi.org/10.1111/j.1469-8986.2007.00505.x

Article  PubMed  Google Scholar 

Herzmann G, Schweinberger SR, Sommer W, Jentzsch I (2004) What’s special about personally familiar faces? A multimodal approach. Psychophysiology 41(5):688–701. https://doi.org/10.1111/j.1469-8986.2004.00196.x

Article  PubMed  Google Scholar 

Hu L, Liang M, Mouraux A, Wise RG, Hu Y, Iannetti GD (2011) Taking into account latency, amplitude, and morphology: improved estimation of single-trial ERPs by wavelet filtering and multiple linear regression. J Neurophysiol 106(6):3216–3229. https://doi.org/10.1152/jn.00220.2011

Article  CAS  PubMed  PubMed Central  Google Scholar 

Ismail Fawaz H, Forestier G, Weber J, Idoumghar L, Muller P-A (2019) Deep learning for time series classification: a review. Data Min Knowl Disc 33(4):917–963. https://doi.org/10.1007/s10618-019-00619-1

Article  Google Scholar 

Jaśkowski P, Verleger R (1999) Amplitudes and latencies of single-trial ERP’s estimated by a maximum-likelihood method. IEEE Trans Bio Med Eng 46(8):987–993. https://doi.org/10.1109/10.775409

Article  Google Scholar 

Jung TP, Makeig S, Westerfield M, Townsend J, Courchesne E, Sejnowski TJ (2001) Analysis and visualization of single-trial event-related potentials. Hum Brain Mapp 14(3):166–185. https://doi.org/10.1002/hbm.1050

Article  CAS  PubMed  PubMed Central  Google Scholar 

Kaltwasser L, Hildebrandt A, Recio G, Wilhelm O, Sommer W (2014) Neurocognitive mechanisms of individual differences in face cognition: a replication and extension. Cogn Affect Behav Neurosci 14(2):861–878. https://doi.org/10.3758/s13415-013-0234-y

Article  PubMed  Google Scholar 

King J-R, Gramfort A, Schurger A, Naccache L, Dehaene S (2014) Two distinct dynamic modes subtend the detection of unexpected sounds. PLoS ONE 9(1):e85791. https://doi.org/10.1371/journal.pone.0085791

Article  CAS  PubMed  PubMed Central  Google Scholar 

Lawhern VJ, Solon AJ, Waytowich NR, Gordon SM, Hung CP, Lance BJ (2018) EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces. J Neural Eng 15(5):056013. https://doi.org/10.1088/1741-2552/aace8c

Article  PubMed  Google Scholar 

Luck SJ (2014) An introduction to the event-related potential technique (Second Edition). MIT Press, Cambridge

Mahendran A, Vedaldi A (2016) Salient deconvolutional networks. In: Leibe B, Matas J, Sebe N, Welling M (eds) Computer Vision – ECCV 2016. Springer International Publishing, p. 120–135. https://doi.org/10.1007/978-3-319-46466-4_8

McCarthy G, Wood CC (1985) Scalp distributions of event-related potentials: an ambiguity associated with analysis of variance models. Electroencephalogr Clin Neurophysiol 62(3):203–208. https://doi.org/10.1016/0168-5597(85)90015-2

Article  CAS  PubMed  Google Scholar 

Nowparast Rostami H, Sommer W, Zhou C, Wilhelm O, Hildebrandt A (2017) Structural encoding processes contribute to individual differences in face and object cognition: inferences from psychometric test performance and event-related brain potentials. Cortex 95:192–210. https://doi.org/10.1016/j.cortex.2017.08.017

Article  PubMed  Google Scholar 

Ouyang G, Zhou C (2020) Characterizing the brain’s dynamical response from scalp-level neural electrical signals: a review of methodology development. Cogn Neurodyn 14(6):731–742. https://doi.org/10.1007/s11571-020-09631-4

Article  PubMed  PubMed Central  Google Scholar 

Ouyang G, Herzmann G, Zhou C, Sommer W (2011) Residue iteration decomposition (RIDE): a new method to separate ERP components on the basis of latency variability in single trials. Psychophysiology 48(12):1631–1647. https://doi.org/10.1111/j.1469-8986.2011.01269.x

Article  PubMed  Google Scholar 

Ouyang G, Sommer W, Zhou C (2015a) A toolbox for residue iteration decomposition (RIDE)—A method for the decomposition, reconstruction, and single trial analysis of event related potentials. J Neurosci Methods 250:7–21. https://doi.org/10.1016/j.jneumeth.2014.10.009

Article  PubMed  Google Scholar 

Ouyang G, Sommer W, Zhou C (2015b) Updating and validating a new framework for restoring and analyzing latency-variable ERP components from single trials with residue iteration decomposition (RIDE). Psychophysiology 52(6):839–856. https://doi.org/10.1111/psyp.12411

Article  PubMed  Google Scholar 

Ouyang G, Hildebrandt A, Sommer W, Zhou C (2017) Exploiting the intra-subject latency variability from single-trial event-related potentials in the P3 time range: a review and comparative evaluation of methods. Neurosci Biobehavioral Reviews 75:1–21. https://doi.org/10.1016/j.neubiorev.2017.01.023

Article  Google Scholar 

Paitel ER, Samii MR, Nielson KA (2021) A systematic review of cognitive event-related potentials in mild cognitive impairment and Alzheimer’s disease. Behav Brain Res 396:112904. https://doi.org/10.1016/j.bbr.2020.112904

Article  CAS  PubMed  Google Scholar 

Pavarini SCI, Brigola AG, Luchesi BM, Souza ÉN, Rossetti ES, Fraga FJ, Guarisco LPC, Terassi M, Oliveira NA, Hortense P, Pedroso RV, Ottaviani AC (2018) On the use of the P300 as a tool for cognitive processing assessment in healthy aging: a review. Dement Neuropsychologia 12:1–11. https://doi.org/10.1590/1980-57642018dn12-010001

Article  Google Scholar 

Petruo V, Takacs A, Mückschel M, Hommel B, Beste C (2021) Multi-level decoding of task sets in neurophysiological data during cognitive flexibility. iScience 24(12). https://doi.org/10.1016/j.isci.2021.103502

Rossion B, Gauthier I (2002) How does the brain process upright and inverted faces? Behav Cogn Neurosci Rev 1(1):63–75. https://doi.org/10.1177/1534582302001001004

Article  PubMed  Google Scholar 

Rostami HN, Saville CWN, Klein C, Ouyang G, Sommer W, Zhou C, Hildebrandt A (2017) COMT genotype is differentially associated with single trial variability of ERPs as a function of memory type. Biol Psychol 127:209–219. https://doi.org/10.1016/j.biopsycho.2017.06.002

Article  PubMed  Google Scholar 

Roy Y, Banville H, Albuquerque I, Gramfort A, Falk TH, Faubert J (2019) Deep learning-based electroencephalography analysis: a systematic review. J Neural Eng 16(5):051001. https://doi.org/10.1088/1741-2552/ab260c

Article  PubMed  Google Scholar 

Schirrmeister RT, Springenberg JT, Fiederer LDJ, Glasstetter M, Eggensperger K, Tangermann M, Hutter F, Burgard W, Ball T (2017) Deep learning with convolutional neural networks for EEG decoding and visualization. Hum Brain Mapp 38(11):5391–5420. https://doi.org/10.1002/hbm.23730

Article  PubMed  PubMed Central  Google Scholar 

Schweinberger SR, Neumann MF (2016) Repetition effects in human ERPs to faces. Cortex 80:141–153. https://doi.org/10.1016/j.cortex.2015.11.001

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