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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Comments (0)