Registro:

Referencias:

• Cohen, M.X., (2014) Analyzing Neural Time Series Data: Theory and Practice, , MIT Press, London
• Aspinall, P., Panagiotis, M., Coyne, R., Roe, J., The urban brain: Analysing outdoor physical activity (2015) British Journal of Sports Medicine, 49, pp. 272-276. , https://doi.org/10.1136/bjsports-2012-091877, PMID: 23467965
• Mavros, P., Zaltz Austwick, M., Hudson Smith, A., Geo-EEG: Towards the use of EEG in the study of urban behaviour (2016) Applied Spatial Analysis, 9, pp. 191-212. , https://doi.org/10.1007/s12061-015-9181-z
• Toppi, J., Borghini, G., Petti, M., He, E.J., De Giusti, V., He, B., Investigating cooperative behavior in ecological settings: An EEG hyperscanning study (2016) PLoS ONE, 11. , https://doi.org/10.1371/journal.pone.0154236, PMID: 27124558
• Kuziek, J.W.P., Shienh, A., Mathewson, K.E., Transitioning EEG experiments away from the laboratory using a Raspberry Pi2 (2017) J Neurosci Methods, 277, pp. 75-82. , https://doi.org/10.1016/j.jneumeth.2016.11.013, PMID: 27894782
• St. John, A.M., Kao, K., Liederman, J., Grieve, P.G., Tarullo, A.R., Maternal cortisol slope at 6 months predicts infant cortisol slope and EEG power at 12 months (2017) Dev Psychobiol, 59, pp. 787-801. , https://doi.org/10.1002/dev.21540, PMID: 28686284
• Wilson, B.A., Ecological validity of neuropsychological assessment: Do neuropsychological indexes predict performance in everyday activities? (1993) Appl Prev Psychol, 2, pp. 209-215. , https://doi.org/10.1016/S0962-1849(05)80091-5
• Bowers, E.P., Von Eye, A., Lerner, J.V., Arbeit, M.R., Weiner, M.B., Chase, P., The role of ecological assets in positive and problematic developmental trajectories (2011) J Adolesc, 34, pp. 1151-1165. , https://doi.org/10.1016/j.adolescence.2011.07.007, PMID: 22118508
• Okahashi, S., Seki, K., Nagano, A., Luo, Z., Kojima, M., Futaki, T., A virtual shopping test for realistic assessment of cognitive function (2013) J Neuroeng Rehabil, 10, p. 59. , https://doi.org/10.1186/1743-0003-10-59PMID, 23777412
• Price, R.B., Allen, K.B., Silk, J.S., Ladouceur, C.D., Ryan, N.D., Dahl, R.E., Vigilance in the laboratory predicts avoidance in the real world: A dimensional analysis of neural, behavioral, and ecological momentary data in anxious youth (2016) Dev Cogn Neurosci, 19, pp. 128-136. , https://doi.org/10.1016/j.dcn.2016.03.001, PMID: 27010577
• Mota, N.B., Weissheimer, J., Madruga, B., Adamy, N., Bunge, S.A., Copelli, M., A naturalistic assessment of the organization of children’s memories predicts cognitive functioning and reading ability (2016) Mind, Brain, Educ, 10, pp. 184-195. , https://doi.org/10.1111/mbe.12122
• Robertson, K., Schmitter-Edgecombe, M., Naturalistic tasks performed in realistic environments: A review with implications for neuropsychological assessment (2017) Clin Neuropsychol, 31, pp. 16-42. , https://doi.org/10.1080/13854046.2016.1208847, PMID: 27406700
• Badcock, N., Mousikou, P., Mahajan, Y., De Lissa, P., Thie, J., McArthur, G., Validation of the Emotiv EPOC(®) EEG gaming system for measuring research quality auditory ERPs (2013) PeerJ, 1, p. e38. , https://doi.org/10.7717/peerj.38, PMID: 23638374
• Badcock, N.A., Preece, K.A., De Wit, B., Glenn, K., Fieder, N., Thie, J., Validation of the Emotiv EPOC EEG system for research quality auditory event-related potentials in children (2015) PeerJ, 3. , https://doi.org/10.7717/peerj.907, PMID: 25922794
• Ries, A.J., Touryan, J., Vettel, J., McDowell, K., Hairston, W.D., A comparison of electroencephalography signals acquired from conventional and mobile systems (2014) J Neurosci Neuroengineering, 3, pp. 10-20. , https://doi.org/10.1166/jnsne.2014.1092
• Stopczynski, A., Stahlhut, C., Larsen, J.E., Petersen, M.K., Hansen, L.K., The smartphone brain scanner: A portable real-time neuroimaging system (2014) PLoS ONE, 9. , https://doi.org/10.1371/journal.pone.0086733, PMID: 24505263
• Stopczynski, A., Stahlhut, C., Petersen, M.K., Larsen, J.E., Jensen, C.F., Ivanova, M.G., Smartphones as pocketable labs: Visions for mobile brain imaging and neurofeedback (2014) Int J Psychophysiol, 91, pp. 54-66. , https://doi.org/10.1016/j.ijpsycho.2013.08.007, PMID: 23994206
• Ekanayake, H., P300 and Emotiv EPOC: Does Emotiv EPOC capture real EEG? Visaduma, , http://neurofeedback.visaduma.info/emotivresearch.htm, December 2010. In: Internet
• Debener, S., Minow, F., Emkes, R., Gandras, K., Vos, M., How about taking a low-cost, small, and wireless EEG for a walk? (2012) Psychophysiology, 49, pp. 1617-1621. , https://doi.org/10.1111/j.1469-8986.2012.01471.x, PMID: 23013047
• Boutani, H., Ohsuga, M., Applicability of the “Emotiv EEG Neuroheadset” as a user-friendly input interface (2013) Conf Proc. Annu Int Conf IEEE Eng Med Biol Soc IEEE Eng Med Biol Soc Annu Conf., pp. 1346-1349. , https://doi.org/10.1109/EMBC.2013.6609758
• Mayaud, L., Congedo, M., Van Laghenhove, A., Orlikowski, D., Figère, M., Azabou, E., A comparison of recording modalities of P300 event-related potentials (ERP) for brain-computer interface (BCI) paradigm (2013) Neurophysiol Clin, 43, pp. 217-227. , https://doi.org/10.1016/j.neucli.2013.06.002, PMID: 24094907
• Näpflin, M., Wildi, M., Sarnthein, J., Test-retest reliability of resting EEG spectra validates a statistical signature of persons (2017) Clin Neurophysiol, 118, pp. 2519-2524. , https://doi.org/10.1016/j.clinph.2007.07.022PMID, 17892969
• Pietto, M.L., Kamienkowski, J.E., Lipina, S.J., Electrophysiological approaches in the study of the influence of childhood poverty on cognition (2017) Neuroscience and Social Science, pp. 349-381. , Ibáñez A, Sedeño L, García AM, editors. Springer International Publishing, Cham
• Peng, C.K., Havlin, S., Stanley, H.E., Goldberger, A.L., Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series (1995) Chaos An Interdiscip J Nonlinear Sci, 5, pp. 82-87. , https://doi.org/10.1063/1.166141, PMID: 11538314
• Linkenkaer-Hansen, K., Nikouline, V.V., Palva, J.M., Ilmoniemi, R.J., Long-range temporal correlations and scaling behavior in human brain oscillations (2001) J Neurosci, 21, pp. 1370-1377. , https://doi.org/10.1002/anie.201106423, PMID: 11160408
• Peirce, J.W., PsychoPy—Psychophysics software in Python (2007) J Neurosci Methods, 162, pp. 8-13. , https://doi.org/10.1016/j.jneumeth.2006.11.017, PMID: 17254636
• Van, R.G., Drake, F.L., (2010) The Python Library Reference, pp. 1-1144. , October
• Johnstone, S.J., Pleffer, C.B., Barry, R.J., Clarke, A.R., Smith, J.L., Development of inhibitory processing during the Go/Nogo task: A behavioral and event-related potential study of children and adults (2005) J Psychophysiol, 19, pp. 11-23. , https://doi.org/10.1027/0269-8803.19.1.11
• Petersen, S.E., Posner, M.I., The attention system of the human brain: 20 years after (2012) Annu Rev Neurosci, 35, pp. 73-89. , https://doi.org/10.1146/annurev-neuro-062111-150525, PMID: 22524787
• Delorme, A., Makeig, S., EEGLAb: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis (2004) J Neurosci Methods, 134, pp. 9-21. , https://doi.org/10.1016/j.jneumeth.2003.10.009, PMID: 15102499
• Pietto, M., Parra, M.A., Trujillo, N., Flores, F., García, A.M., Bustin, J., Behavioral and electrophysiological correlates of memory binding deficits in patients at different risk levels for Alzheimer’s disease (2016) J Alzheimer’S Dis, 53, pp. 1325-1340. , https://doi.org/10.3233/JAD-160056, PMID: 27372640
• Jodo, E., Kayama, Y., Relation of a negative ERP component to response inhibition in a Go/No-go task (1992) Electroencephalogr Clin Neurophysiol, 82, pp. 477-482. , https://doi.org/10.1016/0013-4694(92)90054-L, PMID: 1375556
• Best, J.R., Miller, P.H., A developmental perspective on executive function (2010) Child Dev, 81, pp. 1641-1660. , https://doi.org/10.1111/j.1467-8624.2010.01499.x, PMID: 21077853
• Quian Quiroga, R., Obtaining single stimulus evoked potentials with wavelet denoising (2000) Phys D Nonlinear Phenom, 145, pp. 278-292. , https://doi.org/10.1016/S0167-2789(00)00116-0
• Ahmadi, M., Quian Quiroga, R., Automatic denoising of single-trial evoked potentials (2013) Neuroimage, 66, pp. 672-680. , https://doi.org/10.1016/j.neuroimage.2012.10.062, PMID: 23142653
• Navajas, J., Ahmadi, M., Quian Quiroga, R., Uncovering the mechanisms of conscious face perception: A single-trial study of the n170 responses (2013) J Neurosci, 33, pp. 1337-1343. , https://doi.org/10.1523/JNEUROSCI.1226-12.2013, PMID: 23345210
• Munro, P., Toivonen, H., Webb, G.I., Buntine, W., Orbanz, P., Teh, Y.W., Baum-Welch algorithm (2011) Encyclopedia of Machine Learning, p. 74. , Sam-mut C, Webb GI, editors. Springer US, Boston, MA
• Klimesch, W., EEG alpha and theta oscillations reflect cognitive and memory performance: A review and analysis (1999) Brain Res Rev, 29, pp. 169-195. , http://dx.doi.org/10.1016/S0165-0173(98)00056-3, PMID: 10209231
• Klimesch, W., Α-band oscillations, attention, and controlled access to stored information (2012) Trends Cogn Sci, 16, pp. 606-617. , https://doi.org/10.1016/j.tics.2012.10.007, PMID: 23141428
• Foster, J.J., Sutterer, D.W., Serences, J.T., Vogel, E.K., Awh, E., The topography of alpha-band activity tracks the content of spatial working memory (2016) J Neurophysiol, 115, pp. 168-177. , https://doi.org/10.1152/jn.00860.2015, PMID: 26467522
• Mayer, A., Schwiedrzik, C.M., Wibral, M., Singer, W., Melloni, L., Expecting to see a letter: Alpha oscillations as carriers of top-down sensory predictions (2015) Cereb Cortex, 26, pp. 3146-3160. , https://doi.org/10.1093/cercor/bhv146, PMID: 26142463

Citas:

---------- APA ----------

---------- CHICAGO ----------

---------- MLA ----------

---------- VANCOUVER ----------