Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-848d4c4894-pjpqr Total loading time: 0 Render date: 2024-06-23T18:19:10.085Z Has data issue: false hasContentIssue false

29 - Biosignal Processing in Psychophysiology: Principles and Current Developments

from General Methods

Published online by Cambridge University Press:  27 January 2017

By
Gabriele Gratton  and
Monica Fabiani
Edited by
John T. Cacioppo ,
Louis G. Tassinary  and
Gary G. Berntson
John T. Cacioppo
Affiliation:
University of Chicago
Louis G. Tassinary
Affiliation:
Texas A & M University
Gary G. Berntson
Affiliation:
Ohio State University
Get access

Summary

A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Chapter
Information
Handbook of Psychophysiology , pp. 628 - 661
Publisher: Cambridge University Press
Print publication year: 2016

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Barrett, G., Shibasaki, H., & Neshige, R. (1986). Cortical potentials preceding voluntary movement: evidence for three periods of preparation in man. Electroencephalography & Clinical Neurophysiology, 63: 327–339.Google Scholar
Basar, E., Basar-Eroglu, C., Karakas, S., & Schurmann, M. (1999). Are cognitive processes manifested in event-related gamma, alpha, theta and delta oscillations in the EEG? Neuroscience Letters, 259: 165–168.CrossRefGoogle ScholarPubMed
Ben-Shakhar, G. (1985). Standardization within individuals: a simple method to neutralize individual differences in skin conductance. Psychophysiology, 22: 292–299.Google Scholar
Bradley, M. M., Cuthbert, B. N., & Lang, P. J. (1991). Startle and emotion: lateral acoustic probes and the bilateral blink. Psychophysiology, 28: 285–295.Google Scholar
Bullmore, E. & Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10: 186–198.Google Scholar
Cacioppo, J. T. & Dorfman, D. D. (1987). Waveform moment analysis in psychophysiological research. Psychological Bulletin, 102: 421–438.Google Scholar
Catani, M. & de Schotten, M. T. (2008). A diffusion tensor imaging tractography atlas for virtual in vivo dissections. Cortex, 44: 1105–1132.CrossRefGoogle ScholarPubMed
Chauveau, N., Franceries, X., Doyon, B., Rigaud, B., Morucci, J. P., & Celsis, P. (2004). Effects of skull thickness, anisotropy, and inhomogeneity on forward EEG/ERP computations using a spherical three-dimensional resistor mesh model. Human Brain Mapping, 21: 86–97.Google Scholar
Cherry, S. R. & Phelps, M. E. (1996). Imaging brain function with positron emission tomography. In Toga, A. W. & Mazziotta, J. C. (eds.), Brain Mapping: The Methods (pp. 191–222). San Diego, CA: Academic Press.Google Scholar
Chiarelli, A. M., Maclin, E. L., Low, K. A., Fabiani, M., & Gratton, G. (2015). A comparison of procedures for coregistering scalp-recording locations to anatomical MRI images. Journal of Biomedical Optics, 20: 016009.CrossRefGoogle Scholar
Cohen, M. S. (1996). Rapid MRI and functional applications. In Toga, A. W. & Mazziotta, J. C. (eds.), Brain Mapping: The Methods (pp. 223–258). San Diego, CA: Academic Press.Google Scholar
Cook, E. W. & Miller, G. A. (1992). Digital filtering: background and tutorial for psychophysiologists. Psychophysiology, 29: 350–367.CrossRefGoogle ScholarPubMed
De Martino, F., Valente, G., Staeren, N., Ashburner, J., Goebel, R., & Formisano, E. (2008). Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns. NeuroImage, 43: 44–58.Google Scholar
Dehaene, S., Posner, M. I., & Tucker, D. M. (1994). Localization of a neural system for error detection and compensation. Psychological Science, 5: 303–305.CrossRefGoogle Scholar
Demiralp, T., Yordanova, J., Kolev, V., Ademoglu, A., Devrim, M., & Samr, V. J. (1999). Time-frequency analysis of single-sweep event-related potentials by means of fast wavelet transform. Brain and Language, 66: 129–145.Google Scholar
Donchin, E. (1969). Discriminant analysis in average evoked response studies: the study of single trial data. Electroencephalography & Clinical Neurophysiology, 27: 311–314.Google Scholar
Donchin, E. & Heffley, E. (1978). Multivariate analysis of event-related potential data: a tutorial review. In Otto, D. (ed.), Multidisciplinary Perspectives in Event-Related Brain Potential Research (EPA-600/9-77-043) (pp. 555–572). Washington, DC: US Government Printing Office.Google Scholar
Donchin, E. & Herning, R. I. (1975). A simulation study of the efficacy of stepwise discriminant analysis in the detection and comparison of event related potentials. Electroencephalography & Clinical Neurophysiology, 38: 51–68.CrossRefGoogle ScholarPubMed
Dorfman, D. D. & Cacioppo, J. T. (1990). Waveform moment analysis: topographical analysis of nonrhythmic waveforms. In Tassinary, L. G. & Cacioppo, J. T. (eds.), Principles of Psychophysiology (pp. 661–707). Cambridge University Press.Google Scholar
Elui, R. (1969). Gaussian behavior of the EEG: changes during performance of mental tasks. Science, 164: 328–331.Google Scholar
Estes, W. K. (1956). The problem of inference from curves based on group data. Psychological Bulletin, 53: 133–140.Google Scholar
Evans, A. C., Collins, D. L., Mills, S. R., Brown, E. D., Kelly, R. L., & Peters, T. M. (1993). 3D statistical neuroanatomical models from 305 MRI volumes. In Proceedings of the IEEE Nuclear Science Symposium and Medical Imaging Conference (pp. 1813–1817). Piscataway, NJ: IEEE.Google Scholar
Evans, A. C., Marrett, S., Neelin, P., Collins, L., Worsley, K., Dai, W., … & Bub, D. (1992). Anatomical mapping of functional activation in stereotactic coordinate space. NeuroImage, 1: 43–53.CrossRefGoogle ScholarPubMed
Fabiani, M., Gordon, B. A., Maclin, E. L., Pearson, M., Brumback, C. R., Low, K. A., … & Gratton, G. (2014). Neurovascular coupling in normal aging: a combined optical, ERP and fMRI study. NeuroImage, 1: 592–607.CrossRefGoogle Scholar
Fabiani, M., Gratton, G., Corballis, P., Cheng, J., & Friedman, D. (1998). Bootstrap assessment of the reliability of maxima in surface maps of brain activity of individual subjects derived with electrophysiological and optical methods. Behavior Research Methods, Instruments, & Computers, 30: 78–86.Google Scholar
Fabiani, M., Gratton, G., Karis, D., & Donchin, E. (1987). Definition, identification, and reliability of measurement of the P300 component of the event-related brain potential. In Ackles, P. K., Jennings, J. R., & Coles, M. G. (eds.), Advances in Psychophysiology, vol. 2 (pp. 1–78). Greenwich, CT: JAI Press.Google Scholar
Farwell, L. A., Martinerie, J. M., Bashore, T. R., Rapp, P. E., & Goddard, P. H. (1993). Optimal digital filters for long-latency components of the event-related brain potential. Psychophysiology, 30: 306–315.Google Scholar
Fischl, B. (2012). FreeSurfer. NeuroImage, 62: 774–781.CrossRefGoogle ScholarPubMed
Fortgens, C. & de Bruin, M. P. (1983). Removal of eye movement and ECG artifacts from the non-cephalic reference EEG. Electroencephalography & Clinical Neurophysiology, 56: 90–96.Google Scholar
Fox, P. T. & Raichle, M. E. (1984). Stimulus rate dependence of regional cerebral blood flow in human striate cortex, demonstrated by positron emission tomography. Journal of Neurophysiology, 51: 1109–1120.Google Scholar
Friston, K. J. (1996). Statistical parametric mapping and other analyses of functional imaging data. In Toga, A. W. & Mazziotta, J. C. (eds.), Brain Mapping: The Methods (pp. 363–388). San Diego, CA: Academic Press.Google Scholar
Friston, K. J. (2011). Functional and effective connectivity: a review. Brain Connectivity, 1: 13–36.CrossRefGoogle ScholarPubMed
Gordon, B. A., Tse, C.-H., Gratton, G., & Fabiani, M. (2014). Spread of activation and spread of inhibition: does age matter? Frontiers in Aging Neuroscience, 6: 288.Google Scholar
Gratton, G. (1997). Attention and probability effects in the human occipital cortex: an optical imaging study. NeuroReport, 8: 1749–1753.Google Scholar
Gratton, G., Coles, M. G. H., & Donchin, E. (1983). A new method for offline removal of ocular artifact. Electroencephalography & Clinical Neurophysiology, 55: 468–484.Google Scholar
Gratton, G., Coles, M. G., & Donchin, E. (1989a). A procedure for using multi-electrode information in the analysis of components of the event-related potential: vector filter. Psychophysiology, 26: 222–232.CrossRefGoogle ScholarPubMed
Gratton, G., Kramer, A. F., Coles, M. G., & Donchin, E. (1989b). Simulation studies of latency measures of components of the event-related brain potential. Psychophysiology, 26: 233–248.Google Scholar
Gratton, C., Sreenivasan, K. K., Silver, M. A., & D’Esposito, M. (2013). Attention selectively modifies the representation of individual faces in the human brain. Journal of Neuroscience, 33: 6979–6989.Google Scholar
Hackley, S. A. & Johnson, L. N. (1996). Distinct early and late subcomponents of the photic blink reflex: response characteristics in patients with retrogeniculate lesions. Psychophysiology, 33: 239–251.CrossRefGoogle ScholarPubMed
Herrmann, C. S., Rach, S., Vosskuhl, J., & Strüber, D. (2014). Time-frequency analysis of event-related potentials: a brief tutorial. Brain Topography, 27: 438–450.Google Scholar
Himberg, J., Hyvarinen, A., & Esposito, F. (2004). Validating the independent components of neuroimaging time series via clustering and visualization. NeuroImage, 22: 1214–1222.Google Scholar
Hubel, D. H. & Wiesel, T. N. (1968). Receptive fields and functional architecture of monkey striate cortex. Journal of Physiology, 195: 215–243.Google Scholar
Huberty, C. J. & Morris, J. D. (1989). Multivariate analysis versus multiple univariate analyses. Psychological Bulletin, 105: 302–308.Google Scholar
Jennings, J. R., Kamarck, T., Stewart, C., Eddy, M., & Johnson, P. (1992). Alternate cardiovascular baseline assessment techniques: vanilla or resting baseline. Psychophysiology, 29: 742–750.CrossRefGoogle ScholarPubMed
Jennings, J. R., van der Molen, M. W., Somsen, R. J., & Ridderinkhof, K. R. (1991). Graphical and statistical techniques for cardiac cycle time (phase) dependent changes in interbeat interval. Psychophysiology, 28: 596–606.Google Scholar
Jennings, J. R. & Wood, C. C. (1976). Letter. The epsilon-adjustment procedure for repeated-measures analyses of variance. Psychophysiology, 13: 277–278.Google Scholar
Jung, T.-P., Makeig, S., Westerfield, M., Townsend, J., Courchesne, E., & Sejnowski, T. J. (2000). Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects. Clinical Neurophysiology, 111: 1745–1758.CrossRefGoogle ScholarPubMed
Karis, D., Fabiani, M., & Donchin, E. (1984). “P300” and memory: individual differences in the von Restorff effect. Cognitive Psychology, 16: 177–216.Google Scholar
Karniski, W., Blair, R. C., & Snider, A. D. (1994). An exact statistical method for comparing topographic maps, with any number of subjects and electrodes. Brain Topography, 6: 203–210.Google Scholar
Kennedy, J. J. (1983). Analyzing Qualitative Data: Introductory Log-Linear Analysis for Behavioral Research. New York: Praeger.Google Scholar
Lacey, J. I., Kagan, J., Lacey, B. C., & Moss, H. A. (1963). The visceral level: situational determinants and behavioral correlates of autonomic response patterns. In Knapp, P. H. (ed.), Expression of the Emotions in Man (pp. 161–196). New York: International Universities Press.Google Scholar
Lachaux, J.-P., Lutz, A., Rudrauf, D., Cosmelli, D., Le Van Quyen, M., Martinerie, J., & Varela, F. J. (2002). Estimating the time-course of coherence between single-trial brain signal: an introduction to wavelet coherence. Clinical Neurophysiology, 32: 157–174.Google Scholar
Lachaux, J.-P., Rodriguez, E., Martinerie, J., & Varela, F. J. (1999). Measuring phase synchrony in brain signals. Human Brain Mapping, 8: 194–208.Google Scholar
Lamothe, R. & Stroink, G. (1991). Orthogonal expansions: their applicability to signal extraction in electrophysiological mapping data. Medical & Biological Engineering & Computing, 29: 522–528.Google Scholar
Le Bihan, D., Mangin, J.-F., Poupon, C., Clark, C. A., Pappata, S., Molko, N., & Chabriat, H. (2001). Diffusion tensor imaging: concepts and applications. Journal of Magnetic Resonance Imaging, 13: 534–546.CrossRefGoogle ScholarPubMed
Maier, J., Dagnelie, G., Spekreijse, H., & van Dijk, B. W. (1987). Principal components analysis for source localization of VEPs in man. Vision Research, 27: 165–177.CrossRefGoogle ScholarPubMed
Makeig, S., Jung, T. P., Bell, A. J., Ghahremani, D., & Sejnowski, T. (1997). Blind separation of auditory event-related brain responses into independent components. Proceedings of the National Academy of Sciences of the USA, 94: 10979–10984.Google Scholar
Makeig, S., Westerfield, M., Jung, T.-P., Enghoff, S., Townsend, J., Courchesne, E., & Sejnowski, T. J. (2002). Dynamic brain sources of visual evoked responses. Science, 295: 690–694.Google Scholar
Mathewson, K., Beck, D., Ro, T., Maclin, E. L., Low, K. A., Fabiani, M., & Gratton, G. (2014). Dynamics of alpha control: fronto-parietal modulators of preparatory alpha oscillations revealed with combined EEG and event-related optical signals (EROS). Journal of Cognitive Neuroscience, 26: 2400–2415.CrossRefGoogle Scholar
Mathewson, K., Gratton, G., Fabiani, M., Beck, D., & Ro, A. (2009). To see or not to see: pre-stimulus alpha phase predicts visual awareness. Journal of Neuroscience, 29: 2725–2732.Google Scholar
Mattout, J., Phillip, C., Penny, W. D., Rugg, M. D., & Friston, K. J. (2006). MEG source localization under multiple constraints: an extended Bayesian framework. NeuroImage, 30: 753–767.Google Scholar
McCallum, W. C. & Curry, S. H. (1984). A comparison of early event-related potentials in two target detection tasks. Annals of the New York Academy of Sciences, 425: 242–249.Google Scholar
McCarthy, G. & Wood, C. C. (1985). Scalp distributions of event-related potentials: an ambiguity associated with analysis of variance models. Electroencephalography & Clinical Neurophysiology, 62: 203–208.Google Scholar
Miller, J., Patterson, T., & Ulrich, R. (1998). Jackknife-based method for measuring LRP onset latency differences. Psychophysiology, 35: 99–115.Google Scholar
Möcks, J. (1986). The influence of latency jitter in principal component analysis of event-related potentials. Psychophysiology, 23: 480–484.Google Scholar
Möcks, J. (1988). Decomposing event-related potentials: a new topographic components model. Biological Psychology, 26: 199–215.Google Scholar
Möcks, J., Köhler, W., Gasser, T., & Pham, D. T. (1988). Novel approaches to the problem of latency jitter. Psychophysiology, 25: 217–226.Google Scholar
Möcks, J. & Verleger, R. (1985). Nuisance sources of variance in principal components analysis of event-related potentials. Psychophysiology, 22: 674–688.Google Scholar
Monk, T. H. (1987). Parameters of the circadian temperature rhythm using sparse and irregular sampling. Psychophysiology, 24: 236–242.Google Scholar
Monk, T. H. & Fookson, J. E. (1986). Circadian temperature rhythm power spectra: is equal sampling necessary? Psychophysiology, 23: 472–479.Google Scholar
Nitsche, M. A. & Paulus, W. (2000). Excitability changes induced in the human motor cortex by weak transcranial direct current stimulation. Journal of Physiology, 527: 633–639.Google Scholar
Norman, K. A., Polyn, S. M., Detre, G. J., & Haxby, J. V. (2006). Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends in Cognitive Sciences, 10: 424–430.CrossRefGoogle ScholarPubMed
Parks, N. A., Maclin, E. L., Low, K. A., Beck, D. M., Fabiani, M., & Gratton, G. (2012). Examining cortical dynamics and connectivity with concurrent simultaneous single-pulse transcranial magnetic stimulation and fast optical imaging. NeuroImage, 59: 2504–2510.Google Scholar
Pascual-Leone, A., Valls-Sole, J., Wassermann, E. M., & Hallett, M. (1994). Responses to rapid-rate transcranial magnetic stimulation of the human motor cortex. Brain, 117: 847–858.Google Scholar
Pascual-Marqui, R. D., Esslen, M., Kochi, K., & Lehmann, D. (2002). Functional imaging with low resolution brain electromagnetic tomography (LORETA): review, new comparisons, and new validation. Japanese Journal of Clinical Neurophysiology, 30: 81–94.Google Scholar
Pascual-Marqui, R. D., Michel, C. M., & Lehmann, D. (1994). Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain. International Journal of Psychophysiology, 18: 49–65.Google Scholar
Pereira, F., Mitchell, T., & Botvinick, M. (2009). Machine learning classifiers and fMRI: a tutorial review. NeuroImage, 45: S199–S209.CrossRefGoogle Scholar
Perrin, F., Pernier, J., Bertrand, O., Giard, M. H., & Echallier, J. F. (1987). Mapping of scalp potentials by surface spline interpolation. Electroencephalography & Clinical Neurophysiology, 66: 75–81.Google Scholar
Pfurtscheller, G. & Neuper, C. (1992). Simultaneous EEG 10 Hz desynchronization and 40 Hz synchronization during finger movements. NeuroReport, 3: 1057–1060.Google Scholar
Power, J. D., Cohen, A. L., Nelson, S. M., Wig, G. S., Barnes, K. A., Church, J. A., … & Petersen, S. E. (2011). Functional organization of the human brain. Neuron, 72: 665–678.Google Scholar
Quigley, K. S. & Berntson, G. G. (1996). Autonomic interactions and chronotropic control of the heart: heart period versus heart rate. Psychophysiology, 33: 605–611.Google Scholar
Roach, B. J. & Mathalon, D. H. (2008). Event-related EEG time-frequency analysis: an overview of measures and an analysis of early gamma band phase locking in schizophrenia. Schizophrenia Bulletin, 34: 907–926.Google Scholar
Ruchkin, D. S., Sutton, S., & Stega, M. (1980). Emitted P300 and slow wave event-related potentials in guessing and detection tasks. Electroencephalography & Clinical Neurophysiology, 49: 1–14.Google Scholar
Rykhlevskaia, E., Fabiani, M., & Gratton, G. (2006). Lagged covariance structure models for studying functional connectivity in the brain. NeuroImage, 30: 1203–1218.Google Scholar
Rykhlevskaia, E., Fabiani, M., & Gratton, G. (2008). Combining structural and functional neuroimaging data for studying brain connectivity: a review. Psychophysiology, 45: 173–187.Google Scholar
Samar, V. J., Bopardikar, A., Rao, R., & Swartz, K. (1999). Wavelet analysis of neuroelectric waveforms: a conceptual tutorial. Brain and Language, 66: 7–60.Google Scholar
Scherg, M. & von Cramon, D. (1986). Evoked dipole source potentials of the human auditory cortex. Electroencephalography & Clinical Neurophysiology, 65: 344–360.Google Scholar
Serences, J. T., Saproo, S., Scolari, M., Ho, T., & Muftuler, T., (2009). Estimating the influence of attention on population codes in human visual cortex using voxel-based tuning functions. NeuroImage, 44: 223–231.Google Scholar
Siegel, M., Engel, A. K., & Donner, T. H. (2011). Cortical network dynamics of perceptual decision-making in the human brain. Frontiers in Human Neuroscience, 5: 00021.Google Scholar
Siegler, R. S. (1987). The perils of averaging data over strategies: an example from children’s addition. Journal of Experimental Psychology: General, 116: 250–264.Google Scholar
Skrandies, W. & Lehmann, D. (1982). Spatial principal components of multichannel maps evoked by lateral visual half-field stimuli. Electroencephalography & Clinical Neurophysiology, 54: 662–667.CrossRefGoogle ScholarPubMed
Smulders, F. T., Kenemans, J. L., & Kok, A. (1996). Effects of task variables on measures of the mean onset latency of LRP depend on the scoring method. Psychophysiology, 33: 194–205.Google Scholar
Spencer, K. M., Dien, J., & Donchin, E. (1997). Temporal-spatial analysis of the late positive components of the ERP. Psychophysiology, 34: S6.Google Scholar
Spencer, K. M., Dien, J., & Donchin, E. (1999). Componential analysis of the ERP elicited by novel events using a dense electrode array. Psychophysiology, 36: 409–414.Google Scholar
Squires, K. C. & Donchin, E. (1976). Beyond averaging: the use of discriminant functions to recognize event related potentials elicited by single auditory stimuli. Electroencephalography & Clinical Neurophysiology, 41: 449–459.Google Scholar
Squires, N. K., Squires, K. C., & Hillyard, S. A. (1975). Two varieties of long-latency positive waves evoked by unpredictable auditory stimuli in man. Electroencephalography & Clinical Neurophysiology, 38: 387–401.Google Scholar
Srinivasan, R., Nunez, P. L., Tucker, D. M., Silberstein, R. B., & Cadusch, P. J. (1996). Spatial sampling and filtering of EEG with spline laplacians to estimate cortical potentials. Brain Topography, 8: 355–366.Google Scholar
Steinmetz, H., Furst, G., & Meyer, B.-U. (1989). Craniocerebral topography within the international 10–20 system. Electroencephalography & Clinical Neurophysiology, 72: 499–506.Google Scholar
Stemmler, G. (1987). Standardization within subjects: a critique of Ben-Shakhar’s conclusions. Psychophysiology, 24: 243–246.Google Scholar
Stiber, B. Z. & Sato, S. (1997). Visualization of EEG using time-frequency distributions. Methods of Information in Medicine, 36: 298–301.Google Scholar
Talairach, J. & Tournoux, P. (1988). Co-Planar Stereotactic Atlas of the Human Brain: 3-Dimensional Proportional System: An Approach to Cerebral Imaging. Stuttgart: Thieme.Google Scholar
Tallon-Buadry, C., Bertrand, O., Delpuech, C., & Pernier, J. (1996). Stimulus specificity of phase-locked and non-phase-locked 40 Hz visual responses in human. Journal of Neuroscience, 16: 4240–4249.Google Scholar
Tomoda, H., Celesia, G. G., & Toleikis, S. C. (1991). Effect of spatial frequency on simultaneous recorded steady-state pattern electroretinograms and visual evoked potentials. Electroencephalography & Clinical Neurophysiology: Evoked Potentials, 80: 81–88.CrossRefGoogle ScholarPubMed
Van Essen, D. C., Drury, H. A., Joshi, S., & Miller, M. I. (1998). Functional and structural mapping of human cerebral cortex: solutions are in the surfaces. Proceedings of the National Academy of Sciences of the USA, 95: 788–795.Google Scholar
Vasey, M. W. & Thayer, J. F. (1987). The continuing problem of false positives in repeated measures ANOVA in psychophysiology: a multivariate solution. Psychophysiology, 24: 479–486.Google Scholar
Wainer, H. (1991) Adjusting for differential base rates: Lord’s Paradox again. Psychological Bulletin, 109: 147–151.Google Scholar
Walter, W. G., Cooper, R., Aldridge, V. J., McCallum, W. C., & Winter, A. L. (1964). Contingent negative variation: an electrical sign of sensorimotor association and expectancy in the human brain. Nature, 203: 380–384.Google Scholar
Wang, J. Z., Williamson, S. J., & Kaufman, L. (1992). Magnetic source images determined by a lead-field analysis: the unique minimum-norm least-squares estimation. IEEE Transactions on Biomedical Engineering, 39: 665–675.Google Scholar
Wasserman, S. & Bockenholt, U. (1989). Bootstrapping: applications to psychophysiology. Psychophysiology, 26: 208–221.Google Scholar
Wickens, C. D., Kramer, A. F., Vanasse, L., & Donchin, E. (1983). Performance of concurrent tasks: a psychophysiological analysis of the reciprocity of information-processing resources. Science, 221: 1080–1082.Google Scholar
Wiener, N. (1964). Extrapolation, Interpolation, and Smoothing of Stationary Time Series. Cambridge, MA: MIT Press.Google Scholar
Wilder, J. (1967). Stimulus and Response: The Law of Initial Value. Bristol: John Wright & Sons.Google Scholar
Wood, C. C. & McCarthy, G. (1984). Principal component analysis of event-related potentials: simulation studies demonstrate misallocation of variance across components. Electroencephalography & Clinical Neurophysiology, 59: 249–260.Google Scholar
Woody, C. D. (1967). Characterization of an adaptive filter for the analysis of variable latency neuroelectrical signal. Medical and Biological Engineering, 5: 539–553.Google Scholar
Yantis, S., Meyer, D. E., & Smith, J. K. (1991). Analyses of multinomial mixture distributions: new tests for stochastic models of cognition and action. Psychological Bulletin, 110: 350–374.Google Scholar
Yule, G. U. (1927). On a method of investigating periodicities in disturbed series, with special reference to Wolfer’s sunspot numbers. Philosophical Transactions of the Royal Society of London, Series A, 226: 267–298.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×