Functional imaging with low resolution brain electromagnetic tomography (LORETA): a review


R.D. Pascual-Marqui, M. Esslen, K. Kochi, D. Lehmann


The KEY Institute for Brain-Mind Research, University Hospital of Psychiatry, Zurich, Switzerland



Corresponding author:

Roberto D. Pascual-Marqui

The KEY Institute for Brain-Mind Research

University Hospital of Psychiatry

Lenggstr. 31, CH-8029 Zurich, Switzerland

Tel.:+41-1-3884934 ; Fax:+41-1-3803043

e-mail: ;



Running title: LORETA


Keywords: functional imaging, LORETA, electric neuronal activity, brain mapping, source localization.



This paper reviews several recent publications that have successfully used the functional brain imaging method known as LORETA. Emphasis is placed on the electrophysiological and neuroanatomical basis of the method, on the localization properties of the method, and on the validation of the method in real experimental human data. Papers that criticize LORETA are briefly discussed. LORETA publications in the 1994-1997 period based localization inference on images of raw electric neuronal activity. In 1998 a series of papers appeared that based localization inference on the statistical parametric mapping methodology applied to high time resolution LORETA images. Starting in 1999, quantitative neuroanatomy was added to the methodology, based on the digitized Talairach atlas provided by the Brain Imaging Centre, Montreal Neurological Institute. The combination of these methodological developments has placed LORETA at a level that compares favorably to the more classical functional imaging methods, such as PET and fMRI.





Currently, the most often used methods for functional imaging of the human brain are positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) (Toga and Mazziotta 1996). These tomographies provide three-dimensional (3D) images comprising information on metabolism. Although the spatial resolution of these images is indeed excellent, the temporal resolution is not high enough to keep up with the speed at which neuronal processes occur. For instance, in a fundamental study by Logothetis et al. (2001), it was shown that the time course of the fMRI haemodynamic response was roughly a low pass filtered (i.e., low time resolution) version of the electric neuronal activity.


More recently, a growing number of studies have been published that make use of functional imaging methods based on the electroencephalogram (EEG) and the magnetoencephalogram (MEG). For a recent extensive review of electromagnetic imaging methods, see Baillet et al. (2001).


It may seem paradoxical that although the human EEG was first reported in 1929 (Berger), the recently developed PET and fMRI methods have preceded the use of electromagnetic tomographies. This is due to a fundamental limitation of extracranial EEG/MEG measurements: they do not contain sufficient information on the three-dimensional (3D) distribution of electric neuronal activity. Over 140 years ago, Helmholtz (1853) reported the general non-uniqueness of the solution to this type of electromagnetic inverse problem. It implies that EEG/MEG measurements (even with an infinite number of sensors) can be explained by many different distributions of generators. Naturally, one may ask: which solution corresponds to reality? The answer, in general, is that it cannot be determined.


The curse of non-uniqueness (Pascual-Marqui and Biscay-Lirio 1993) may therefore seem to render hopeless the task of developing an electromagnetic tomography. Fortunately, this is not the case. The EEG and the MEG are not due to capricious distributions of electric neuronal generators. Rather, they obey certain electrophysiological and neuroanatomical constraints, that when plugged into the laws of electrodynamics, offer at least an approximate solution to the inverse problem.


It is now widely accepted that extracranial measurements of EEG and MEG are generated by cortical pyramidal neurons undergoing post-synaptic potentials (PSPs). These neurons are oriented perpendicular to the cortical surface. The magnitude of experimentally recorded extracranial signals, at any given time instant, is due to the spatial summation of the impressed current density induced by highly synchronized PSPs occurring in large clusters of neurons. According to calculations reviewed by Hämäläinen et al. (1993), a typical cluster size must cover at least 40 to 200 mm2 of cortical surface.


All these facts are reviewed in general in (Martin 1991, Dale et al. 2000, Baillet et al. 2001). In addition, independent experimental evidence demonstrating high synchronization of neighboring neurons can be found in (Llinas 1988, Haalman and Vaadia 1997, Sukov and Barth, 1998).


LORETA: neuroanatomy and electrophysiology


Low resolution brain electromagnetic tomography (LORETA) (Pascual-Marqui et al. 1994, Pascual-Marqui 1999) is a functional imaging method based on the electrophysiological and neuroanatomical constraints previously described. For instance, the cortex can be modeled as a collection of volume elements (voxels) in the digitized Talairach atlas provided by the Brain Imaging Center, Montreal Neurological Institute. In this case, the LORETA inverse solution (which is consistent with the EEG/MEG measurements) corresponds to the 3D distribution of electric neuronal activity that has maximum similarity (i.e., maximum synchronization), in terms of orientation and strength, between neighboring neuronal populations (represented by adjacent voxels). In another example, the cortical surface can be modeled as a collection of surface elements with known orientation. LORETA can accommodate this neuroanatomical constraint, and find the inverse solution that maximizes only the synchronization of strength between neighboring neuronal populations.


LORETA: localization error


The consistency of LORETA with physiology is not the only reason for favoring it above so many other published inverse solutions. The most important criterion for choosing a neuroimaging method is that it must be capable of correct localization, since this is the purpose of functional mapping. For instance, consider a method that views the planet earth from afar, and then produces a map that localizes Gaudi’s “La Sagrada Familia” in Cuba. Such a method is worthless, as compared to a method that is capable of localizing it to within 100 Km of its actual position.


In a previous review paper that compared all published linear, distributed inverse solutions (Pascual-Marqui 1999), it was shown that only LORETA was capable of correct localization (to within 1 voxel resolution in the average), whereas all other methods were especially incapable of localizing deep sources. Independent validation of the localization properties of LORETA has been replicated by Yao and He (2001) and by Phillips et al. (2002a, 2002b).


LORETA: experimental validation


An additional essential criterion for choosing an inverse solution is its validation with experimental data under conditions where the sources are known a priori. For instance, inverse methods can be tested with event related potentials (ERPs) obtained under visual or auditory stimulation.


The empirical validity of LORETA has been established under diverse physiological conditions. Lavric et al. (2001a, 2001b) describe LORETA activation of language areas in an ERP study comparing cognitive mechanisms of regular and irregular past-tense production. Waberski et al. (2001) find LORETA activation of the auditory cortex in a mismatch-negativity (MMN) experiment. In a P300 experiment comparing normal subjects with schizophrenic patients, Winterer et al. (2001) found P300 LORETA activation in most of the areas reported by independent studies that used intracortical recordings. In a visual ERP study under hemifield stimulation, Steger et al. (2001) found LORETA activation transfer from contra- (P1a) to ipsilateral (P1b) visual cortices.


Other ERP-type studies providing validation for LORETA, and that also report new findings in cognitive processing, are:

1. Using visual stimulation and finding activation of visual cortices: Khateb et al. (2000, 2001), Hirota et al. (2001), Van Leeuwen et al. (1998), Strik et al. (1998), Pegna et al. (1997).

2. Using auditory stimulation and finding activation of auditory cortices: Mulert et al. (2001), Anderer et al. (1998a, 1998b).

3. Using motor and visuo-motor tasks and finding activation of visual and motor cortices: Thut et al. (2000, 1999).

4. Using visual stimulation with faces and finding activation of face processing cortices: Pizzagalli et al. (2000).


LORETA has also been validated in the analysis of epilepsy-related data:

1. Worrell et al. (2000) localized epileptic foci in patients with MRI lesions.

2. Seeck et al. (1998) found the generators of epileptogenic discharges confirmed by fMRI and subdural recordings.

3. Lantz et al. (1997) found activation of interictal epileptiform activity confirmed with intracranial recordings.


LORETA can also be used to find the generators of EEG frequency components. A detailed description of the methods used in this approach can be found in (Frei et al. 2001, Gomez and Thatcher 2001, Pascual-Marqui et al. 1999). Several studies that provide validation for EEG-based LORETA analysis are the following:

1. In agreement with independent PET studies that implicated the rostral anterior cingulate in depression, Pizzagalli et al. (2001) found that the theta frequency band generator in the same region is a predictor for treatment response in depression.

2. Anderer et al. (2000) found that buspirone-induced activation of EEG generators is in agreement with the localization reported in independent PET studies.

3. Dierks et al. (2000) found correlation between the localization of LORETA EEG generators and PET images in Alzheimer's disease.


Other studies that use LORETA and report new findings are:

1. Frei et al. (2001) and Gamma et al. (2000): drug (MDMA) effect study on EEG generators.

2. Connemann et al. (2001): case study of alpha-delta sleep generators in different sleep stages.

3. Jausovec and Jausovec (2001): P300 generators related to IQ.

4. Isotani et al. (2001): EEG generators of hypnotically induced anxiety and relaxation.

5. Prabhu et al. (2001): P300 generators in female alcoholics.

6. Koles et al. (2001): EEG generators during verbal and spatial cognitive tasks.

7. Kounios et al. (2001): evidence demonstrating different neural substrates for the encoding of fusion and juxtaposition concept associations.

8. Anderer et al. 2001: evidence for two distinct sleep spindle generators, one in prefrontal cortex (Brodmann areas 9 and 10) oscillating with a frequency below 13 Hz, and the other in precuneus (Brodmann area 7) oscillating with a frequency above 13 Hz.

9. Pascual-Marqui et al. (1999): comparison of EEG generators of schizophrenic patients with normal subjects.

10. Wang et al. (1999): generators involved in selective attention based on forms defined by motion.

11. Brandeis et al. (1998): evidence showing that “stop” failures in children with attention deficits occur during posterior activation, which may be related to the orienting of attention, preceding and partly determining inhibitory control problems in ADD.

12. Anderer et al. (1998c): drug effect study on P300 generators in age-associated memory impairment.


In some applications, when the actual generator is known to be very well approximated by an active point (i.e., the single dipole), LORETA images might be too blurred, and dipole fitting methods or non-linear tomographies would certainly be preferred (Leder et al. 2001, Fuchs et al. 1999).


LORETA: some criticisms to the method


Other studies have criticized LORETA:

1. In the opinion of Menendez and Andino (2000), the localization property of LORETA, shared by no other linear, distributed inverse solution, is of no value in source localization. In addition, these authors show that for some test sources, LORETA has a localization error of two or three voxels, a fact that was already reported, and not omitted, in (Pascual-Marqui 1995, 1999).

2. In the opinion of Kincses et al. (1999), the electrophysiological and neuroanatomical constraints used by LORETA are arbitrary, and have no physiological meaning.

3. In a simulation experiment that makes use of a capricious source distribution, Michel et al. (1999) apply LORETA with the purpose of demonstrating that it cannot localize correctly.

4. Based on a theoretical lemma, De Peralta-Menendez and Gonzalez-Andino (1998) state that LORETA is incapable of localizing sources on the boundary of the solution space. However, Pascual-Marqui (1999) demonstrated the falsehood of the statement, and demonstrated that those authors had been systematically criticizing LORETA based on an incorrectly programmed algorithm of their own making.


LORETA: more recent papers


Very recently, a number of LORETA publications have appeared that further make use of the method, and in many instances, provide validation:

1.               Lehmann et al. (2001) study the LORETA generators of EEG gamma frequency during meditation.

2.               Thayer et al. (2001) used LORETA to study the mechanisms of mental rotation of human hands.

3.               Berg et al. (2001) use LORETA in a Go/NoGo task to compare patients with writer's cramp and control subjects.

4.               Bokura et al. (2001) use LORETA in a Go/NoGo task.

5.               Carretié et al. (2001) use ERP LORETA to study emotion and attention interaction.

6.               Szelenberger and Niemcewicz (2001) study event-related LORETA in primary insomnia.

7.               Schairer et al. (2001) study LORETA images of mismatch negativity.

8.               Saletu et al. (2002) study drug effects on EEG and auditory ERPs generators.

9.               Pizzagalli et al (2002) compare EEG based generator distributions of control and depressed subjects.

10.           Khateb et al. (2002) use ERP LORETA to study the dynamics of brain activation during explicit word and image recognition tasks.

11.           Hamm et al (2002) use ERP LORETA to compare the N300 and N400 generators related to picture stimuli in congruent and incongruent contexts.

12.           Mulert et al. (2002) correlate LORETA activity with loudness of auditory stimuli for the N1/P2 component, and are able to predict treatment response in major depression.

13.           Coatanhay et al. (2002) study EEG frequency domain generators during sleep.

14.           Brandeis et al. (2002) study the P3b generators in hyperkinetic children.

15.           Vitacco et al. (2002) perform a direct comparison of LORETA and fMRI during a language processing task. Horwitz and Poeppel (2002) write a very encouraging editorial comment on this work.

16.           Park et al. (2002) validate LORETA with high density EEG, using single-subject-MRIs, and applying the techniques of statistical parametric mapping.

17.           Fallgatter et al. (2002), using LORETA, confirm the implication of the anterior cingulate in conflict monitoring and allocation of attention.

18.           Pizzagalli et al. (2002), using LORETA found that in the fusiform gyri, at around 160 ms post-stimulus, liked faces elicited stronger activation than disliked and neutral faces and checkerboard-reversal stimuli.

19.           Gallinat et al (2002) found with LORETA frontal and temporal dysfunction of auditory stimulus processing in schizophrenia.

20.           Anderer et al. (2002) study the LORETA localization of P300 generators in normal aging and age-associated memory impairment. They obtain with LORETA all P300 generators reported by independent depth electrode studies, except for hippocampal generators which are excluded from the LORETA solution space.


And what about the future?


A new imaging method: sLORETA (standardized low resolution brain electromagnetic tomography)


In an accompanying paper in this issue (Pascual-Marqui, 2002), the new imaging method sLORETA is fully described. sLORETA yields images of standardized current density with zero localization error. The accuracy of these results cannot be improved upon. No other instantaneous, distributed, discrete, imaging method for EEG/MEG has been published (to the best of the authors’ knowledge) that achieved perfect localization. All other previously published methods at best produced systematic non-zero localization errors. Experimental validation for sLORETA was demonstrated with visual ERPs to stimulation with pictures of human faces (Pascual-Marqui et al. 2002).


In the very near future, we expect to release a free software package implementing this new method.





1)            Anderer P, Klosch G, Gruber G, Trenker E, Pascual-Marqui RD, Zeitlhofer J, Barbanoj MJ, Rappelsberger P, Saletu B: Low-resolution brain electromagnetic tomography revealed simultaneously active frontal and parietal sleep spindle sources in the human cortex. Neuroscience 103: 581-592, 2001.

2)            Anderer P, Pascual-Marqui RD, Semlitsch HV, Saletu B: Electrical sources of P300 event-related brain potentials revealed by low resolution electromagnetic tomography .1. Effects of normal aging. Neuropsychobiology 37: 20-27, 1998a.

3)            Anderer P, Pascual-Marqui RD, Semlitsch HV, Saletu B: Differential effects of normal aging on sources of standard N1, target N1 and target P300 auditory event-related brain potentials revealed by low resolution electromagnetic tomography (LORETA). Evoked Potentials-Electroencephalography and Clinical Neurophysiology 108: 160-174, 1998b.

4)            Anderer P, Saletu B, Pascual-Marqui RD: Effect of the 5-HT1a partial agonist buspirone on regional brain electrical activity in man: a functional neuroimaging study using low-resolution electromagnetic tomography (LORETA). Psychiatry Research-Neuroimaging 100: 81-96, 2000.

5)            Anderer P, Saletu B, Semlitsch HV, Pascual-Marqui RD: Electrical sources of P300 event-related brain potentials revealed by low resolution electromagnetic tomography .2. Effects of nootropic therapy in age-associated memory impairment. Neuropsychobiology 37: 28-35, 1998c.

6)            Anderer P, Saletu B, Semlitsch HV, Pascual-Marqui RD. Non-invasive localization of P300 sources in normal aging and age-associated memory impairment. Neurobiology of Aging, 2002, in press.

7)            Baillet S, Mosher JC, Leahy RM: Electromagnetic Brain Mapping. IEEE Signal Processing Magazine 18:14-30, 2001.

8)            Berg D, Herrmann MJ, Müller TJ, Strik WK, Aranda D, Koenig T, Naumann M, Fallgatter AJ. Cognitive response control in writer's cramp. European Journal of Neurology 2001, 8:587-594.

9)            Berger H: Über das Elektroencephalogramm des Menschen. Archiv für Psychiatrie und Nervenkrankheit 87: 527-570, 1929.

10)        Bokura H, Yamaguchi S, Kobayashi S. Electrophysiological correlates for response inhibition in a Go/NoGo task. Clinical Neurophysiology, 2001, 112:2224-2232.

11)        Brandeis D, Van Leeuwen TH, Rubia K, Vitacco D, Steger J, Pascual-Marqui RD, Steinhausen HC: Neuroelectric mapping reveals precursor of stop failures in children with attention deficits. Behavioural Brain Research 94: 111-125, 1998.

12)        Brandeis D, Banaschewski T, Baving L, Georgiewa P, Blanz B, Schmidt MH, Warnke A, Steinhausen HC, Rothenberger A, Scheuerpflug P. Multicenter P300 brain mapping of impaired attention to cues in hyperkinetic children. J. Am. Acad. Child Adolesc. Psychiatry, 2002, 41:990-998.

13)        Carretié L, Martín-Loeches M, Hinojosa JA, Mercado F. Emotion and attention interaction studied through event-related potentials. Journal of Cognitive Neuroscience 2001, 13:1109-1128.

14)        Coatanhay A, Soufflet L, Staner L, Boeijinga P. EEG source identification: frequency analysis during sleep. C. R. Biologies, 2002, 325:273-282.

15)        Connemann BJ, Mann K, Pascual-Marqui RD, Roschke J: Limbic activity in slow wave sleep in a healthy subject with alpha-delta sleep. Psychiatry Research-Neuroimaging 107: 165-171, 2001.

16)        Dale AM, Liu AK, Fischl BR, Buckner RL, Belliveau JW, Lewine JD, Halgren E: Dynamic statistical parametric mapping: combining fMRI and MEG for high-resolution imaging of cortical activity. Neuron 26: 55-67, 2000.

17)        De Peralta-Menendez RG, Gonzalez-Andino SL: A critical analysis of linear inverse solutions to the neuroelectromagnetic inverse problem. IEEE Transactions on Biomedical Engineering 45: 440-448, 1998.

18)        Dierks T, Jelic V, Pascual-Marqui RD, Wahlund LO, Julin P, Linden DEJ, Maurer K, Winblad B, Nordberg A: Spatial pattern of cerebral glucose metabolism (PET) correlates with localization of intracerebral EEG-generators in Alzheimer's disease. Clinical Neurophysiology 111: 1817-1824, 2000.

19)        Fallgatter AJ, Bartsch AJ, Herrmann MJ. Electrophysiological measurements of anterior cingulate function. J Neural Transm, 2002, 109:977-988.

20)        Frei E, Gamma A, Pascual-Marqui R, Lehmann D, Hell D, Vollenweider FX: Localization of MDMA-induced brain activity in healthy volunteers using low resolution brain electromagnetic tomography (LORETA). Human Brain Mapping 14: 152-165, 2001.

21)        Fuchs M, Wagner M, Kohler T, Wischmann HA: Linear and nonlinear current density reconstructions. Journal of Clinical Neurophysiology 16: 267-295, 1999.

22)        Gallinat J, Mulert C, Bajbouj M, Herrmann WM, Schunter J, Senkowski D, Moukhtieva R, Kronfeldt D, Winterer G. Frontal and temporal dysfunction of auditory stimulus processing in schizophrenia. NeuroImage, 2002, 7:110-127.

23)        Gamma A, Frei E, Lehmann D, Pascual-Marqui RD, Hell D, Vollenweider FX: Mood state and brain electric activity in Ecstasy users. Neuroreport 11: 157-162, 2000.

24)        Gomez JF, Thatcher RW: Frequency domain equivalence between potentials and currents using LORETA. International Journal of Neuroscience 107: 161-171, 2001.

25)        Haalman I, Vaadia E: Dynamics of neuronal interactions: relation to behavior, firing rates, and distance between neurons. Human Brain Mapping 5: 249-253, 1997.

26)        Hämäläinen MS, Hari R, Ilmoniemi RJ, Knuutila J, Lounasmaa OV: Magnetoencephalography - theory, instrumentation, and applications to noninvasive studies of the working human brain. Rev. Mod. Phys. 65: 413–497, 1993.

27)        Hamm JP, Johnson BW, Kirk IJ. Comparison of the N300 and N400 ERPs to picture stimuli in congruent and incongruent contexts. Clinical Neurophysiology, 2002, 113:1339-1350.

28)        Helmholtz H: Ueber einige Gesetze der Vertheilung elektrischer Ströme in körperlichen Leitern, mit Anwendung auf die thierisch-elektrischen Versuche, Ann. Phys. Chem. 89: 211-233, 353-377, 1853.

29)        Hirota T, Yagyu T, Pascual-Marqui RD, Saito N, Kinoshita T: Spatial structure of brain electric fields during intermittent photic stimulation. Neuropsychobiology 44: 108-112, 2001.

30)        Horwitz B, Poeppel D. COMMENTARY: How can EEG/MEG and fMRI/PET data be combined? Human Brain Mapping, 2002, 17:1-3.

31)        Isotani T, Tanaka H, Lehmann D, Pascual-Marqui RD, Kochi K, Saitoh N, Yagyu T, Kinoshita T, Sasada K: Source localization of EEG activity during hypnotically induced anxiety and relaxation. International Journal of Psychophysiology 41: 143-153, 2001.

32)        Jausovec N, Jausovec K: Differences in EEG current density related to intelligence. Cognitive Brain Research 12: 55-60, 2001.

33)        Khateb A, Michel CM, Pegna, AJ, Landis T, Annoni JM: New insights into the stroop effect: a spatiotemporal analysis of electric brain activity. Neuroreport 11: 1849-1855, 2000.

34)        Khateb A, Michel CM, Pegna AJ, Thut G, Landis T, Annoni JM: The time course of semantic category processing in the cerebral hemispheres: an electrophysiological study. Cognitive Brain Research 10: 251-264, 2001.

35)        Khateb A, Pegna AJ, Michel CM, Landis T, Annoni JM. Dynamics of brain activation during an explicit word and image recognition task: an electrophysiological study. Brain Topography, 2002, 14:197-213.

36)        Kincses WE, Braun C, Kaiser S, Elbert T: Modeling extended sources of event-related potentials using anatomical and physiological constraints. Human Brain Mapping 8(4) :182-193, 1999.

37)        Koles ZJ, Flor-Henry P, Lind JC: Low-resolution electrical tomography of the brain during psychometrically matched verbal and spatial cognitive tasks. Human Brain Mapping 12: 144-156, 2001.

38)        Kounios J, Smith RW, Yang W, Bachman P, D'esposito M: Cognitive association formation in human memory revealed by spatiotemporal brain imaging. Neuron 29: 297-306, 2001.

39)        Lantz G, Michel CM, Pascual-Marqui RD, Spinelli L, Seeck M, Seri S, Landis T, Rosen I: Extracranial localization of intracranial interictal epileptiform activity using LORETA (low resolution electromagnetic tomography). Electroencephalography and Clinical Neurophysiology 102: 414-422, 1997.

40)        Lavric A, Pizzagalli D, Forstmeier S, Rippon G: Mapping dissociations in verb morphology. Trends in Cognitive Sciences 5: 301-308, 2001a.

41)        Lavric A, Pizzagalli D, Forstmeier S, Rippon G: A double-dissociation of English past-tense production revealed by event-related potentials and low-resolution electromagnetic tomography (LORETA). Clinical Neurophysiology 12: 1833-1849, 2001b.

42)        Leder U, Haueisen J, Pohl P, Malur FM, Heyne JP, Baier V, Figulla HR: Methods for the computational localization of atrio-ventricular pre-excitation syndromes. International Journal of Cardiac Imaging 17: 153-160, 2001.

43)        Lehmann D, Faber PL, Achermann P, Jeanmonod D, Gianotti LRR, Pizzagalli D. Brain sources of EEG gamma frequency during volitionally meditation-induced, altered states of consciousness, and experience of the self. Psychiatry Research: Neuroimaging, 2001, 108:111-121.

44)        Llinas RR: The intrinsic electrophysiological properties of mammalian neurons: insights into central nervous system function. Science 242: 1654-1664, 1988.

45)        Logothetis NK, Pauls J, Augath M, Trinath T, Oeltermann A: Neurophysiological investigation of the basis of the fMRI signal. Nature 412: 150-157, 2001.

46)        Martin JH: The collective electrical behavior of cortical neurons: The electroencephalogram and the mechanisms of epilepsy. In Kandel ER, Schwartz JH, Jessell TM (Eds.) Principles of Neural Science, Prentice Hall International, London, pp 777-791, 1991.

47)        Menendez RGD, Andino SLG: Discussing the capabilities of Laplacian minimization. Brain Topography 13: 97-104, 2000.

48)        Michel CM, De Peralta RG, Lantz G, Andino SG, Spinelli L, Blanke O, Landis T, Seeck M: Spatiotemporal EEG analysis and distributed source estimation in presurgical epilepsy evaluation. Journal of Clinical Neurophysiology 16: 239-266, 1999.

49)        Mulert C, Gallinat J, Pascual-Marqui R, Dorn H, Frick K, Schlattmann P, Mientus S, Herrmann WM, Winterer G: Reduced event-related current density in the anterior cingulate cortex in schizophrenia. Neuroimage13: 589-600, 2001.

50)        Mulert C, Juckel G, Augustin H, Hegerl U. Comparison between the analysis of the loudness dependency of the auditory N1/P2 component with LORETA and dipole source analysis in the prediction of treatment response to the selective serotonin reuptake inhibitor citalopram in major depression. Clinical Neurophysiology 2002, 113:1566-1572.

51)        Park HJ, Kwon JS, Youn T, Pae JS, Kim JJ, Kim MS, Ha KS. Statistical parametric mapping of LORETA using high density EEG and individual MRI: Application to mismatch negativities in schizophrenia. Human Brain Mapping, 2002, 17:168-178.

52)        R.D. Pascual-Marqui. Standardized low resolution brain electromagnetic tomography (sLORETA): technical details. Methods & Findings in Experimental & Clinical Pharmacology 2002, 24D:5-12.

53)        Pascual-Marqui RD, Esslen M, Kochi K, Lehmann D. Functional mapping of electric neuronal activity with zero localization error: standardized low resolution brain electromagnetic tomography (sLORETA). Presented at the 8th International Conference on Functional Mapping of the Human Brain, June 2-6, 2002, Sendai, Japan. Available on CD-Rom in NeuroImage, 2002, Vol. 16, No. 2.

54)        Pascual-Marqui RD: Reply to comments by Hämäläinen, Ilmoniemi and Nunez. In Source Localization: Continuing Discussion of the Inverse Problem (W. Skrandies, Ed.), pp. 16-28, ISBET Newsletter No.6 (ISSN 0947-5133), 1995.

55)        Pascual-Marqui RD: Review of methods for solving the EEG inverse problem. International Journal of Bioelectromagnetism 1: 75-86, 1999.

56)        Pascual-Marqui RD, Biscay-Lirio R. Spatial resolution of neuronal generators based on EEG and MEG measurements. International Journal of Neuroscience 68: 93-105, 1993.

57)        Pascual-Marqui RD, Lehmann D, Koenig T, Kochi K, Merlo MCG, Hell D, Koukkou M: Low resolution brain electromagnetic tomography (LORETA) functional imaging in acute, neuroleptic-naive, first-episode, productive schizophrenia. Psychiatry Research-Neuroimaging 90: 169-179, 1999.

58)        Pascual-Marqui RD, Michel CM, Lehmann D: Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain. International Journal of Psychophysiology. 18: 49-65, 1994.

59)        Pegna AJ, Khateb A, Spinelli L, Seeck M, Landis T, Michel CM: Unraveling the cerebral dynamics of mental imagery. Human Brain Mapping 5: 410-421, 1997.

60)        Pizzagalli D, Lehmann D, Koenig T, Regard M, Pascual-Marqui RD: Face-elicited ERPs and affective attitude: brain electric microstate and tomography analyses. Clinical Neurophysiology 111: 521-531, 2000.

61)        Pizzagalli D, Pascual-Marqui RD, Nitschke JB, Oakes TR, Larson CL, Abercrombie HC, Schaefer SM, Koger JV, Benca RM, Davidson RJ: Anterior cingulate activity as a predictor of degree of treatment response in major depression: evidence from brain electrical tomography analysis. American Journal of Psychiatry 158: 405-415, 2001.

62)        Pizzagalli DA, Nitschke JB, OakesTR, Hendrick AM, Horras KA, Larson CL, Abercrombie HC, Schaefer SM, Koger JV, Benca RM, Pascual-Marqui RD, Davidson RJ. Brain electrical tomography in depression: the importance of symptom severity, anxiety, and melancholic features. Biological Psychiatry, 2002, 52:73–85.

63)        Pizzagalli DA, Lehmann D, Hendrick AM, Regard M, Pascual-Marqui RD, Davidson RJ. Affective judgments of faces modulate early activity (~160 ms) within the fusiform gyri. NeuroImage, 2002, 16:663-677.

64)        Phillips C, Rugg MD, Friston KJ. Anatomically informed basis functions for eeg source localization: combining functional and anatomical constraints. NeuroImage 2002a, 16:678–695.

65)        Phillips C, Rugg MD, Friston KJ. Systematic regularization of linear inverse solutions of the eeg source localization problem. NeuroImage 2002b, 17:287-301.

66)        Prabhu VR, Porjesz B, Chorlian DB, Wang KM, Stimus A, Begleiter H: Visual P3 in female alcoholics. Alcoholism-Clinical and Experimental Research 25: 531-539, 2001.

67)        Saletu B, Anderer P, Di Padova C, Assandri A, Saletu-Zyhlarz GM. Electrophysiological neuroimaging of the central effects of S-adenosyl-L-methionine by mapping of electroencephalograms and event-related potentials and low-resolution brain electromagnetic tomography. Am J Clin Nutr 2002, 76(suppl):1162S–1171S.

68)        Schairer KS, Gould HJ, Pousson MA. Source generators of mismatch negativity to multiple deviant stimulus types. Brain Topography, 2001, 14:117-130.

69)        Seeck M, Lazeyras F, Michel CM, Blanke O, Gericke CA, Ives J, Delavelle J, Golay X, Haenggeli CA, De Tribolet N, Landis T: Non-invasive epileptic focus localization using EEG-triggered functional MRI and electromagnetic tomography. Electroencephalography and Clinical Neurophysiology 106: 508-512, 1998.

70)        Steger J, Imhof K, Denoth J, Pascual-Marqui RD, Steinhausen HC, Brandeis D: Brain mapping of bilateral visual interactions in children. Psychophysiology 38: 243-253, 2001.

71)        Strik WK, Fallgatter AJ, Brandeis D, Pascual-Marqui RD: Three-dimensional tomography of event-related potentials during response inhibition: evidence for phasic frontal lobe activation. Evoked Potentials-Electroencephalography and Clinical Neurophysiology 108: 406-413, 1998.

72)        Sukov W, Barth DS: Three-dimensional analysis of spontaneous and thalamically evoked gamma oscillations in auditory cortex. J. Neurophysiol. 79: 2875–2884, 1998.

73)        Szelenberger W, Niemcewicz S. Event-related current density in primary insomnia. Acta Neurobiol. Exp. 2001, 61:299-308.

74)        Talairach J, Tournoux P: Co-Planar Stereotaxic Atlas of the Human Brain: Three-Dimensional Proportional System. Georg Thieme, Stuttgart, 1988.

75)        Thayer ZC, Johnson BW, Corballis MC, Hamm JP. Perceptual and motor mechanisms for mental rotation of human hands. Neuroreport, 2001, 12:3433-3437.

76)        Thut G, Hauert CA, Morand S, Seeck M, Landis T, Michel C: Evidence for interhemispheric motor-level transfer in a simple reaction time task: an EEG study. Experimental Brain Research 128: 256-261, 1999.

77)        Thut G, Hauert CA, Viviani P, Morand S, Spinelli L, Blanke O, Landis T, Michel C: Internally driven vs. externally cued movement selection: a study on the timing of brain activity. Cognitive Brain Research 9: 261-269, 2000.

78)        Toga AW, Mazziotta JC: Brain mapping: the methods. Academic Press, San Diego, 1996.

79)        Van Leeuwen TH, Steinhausen HC, Overtoom CCE, Pascual-Marqui RD, Van't Klooster B, Rothenberger A, Sergeant JA, Brandeis D: The continuous performance test revisited with neuroelectric mapping: impaired orienting in children with attention deficits. Behavioural Brain Research. 94: 97-110, 1998.

80)        Vitacco D, Brandeis D, Pascual-Marqui RD, Martin E. Correspondence of event-related potential tomography and functional magnetic resonance imaging during language processing. Human Brain Mapping, 2002, 17:4-12.

81)        Waberski TD, Kreitschmann-Andermahr I, Kawohl W, Darvas F, Ryang Y, Gobbele R, Buchner H: Spatio-temporal source imaging reveals subcomponents of the human auditory mismatch negativity in the cingulum and right inferior temporal gyrus. Neuroscience Letters 308: 107-110, 2001.

82)        Wang J, Jin YP, Xiao F, Fan SL, Chen L: Attention-sensitive visual event-related potentials elicited by kinetic forms. Clinical Neurophysiology 110: 329-341, 1999.

83)        Winterer G, Mulert C, Mientus S, Gallinat J, Schlattmann P, Dorn H, Herrmann WM: P300 and LORETA: Comparison of normal subjects and schizophrenic patients. Brain Topography 13: 299-313, 2001.

84)        Worrell GA, Lagerlund TD, Sharbrough FW, Brinkmann BH, Busacker NE, Cicora KM, O'Brien TJ: Localization of the epileptic focus by low-resolution electromagnetic tomography in patients with a lesion demonstrated by MRI. Brain Topography 12: 273-282, 2000.

85)        Yao D, He B. A self-coherence enhancement algorithm and its application to enhancing three-dimensional source estimation from EEGs. Annals of Biomedical Engineering, 2001, 29:1019–1027.