Blake philosophy teaching

Blake, Nigel (2000)

Tutors and Students without Faces or Places

Journal of Philosophy of Education, 34,1, 183-196

Reflect on distance education

P183 ‘Centrally, tuition is conducted here in personal interaction through the written word’. P 183 ‘However, it is constantly compared and contrasted negatively with conventional education in at least one respect: that it is not ‘face-to-face’ it is the alternative for students who need to study at a distance. There is a notion that  interaction by means of the written word diminishes the quality of communication between tutors and students. ‘ the belief that body language , as an aspect of communication, is an unqualified good’ KRO what would constitute an adequate defence of these claims?

P 184 ‘The idea that there is something special and important about the physical co-presence of teachers and students may be ill-articulated but it obviously has plausible appeal’ Lets take the scenario of the way that the distance learner will study.  The live teacher is replaced by a teaching text crafted by a live teacher. P184 ‘ Instead of the text actually teaching, the student has to actively study the text’….p185 ‘ So if we force the question what goes on, teaching or study, on such occasions, the question has really no point of interest. The answer is rather that a conventional link between teaching and physical presence is less fundamental that we often think and has been broken here’

Evaluate face-to-face

lets dissect a face-to-face interaction – what kind of speech acts are involved, greetings & the exchange of pleasantries, compliments, jokes, advice, warnings, rebukes, insults, evasions. Although they all have a cognitive element  (they involve information exchange) constatives, there is also a large element of  the performative, p186 ‘ whose character as social actions is at least as important as their character of communicating information’ ( KRO and that has been the challenging part of constructing the narrative) ‘ As speech acts, as events at some particular time and place, performatives are highly sensitive to social and material context.’ In contrast constatives are not so reliant on context. Constatives tend to be right or wrong, whereas perfomatives are either successful or unsuccessful. Perfomatives can misfire, the social background has to be properly understood by both the speaker and the hearer p 186 ‘Plausibly, it is the nature of the social relationship between the speakers and how it is reinforced or transformed which is the important issue in these speech interactions’ performatives are easily aligned with familiar kinds of intonation, body language and facial expression. In these cases of mundane face-to-face interaction, there seems a characteristic intertwining between the physical and the verbal……..there is something patently appropriate about physical interaction in these banal contexts. Those with whom we cement relationships, are embodied people, and their personal characteristics as embodied are often relevant to the nature of the relationship we have with them. Moreover, the inherent actual or potential embodiedness of relationships – the inherent address of relationships to the Other as embodied – is betrayed by the fact that ‘body language’ and facial expression are not in fact simply items of a kind of shared, intuitive physical  ‘ vocabulary’ . They also reveal aspects of ourselves to other quite unintentionally and often without us realising it, thus cementing or impeding relationships all the more effectively. So in this unconscious way too, bodies can and do intervene in the construction of relationships’ and it is context sensitive.  (KRO therefore important to retain the context for the narratives)

What is appropriate for academic interactions, particularly in HE?

Focus – the substance and complexities of the discipline, not on our selves, our own interests or even on our personal reactions to the topic.

Values – disinterestedness

Vices – bias, partiality, vested interest, prejudice

Role ‘Academic objectivity requires us to sift very carefully questions of true and false, right and wrong, valid or invalid, good and bad, insightful or obtuse from those of personal taste or distaste, political or religious commitment, fear or loathing, enthusiasm or delight’

Skill/ Competence p 188 ‘the personal, the subjective and the individual have to be somehow bracketed off and kept in their place, on both sides of the teaching interaction….. one of the tasks of  a teacher may often be to alert the student to her own lapses of objectivity, to the moments where her own personal values, emotions, and limitations may be clouding or distorting her judgement’  Therefore in face-to-face academic practice there is a presumption about the ‘non-verbal as being inappropriate’, ‘that academic life has its own decorum, functional for the pursuit of its higher aims. The purpose of this decorum is precisely to bracket off, to tame or even sometimes to expunge the influence of non-academic personal relations, personal interests and commitments’ that the personal ‘is an impediment to distinterestedness’ ie the non-verbal  (that extends the communicative repetiore) may be irrelevant or invidious even to academic teaching.

Academic interactions online

’In the previous section we explored the idea that cognitive use of language comes first in academia. ‘p 193 ‘ If online tuition is to be genuine teaching, then insightful interpretation of the student’s written word is at a premium. The question is not ‘ “What do these words mean?” but “What does this student mean [by these words]” And in addressing that particular problem, any indications the tutor can garner from the student’s text may seem relevant and appropriate to the task. Moreover, we cannot assume any a priori limits as to what aspects of a student’s life and experience will influence or inform her own attempts to make sense of academic material and ideas’

Then mostly on identity

useful article Authored by .... on his blog

Top IT issues

  by  torkildr

What's the trend of the year in educational technology? Most people would immediately shout out MOOCs (as I predicted way back in January, 2012 - the year of the MOOC) but according to anEducause survey, Top-10 IT issues 2012,  the most important concerns are not whether to join the MOOC movement but the fundamental issue of how to integrate IT into all of the university's activities and develop more mature strategies for the use of technology.

The survey asked a panel of higher education IT experts what the biggest single IT-realted issue facing their institution had been in 2012. Here's their top ten:

1. Updating IT professionals' skills and roles to accommodate new technologies and changing IT delivery models
2. Supporting IT consumerization and bring-your-own device programs
3. Developing a cloud strategy
4. Improving the institution's operational efficiency through IT
5. Integrating IT into institutional decision-making
6. Using analytics to support the important institutional outcomes
7. Funding IT initiatives
8. Transforming the institution's business with IT
9. Supporting research with high-performance computing, large data, and analytics
10. Establishing and implementing IT governance throughout the institution

Source: "Educause Top-Ten IT Issues 2012" from Educause Center for Applied Research (ECAR)


The common thread here is the need to integrate technology use into all areas of the university from the management down and to ensure that staff have the right competence to deal with this change. IT is no longer simply a technology issue and is no longer limited to the IT department; it supports all processes and affects every member of staff. Educational technology is moving from a marginal pioneer movement of enthusiasts to mainstream and default. Institutional strategies are now needed where before there were simply uncoordinated grassroots initiatives. The tools and software that were once provided in-house are now freely available in the cloud and at the same time the carefully controlled infrastructure of university owned computer labs is being replaced by students using their own devices and expecting access anywhere any time.

The challenges facing the role of IT in higher education are finding strategies of benefitting from the diversity and freedom of cloud-based solutions and personal devices while maintaining some level of control and security. The survey highlight above all else the rapidly changing role of the university IT department.

"This year's list transcends the IT org chart with two predominant themes: the IT organization's obligation to the institution; and the IT organization's relationship to technology outside the institution. The former views the IT organization as an enabler and partner in helping colleges and universities adapt to and even capitalize on changing realities and needs via automation (Issue #4), analytics (Issue #6), business transformation (Issue #8), and research computing (Issue #9). It also recognizes that the IT organization's relationship with institutional leaders must be effective for it to truly support institutional priorities, by integrating information technology into institutional decision-making (Issue #5), funding information technology strategically (Issue #7), and establishing and implementing IT governance throughout the institution (Issue #10)."

Read more in an article in Campus Technology, Reflecting on the Top IT Issues of 2012

wellcome research

Learning to control brain activity improves visual sensitivity

5 December 2012

Training eople to control their own brain activity can enhance their visual sensitivity, according to a new study. This non-invasive ‘neurofeedback’ approach could one day be used to improve brain function in patients with abnormal patterns of activity, for example after a stroke.

Researchers at the Wellcome Trust Centre for Neuroimaging at UCL used non-invasive, real-time brain imaging that enabled participants to watch their own brain activity on a screen, a technique known as neurofeedback. During the training phase, they were asked to try to increase activity in the area of the brain that processes visual information, the visual cortex, by imagining images and observing how their brains responded.

After the training phase, the participants' visual perception was tested using a new task that required them to detect very subtle changes in the contrast of an image. When they were asked to repeat this task while clamping brain activity in the visual cortex at high levels, those who had successfully learned to control their brain activity could improve their ability to detect even very small changes in contrast.

This improved performance was only observed when participants were exercising control over their brain activity.

Lead author Dr Frank Scharnowski, who is now based at the University of Geneva, explains: "We've shown that we can train people to manipulate their own brain activity and improve their visual sensitivity, without surgery and without drugs."

In the past, researchers have used recordings of electrical activity in the brain to train people on various tasks, including cutting their reaction times, altering their emotional responses and even improving their musical performance. In this study, the researchers used functional magnetic resonance imaging (fMRI) to provide the volunteers with real-time feedback on brain activity. The advantage of this technique is that you can see exactly where in the brain the training is having an effect, so you can target the training to particular brain areas that are responsible for specific tasks.

"The next step is to test this approach in the clinic to see whether we can offer any benefit to patients, for example to stroke patients who may have problems with perception, even though there is no damage to their vision," adds Dr Scharnowski.

The study, funded by the Wellcome Trust, the Swiss National Science Foundation and the European Union, is published online today in the 'Journal of Neuroscience'.

Image: Functional MRI scans showing visual cortex activity before (top row) and after (bottom row) neurofeedback training. Credit: F Scharnowski

Reference

Scharnowski F et al. Improving visual perception through neurofeedback. J Neurosci 2012 [epub].

Foster & Harrison (2002)

Foster, P.S. and Harrison, D.W. (2002)

The relationship between magnitude of cerebral activation and intensity of emotional arousal

International Journal Neuroscience, 112, 1463-1477

P 1465 ‘Very little know about the cerebral representation of subjective emotional intensity’

P1466 ‘ numerous investigations have implicated the temporal lobes in the experience of both positively and negatively valenced emotions’.

Hypothesis ‘increased subjective intensity of angry memories would be associated with increasing cerebral activation’  especially of low beta (13-21) and high beta (21-32) Hz.  But doesn’t say why beta is targeted

Method

Monopolar QEEG recordings , linked ear references. Sampling rate 256 Hz frequencies below 2 Hz eliminated.

Target frequencies

Alpha (8-13 Hz)

High beta (21-32 Hz)

Low beta (13-21) Hz

Eyes closed throughout

Procedure

1. Base line measure : 46 1 sec epochs
2. 5-6 mins to relax
3. instructed to recall a memory to which they had responded with anger – 46 secs. Therefore collected 46 1 sec epochs during recall of an emotional (angry) event

Participants asked to rate memory on intensity from 1 to 7.

Data reduction

P1486 ‘ Change scores were created for the purpose of calculating correlations between changes in cerebral activation, as measured by EEG, and the ratings of subjective intensity of angry memories’.

Change score average mv ( memory condition) – average mV (baseline) after artefact removal.  Male and female analysed separately.

Results

Males

Alpha – no correlation

High beta - FP1, FP2, F8, T6, P3, O1

Low beta -                   F8, T5, T6, PZ,P4,O1,O2

Females

Alpha - no correlation

High beta – T6

Low beta – no correlation

Discussion

Results are consistent with research that sees the right hemisphere as being implicated in emotion processing, particularly negative emotions, and that laterality effects are stronger in men.

research at wellcome

Primates (and people) are remarkably good at ranking each other within social hierarchies, a survival technique that helps us to avoid conflict and select advantageous 

Primates (and people) are remarkably good at ranking each other within social hierarchies, a survival technique that helps us to avoid conflict and select advantageous 

allies. However, we know surprisingly little about how the brain does this.

The team at the UCL Institute for Cognitive Neuroscience used brain imaging techniques to investigate this in 26 healthy volunteers.

Participants were asked to play a simple science fiction computer game in which they acted as future investors. In the first phase, they needed to learn which individuals have most power within a fictitious space mining company (the social hierarchy) and then which galaxies have most precious minerals (non-social information).

While the participants were doing the experiment, the team used functional magnetic resonance imaging (fMRI) to monitor activity in their brains, and another MRI scan was also taken to look at their brain structure. The findings reveal a striking dissociation between the neural circuits used to learn social and non-social hierarchies.

The researchers observed increased neural activity in both the amygdala and the hippocampus when participants were learning about the hierarchy of executives within the fictitious space mining company. By contrast, when they were learning about the non-social hierarchy (i.e. which galaxies had more minerals), only the hippocampus was used.

The researchers also found that participants who were better at learning the social hierarchy had a larger volume of grey matter in the amygdala than those who were less able.

Dr Dharshan Kumaran at the UCL Institute of Cognitive Neuroscience, who led the study, explains: "These findings are telling us that the amygdala is specifically involved in learning information about social rank based on experience and suggest that separate neural circuits are involved than for learning hierarchy information of a non-social nature."

This is the first time that researchers have looked at how rank within a social hierarchy is judged based on knowledge acquired through experience, rather than perceptual cues (like visual appearance), which are typically unreliable predictors of rank. In a second phase of the experiment, the team also looked at how we recall information about social rank when we meet somebody again, and their study reveals how this information is represented in the brain.

They asked participants to place bids on investment projects based on the knowledge about rank they had acquired during the first phase of the experiment. This was played out in the game as a particular executive leading a mission to harvest minerals from a galaxy.

They found evidence that social rank, but not non-social rank, is translated into neural activity in the amygdala in a linear fashion: the level of activity in the amygdala was observed to increase according to the social rank of the person being encountered. This signal provides a potential mechanism by which individuals select coalition partners in the real world based on their rank.

Being able to interpret social rank is important for us to meet the challenging pressures of living in large social groups. Knowing where we fit into a social group determines how we behave towards different people.

As well as giving new understanding of which brain circuits are involved in learning and storing this information, the findings reported in this study help to explain why some people are better at it than others. The researchers are now keen to look at people with brain and developmental disorders to see how their ability to learn social hierarchies is affected.

The study has been published in the journal 'Neuron'.

Image: An fMRI scan reveals that brain activity in the amygdala tracks participants’ knowledge about a social, but not a non-social, hierarchy. Credit: D Kumaran et al. (Neuron, 2012).

Kaiser QEEG

What is Quantitative EEG

David A. Kaiser, Ph.D.

Rochester Institute of Technology

What determines the characteristic rhythms of the EEG?

Moruzzi and Magoun (1949) were the first to shed light on the origin of EEG

rhythms. Working in cats they determined that stimulating the reticular formation of the brainstem aroused the animal behaviorally and any dispersed high-amplitude EEG rhythms at the cortex. This work eventually led to a general physiological model of rhythmic activity (Andersen & Andersson, 1968; Steriade et al. 1990). According to the model, isolated thalamocortical neurons fire rapidly at their own pace due to metabolic characteristics but in an intact brain, a sheath of cells known as the reticular thalamic nucleus (RTN) inhibits intrinsic or random firing of thalamocortical neurons and unites

individual discharges into simultaneous volleys. These volleys propagate to the cortex and synchronize pyramidal cell activity, whose synchronization can be detected at the scalp as high-amplitude oscillations (e.g., alpha bursts, sleep spindles). Corticothalamic feedback influences these volleys by inhibiting RTN's inhibitory action so that neuronal ensembles may break free of reticular thalamic influence and fire in response to specific processing demands. When this occurs, large slow waveforms (theta, alpha) are replaced

by faster frequencies of lower amplitude (beta, gamma), a process originally called alpha blocking and now called EEG desynchronization. Desynchronization may be localized to a single electrode as uncommitted cortical areas remain "idling" or synchronized, or it may involve several brain areas or electrodes (Pfurtscheller, 1992; Sterman et al. 1994). Regional patterns of simultaneous desynchronization and synchronization characterize

specific cognitive and behavioral states (Pfurtscheller & Klimesch, 1990) and it is bymeasuring the mix of slow and fast rhythms across the head that we identify the nature and extent of cortical engagement.

How is QEEG Related to Human Behavior?

EEG as a crude measure of mental state ( eg eyes open (attention, alert, aroused or eyes closed (inattention and inactivity) has been documented extensively and over a considerable period of time.

Behavioral and mental states such as mathematical processing, reading, or relaxed wakefulness are assumed to be distinct and uniform in nature, consisting of similar perceptual and cognitive operations whenever they occur. It is also assumed that distinct mental operations present distinct EEG and biochemical profiles which are reproduced reliably whenever a task or mental state occurs. These assumptions lay the foundation to functional MRI as well as EEG assessment and are the rationale for EEG normalization

training.

The most reliable finding in EEG research occurs when an individual

resting with eyes closed opens his or her eyes in a well-lit room: alpha blocking occurs. The alpha rhythm is replaced by fast low-amplitude waveforms, or beta rhythm. (When eyes are opened in a dark room, alpha blocking does not generally occur; Bohdanecky et al., 1984.) The degree and localization of blocking or desynchronization is associated with stimulus intensity, complexity, novelty, and meaningfulness (Gale & Edwards,1983; Berlyne & McDonnell, 1965; Baker & Franken, 1967; Boiten, Sergeant, & Geuze,1992; Gevins & Schaffer, 1980). Topographic analysis reveals whether EEGdesynchronization is nonspecific (many or all sites) or selective (few sites). Nonspecific arousal is modulated by drugs, drowsiness, drive, and time of day, whereas sensory and strategic demands activate specific brain areas such as parietal and occipital cortex to visual stimulation and temporal cortex to acoustic stimulation (e.g., Grillon & Buchsbaum, 1986; Pfurtscheller, Maresh, & Schuy, 1977; Chapotot, Jouny, Muzet,

Buguet, & Brandenberger, 2000).

Table 4. Cortical gyrus below each electrode position, based on Mokotoma et al

LOBE	GYRUS	BRODMANN AREA	SITE (left/right)
Frontal	Superior	10	Fp1/2
	Inferior	47	F7/8
	Medial	9	F3/4
	Medial	8	Fz
	Precentral	6	C3/4
	Superior	6	Cz
Temporal	Medial	21	T3/4
	Medial	37	T5/6
Parietal	Inferior	7	P3/4
	Precuneus	7	Pz
Occipital	Medial	19	O1/2

Figure 3. International 10-20 system for electrode placement on the scalp.

Coherence and Co modulation

Coherence analysis

quantifies phase consistency between signals and comodulation analysis quantifies

magnitude consistency (Goodman, 1957; Kaiser, 1994). Two signals are said to be

coherent when their phase relationship is stable, even if signals are entirely out of phase

with each other. Two signals are said to comodulate when their magnitude relationship is

stable, regardless of absolute difference between signals.

Development before an adult rhythm at 10 Hz is established (Niedermeyer, 1987).

The alpha rhythm emerges as a slow 3-4 Hz rhythm in infancy, and it takes a decade

Table 5. Rhythm Maturation: Alpha & Sleep Spindle Frequency Range by Age Group

(modified from Niedermeyer, 1987)

Rhythm	Newborn	Infant	Toddler	Preschooler	Preteen
Alpha	Not present	4-6	5-8	7-9	9-10
Sleep spindle	Not present	12-14	12-14	12-14	12-14

 BooksPfurtscheller G, & Lopes da Silva FH. (1999). Event-related EEG/MEG synchronization and

desynchronization: basic principles. Clinical Neurophysiology, 110, 1842-57.

References

Aboitiz F, Rodriguez E, Olivares R, Zaidel E. (1996). Age-related changes in fibre composition

of the human corpus callosum: sex differences. Neuroreport, 7, 1761-4.

Aboitiz, F., Scheibel, A.B., Fisher, R.S., & Zaidel, E. (1992). Individual differences in brain

asymmetries and fiber composition in the human corpus callosum. Brain Research, 598, 154-161.

American Electroencephalographic Society. (1994). Guideline thirteen: Guidelines for standard

electrode position nomenclature. Journal of Clinical Neurophysiology, 11, 111-113.

Andersen, P., & Andersson, S.A. (1968). Physiological basis of the alpha rhythm. New York:

Appleton-Century- Crofts.

Baker, G., & Franken, R. (1967). Effects of stimulus size, brightness and complexity on EEG

desynchronization. Psychonomic Science, 7, 289- 290.

Berfield, K.A., Ray, W.J., & Newcombe, N. (1986). Sex role and spatial ability: An EEG study.

Neuropsychologia, 24, 731-735.

Berger H (1929): Über das Elektroenkephalogram des Menschen. Arch. f. Psychiat. 87: 527-70.

Bohdanecky, Z., Indra, M., Lansky, P., & Radil-Weiss, T. (1984). Alternation of EEG alpha and

non-alpha periods does not differ in open and closed eye condition in darkness. Acta Neurobiology

Experimental, 44, 230-232.

What is Quantitative EEG - Kaiser - 28

Boiten, F., Sergeant, J., & Geuze, R. (1992). Event-related desynchronization: The effects of

energetic and computational demands. Electroencephalography & Clinical Neurophysiology, 82, 302-309.

Brazier MAB. (1961). Computer techniques in EEG analysis. Electroencephalogr Clin

Neurophysiology, S20, 2-6.

Brown, J.W., & Grober, E. (1983). Age, sex, and aphasia type: Evidence for a regional cerebral

growth process underlying lateralization. Journal of Nervous & Mental Disease, 171, 431-434.

Butler, S.R., & Glass, A. (1974). Asymmetries in the electroencephalogram associated with

cerebral dominance. Electroencephalography and Clinical Neurophysiology, 36, 481-491.

Chatrian GE, Lettich E, & Nelson PL (1985). Ten percent electrode system for topographic

studies of spontaneous and evoked EEG activity. American Journal of EEG Technology, 25, 83-92.

Clarke, J.M. (1990). Interhemispheric functions in humans: Relationships between anatomical

measures of the corpus callosum, behavioral laterality effects, and cognitive profiles. Unpublished doctoral

dissertation, Psychology Department, University of California, Los Angeles.

Coburn, K.L., & Moreno, M.A. (1988). Facts and artifacts in brain electrical activity mapping.

Brain Topography, 1, 37-45.

Cole, H. W., & Ray, W.J. (1985). EEG correlates of emotional tasks related to attentional

demands. International Journal of Psychophysiology, 3, 33-41.

Cooley, JW & Tukey JW (1965) An algorithm for the machine computation of the complex

Fourier series. Mathematics of Computation 19, 297-301.

Davidson, R.J., Chapman, J.P., Chapman, L.J., & Henriques, J. (1990). Asymmetrical brain

electrical activity discriminates between psychometrically-matched verbal and spatial cognitive tasks.

Psychophysiology, 27, 528-543.

de Rijke, W., & Visser, S.L. (1989). The use of derived map parameters in alpha blocking. In K.

Maurer (Ed.), Topographic brain mapping of EEG and evoked potentials (pp. 136-140). New York:

Springer-Verlag.

de Toffol, B., & Autret, A. (1991). Influence of lateralized neuropsychological activities with and

without sensorimotor components on EEG spectral power (a-rhythm). International Journal of

Psychophysiology, 11, 109-114.

What is Quantitative EEG - Kaiser - 29

Deakin, J.F., & Exley, K.A. (1979). Personality and male-female influences on the EEG alpha

rhythm. Biological Psychology, 8, 285-290.

Dietsch G. (1932) Fourier-Analyse von Elektrenkephalogrammen des Menschen. Pflugers Arch

1932:230:106-12

Dolce, G., & Waldeier, H. (1974). Spectral and multivariate analysis of EEG changes during

mental activity in man. Electroencephalography and Clinical Neurophysiology, 36, 577-584.

Dujardin, K., Derambure, P., Defebvre, L., Bourriez, J.L., Jacquesson, J.M., & Guieu, J.D. (1993).

Evaluation of event-related desynchronization (ERD) during a recognition task: Effect of attention.

Electroencephalography & Clinical Neurophysiology, 86, 353-356.

Dumermuth G & Fluhler, H (1967). Some modern aspects in numerical spectrum analysis of

multichannel electroencephalographic data. Medical and Biological Engineering, 5, 319-331.

Earle, J.B., & Pikus, A.A. (1982). The effect of sex and task difficulty of EEG alpha activity in

association with arithmetic. Biological Psychology, 15, 1-14.

Faber-Clark, M. M., & Moore, W.H. (1983). Sex task and processing strategy effects in

hemispheric alpha asymmetries for the recall and recognition of arousal words: Results from perceptual

and motor tasks in males and females. Brain & Cognition, 2, 233-250.

Fernandez, T., Harmony, T., Rodriguez, M., Reyes, A., Marosi, E., & Bernal, J. (1993). Testretest

reliability of EEG spectral parameters during cognitive tasks: I. Absolute and relative power.

International Journal of Neuroscience, 68, 255-261.

Flor-Henry, P., & Koles., Z.J. (1982). EEG characteristics of normal subjects: A comparison of

men and women and of dextrals and sinistrals. Research Communications in Psychology, Psychiatry, and

Behavior, 7, 21-38.

Freeman, W.J., & Maurer, K. (1989). Advances in brain theory give new direction to the use of

the technologies of brain mapping in behavioral studies. In K. Maurer (Ed.), Topographic brain mapping of

EEG and evoked potentials (pp. 118-126). New York: Springer-Verlag.

Gale, A., & Edwards, J. (1983). The EEG and human behavior. In E. Gale and C.A. Edwards

(Eds.), Physiological Correlates of Human Behavior (pp.99-127). London: Academic Press.

Gasser, T., Bacher, P., & Mocks, J. (1982). Transformations towards the normal distribution of

What is Quantitative EEG - Kaiser - 30

broad band spectral parameters of the EEG. Electroencephalography and Clinical Neurophysiology, 53,

119-124.

Gevins, A.S. (1984). Analysis of the electromagnetic signals of the human brain: Milestones,

obstacles, and goals. IEEE Transactions on Biomedical Engineering, 31, 833-850.

Gevins, A.S. (1986). Quantitative Human Neurophysiology. In H.J. Hannay (Ed.), Experimental

techniques in human neuropsychology (pp. 419- 456). New York: Oxford Press.

Gevins, A.S. (1993). High resolution EEG. Brain Topography, 5, 321-325.

Gevins, A.S., & Schaffer, R.E. (1980). A critical review of electroencephalographic (EEG)

correlates of higher cortical functions. CRC Critical Reviews in Bioengineering, 4, 113-164.

Goodman NR (1957). On the joint estimation of the spectra, cospectrum and quadrature spectrum

of a two-dimensional stationary Gaussian process. Ph.D. dissertation, Princeton Univ.

Goodman, D., & Mulholland, T. (1988). Detection of cerebral lateralization of function using

EEG alpha-contingent visual stimulation II. International Journal of Psychophysiology, 6, 255-261.

Grass, AM & Gibbs FA. 1938. A Fourier transform of the. electroencephalogram. J.

Neurophysiology, 1, 521–526

Gratton, G., Villa, A.E., Fabiani, M., Colombis, G., Palin, E., Bolcioni, G., & Fiori, M.G. (1992).

Functional correlates of a three-component spatial model of the alpha rhythm. Brain Research, 582, 159-

162.

Gregson, R.A., Britton, L.A., Campbell, E.A., & Gates, G. R. (1990). Comparisons of the

nonlinear dynamics of electroencephalograms under various task loading conditions: A preliminary report.

Biological Psychology, 31, 173-191.

Grillon, C., & Buchsbaum, M.S. (1986). Computed EEG topography of response to visual and

auditory stimuli. Electroencephalography and clinical Neurophysiology, 63, 42-53.

Gundel, A., & Wilson, G.F. (1992). Topographical changes in the ongoing EEG related to the

difficulty of mental tasks. Brain Topography, 5, 17-25.

Herring, S., & Reitan, R.M. (1986). Sex similarities in verbal and performance IQ deficits

following unilateral cerebral lesions. Journal of Consulting & Clinical Psychology, 54, 537-541.

Herring, S.L., & Reitan, R.M. (1992). Gender influence on neuropsychological performance

What is Quantitative EEG - Kaiser - 31

following unilateral cerebral lesions. Clinical Neuropsychologist, 6, 431-442.

Homan, R.W., Herman, J., & Purdy, P. (1987). Cerebral location of international 10-20 system

electrode placement. Electroencephalography and Clinical Neurophysiology, 66, 376-382.

Inglis, J., & Lawson, J.S. (1982). A meta-analysis of sex differences in the effects of unilateral

brain damage on intelligence test results. Canadian Journal of Psychology, 36, 670-683.

Jasper, H.H. (1958). The 10-20 system of the international federation. Electroencephalography

and Clinical Neurophysiology, 10, 371-375.

Jervis, B.W., Coelho, M., & Morgan, G.W. (1989). Spectral analysis of EEG responses. Medical

and Biological Engineering and Computing, 27, 230-238.

Kaiser, D.A. (1994). Interest in films as measured by ratings and topographic EEG. Ph.D.

Dissertation, University of California Los Angeles.

Kaiser DA (2006). Comodulation and coherence in normal and clinical populations. Presented at

37th Association for Applied Psychophysiology and Biofeedback, Portland, Apr 8.

Kimura, D. (1983). Sex differences in cerebral organization for speech and praxic functions.

Canadian Journal of Psychology, 37, 19-35.

Kinsborne, M. (1980). If sex differences in brain lateralization exist, they have yet to be

discovered. Behavioral and Brain Sciences, 3, 231-232.

Klimesch, W., Pfurtscheller, G., Mohl, W., & Schimke, H. (1990). Event-related

desynchronization, ERD-mapping and hemispheric differences for words and numbers. International

Journal of Psychophysiology, 8, 297-308.

Klimesch, W., Pfurtscheller, G., & Schimke, H. (1992). Pre- and post-stimulus processes in

category judgement tasks as measured by event-related desynchronization (ERD). Journal of

Psychophysiology, 6, 185-203.

Konovalov, V.F., & Otmakhova, N.A. (1983). EEG manifestations of functional asymmetry of the

human cerebral cortex during perception of words and music. Human Psychophysiology, 9, 250-255.

Lake, D.A., & Bryden, M.P. (1976). Handedness and sex differences in hemispheric asymmetry.

Brain & Language, 3, 266-282.

Lavie, P. (1989). Ultradian rhythms in arousal: The problem of masking. Chronobiology

What is Quantitative EEG - Kaiser - 32

International, 6, 21-28.

Legewie, H., Simonova, O., & Creutzfeldt, O.D. (1969). EEG changes during performance of

various tasks under open- and closed-eyed conditions. Electroencephalography & Clinical

Neurophysiology, 27, 470-479.

Lopes da Silva, F.H. (1978). Analysis of EEG Non- Stationarities. In W.A. Cobb & H. Van Duijn,

Contemporary clinical neurophysiology (EEG Supplement No. 34). Amsterdam: Elsevier Scientific

Publishing Co. 163-179.

Lopes da Silva, F.H. (1991). Neural mechanisms underlying brain waves: From neural membranes

to networks. Electroencephalography and Clinical Neurophysiology, 79, 81-93.

Lopes da Silva, F.H., Van Lierop, T.H.M.T., Schrijer, D.F.M., & Storm van Leeuwen, W. (1973).

Essential difference between alpha rhythms and barbiturate spindles: Spectra and thalamo-cortical

coherences. Electroencephalography and Clinical Neurophysiology, 35, 627- 639.

Lorig, T.S., & Schwartz, G. (1989). Factor analysis of the EEG indicates inconsistencies in

traditional frequency bands. Journal of Psychophysiology, 3, 369- 375.

Makino, A. (1986). Topographic EEG analysis in relation to higher brain function. Tokushima

Journal of Experimental Medicine, 33, 59-68.

McGlone, J. (1978). Sex differences in functional brain asymmetry. Cortex, 14, 122-128.

McGlone, J. (1980). Sex differences in human brain asymmetry: A critical survey. Behavioral and

Brain Sciences, 3, 215-263.

Meneses-Ortega, S., & Corsi-Cabrera, M. (1990). Ultradian rhythms in the EEG and task

performance. Chronobiologia, 17, 183-194.

Miller, G.A., Lutzenberger, W., & Elbert, T. (1991). The linked-reference issue in EEG and ERP

recording. Journal of Psychophysiology, 5, 273-276.

Millett D. (2001). Hans Berger: from psychic energy to the EEG.

Perspectives in biology and medicine, 44, 522-42

Moruzzi, G., & Magoun, H.W. (1949). Brain stem reticular formation and activation of EEG.

Electroencephalography and clinical Neurophysiology, 1, 455-473.

Murakami S, Okada Y. (2006). Contributions of Principal Neocortical Neurons to

What is Quantitative EEG - Kaiser - 33

Magnetoencephalography (MEG) and Electroencephalography (EEG) Signals.

Journal of Physiology.

Niedermeyer, E. (1987). Maturation of the EEG: Development of waking and sleep

patterns. In: E. Niedermeyer and F. Lopes da Silva (Eds.), Electroencephalography: Basic Principles,

Clinical Applications and Related Fields (pp. 133-157). Urban and Schwarzenberg, Baltimore.

Nunez, P.L. (1981). Electric fields of the brain: The neurophysics of EEG. New York: Oxford

University Press.

Nuwer, M.R. (1988). Quantitative EEG: I. Techniques and problems of frequency analysis and topographic

mapping. Journal of Clinical Neurophysiology, 5, 1-43.

Okamoto M, Dan H, Sakamoto K, Takeo K, Shimizu K, Kohno S, Oda I, Isobe S, Suzuki T,

Kohyama K, Dan I. (2004). Three-dimensional probabilistic anatomical cranio-cerebral correlation via the

international 10-20 system oriented for transcranial functional brain mapping. Neuroimage, 21, 99-111.

Oldfield, R.C. (1971). The assessment and analysis of handedness, the Edinburgh Inventory.

Neuropsychologia, 9, 97-113.

Pakkenberg B, Pelvig D, Marner L, Bundgaard MJ, Gundersen HJ, Nyengaard JR, Regeur L.

(2003) Aging and the human neocortex. Experimental gerontology, 38, 95-99.

Peniston EG, Kulkosky PJ. (1989) Alpha-theta brainwave training and beta-endorphin levels in

alcoholics. Alcoholism, clinical and experimental research, 13, 271-9.

Pfurtscheller, G. (1977). Graphical display and statistical evaluation of event-related

desynchronization (ERD). Electroencephalography & Clinical Neurophysiology, 43, 757-760.

Pfurtscheller, G. (1986). Event-related desynchronization mapping: Visualization of cortical

activation patterns. In F.H. Duffy (Ed.) Topographic mapping of brain electrical activity (pp. 99- 111).

Boston: Butterworths.

Pfurtscheller, G. (1988). Mapping of event-related desynchronization and type of derivation.

Electroencephalography and Clinical Neurophysiology, 70, 190-193.

Pfurtscheller, G. (1989). Spatiotemporal analysis of alpha frequency components with the ERD technique.

Brain Topography, 2, 3-8.

Pfurtscheller, G. (1992). Event-related synchronization (ERS): An electrophysiological correlate

What is Quantitative EEG - Kaiser - 34

of cortical areas at rest. Electroencephalography & Clinical Neurophysiology, 83, 62-69.

Pfurtscheller, G., & Berghold, A. (1989). Patterns of cortical activation during planning of

voluntary movement. Electroencephalography and Clinical Neurophysiology, 72, 250-258.

Pfurtscheller, G., Flotzinger, D., Mohl, W., & Peltoranta, M. (1992). Prediction of the side of hand

movements from single-trial multi-channel EEG data using neural networks. Electroencephalography and

Clinical Neurophysiology, 82, 313-315.

Pfurtscheller, G., & Klimesch, W. (1990). Topographical display and interpretation of eventrelated

desynchronization during a visual-verbal task. Brain Topography, 3, 85-93.

Pfurtscheller, G., Maresh, H., & Schuy, S. (1977). Inter- and intra-hemispheric differences in the

peak frequency of rhythmic activity within the alpha band. Electro- encephalography and clinical

Neurophysiology, 42, 77-83.

Provins, K.A., & Cunliffe, P. (1972). The relationship between EEG activity and handedness.

Cortex, 8, 136-146.

Ray, W.J., & Cole, H.W. (1985). EEG alpha activity reflects attentional demands, and beta

activity reflects emotional and cognitive demands. Science, 228, 750- 752.

Rasmussen T, Milner B. (1977). The role of early left-brain injury in determining lateralization of

cerebral speech functions. Annals of the New York Academy of Sciences, 299, 355-69.

Rebert, C.S., & Low, D.W. (1978). Differential hemispheric activation during complex

visuomotor performance. Electroencephalography & Clinical Neurophysiology, 44, 724-734.

Rebert, C.S., & Mahoney, R. (1978). Functional cerebral asymmetry and performance III.

Reaction time as a function of task, hand, sex, and EEG asymmetry. Psychophysiology, 15, 9-16.

Remond, A., & Lairy, G.C. (1972). Handbook of EEG and clinical neurophysiology (6-161).

Amsterdam: Elsevier Scientific Publishing Co.

Shearer, D.E., Cohn, N.B., Dustman, R.E., & LaMarche, J.A. (1984). Electrophysiological

correlates of gender differences: A review. American Journal of EEG Technology, 24, 95-107.

Shepherd, R. (1982). EEG correlates of sustained attention: Hemispheric and sex differences.

Current Psychological Research, 2, 1-20.

Steriade, M., Gloor, P., Llinas, R.R., Lopes da Silva, F.H., & Mesulam, M.-M. (1990). Basic

What is Quantitative EEG - Kaiser - 35

mechanisms of cerebral rhythmic activities. Electroencephalography and Clinical Neurophysiology, 76,

481-508.

Steriade, M., & Llinas, R.R. (1988). The functional states of the thalamus and the associated

neuronal interplay. Physiological Reviews, 68, 649-742.

Sterman, M.B., Mann, C.A., Kaiser, D.A., & Suyenobu, B.Y. (1994). Multiband topographic EEG

analysis of a simulated visuomotor aviation task. International Journal of Psychophysiology, 16, 49-56.

Torello, M. (1989). Topographic mapping of EEG and evoked potentials in psychiatry: Delusions,

illusions, and realities. Brain Topography, 1, 157-174.

Trotman, S.C., & Hammond, G.R. (1979). Sex differences in task-dependent EEG asymmetries.

Psychophysiology, 16, 429-431.

Tucker, D.M. (1976). Sex differences in hemispheric specialization for synthetic visuospatial

functions. Neuropsychologia, 14, 447-454.

Zaidel, E., Aboitiz, F., Clarke, J., Kaiser, D., & Matteson, R. (in press). Sex differences in

interhemispheric language relations.

Endnotes: