On-line analysis of EEG with a small computer (period-amplitude analysis)

Legewie, Heiner; Probst, Wolfgang

doi:10.1016/0013-4694(69)90194-1

Cited by 38 publications

(4 citation statements)

References 3 publications

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“…There are several methods for computer analysis of the EEG signal (Valter 1963, Legewie andProbst 1969, Pfurtscheller andHaying 1969). Of particular importance is application of the fast Fourier transform (FFT) (Cooley and Tukey 1965) to the spectral analysis of the EEG (Dumermuth and Fluhler 1967) since it considerably shortens the analysis time and allows an almost on-line spectral analysis that is very important for routine EEG diagnosis.…”

Section: Introductionmentioning

confidence: 99%

EEG Frequency Spectrum Characteristics of Sleep States in Full-Term and Preterm Infants¹

1975

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ManitobaHavlicek, V., Childiaeva, R. and Chernick, V.: EEG frequency spectrum characteristics of sleep states in full-term and preterm infants. Neuropadiatrie 6: 24-40 (1975). Spectral analysis of the EEG using fast Fourier transform in preterm and full-term infants revealed significant maturational changes in all three stages of sleep. In quiet sleep full-term infants differed from preterm babies (< 37 weeks gestation) by significantly higher power in delta and theta frequencies (1.6-7.5 Hz). During indeterminate sleep the slowest frequencies (0.1--1.5 Hz) showed significantly less power, while power of faster frequencies (1.6-7.5 Hz) was significantly greater in full-term infants. REM sleep differed in slowest (0.1-1.5 Hz) and fastest (17.5-25 Hz) frequency bands, showing significantly lower power in the former and significantly higher in the latter in full-term infants. From the diagnostic point of view the most important maturational EEG changes were found by computing the REM sleep-quiet frequency spectrum difference. This showed significantly higher power during quiet sleep over a wide range of frequencies (0.1-12.5 Hz) in fullterm infants, while preterm infants had an almost identical spectrum during these two stages of sleep with a significant intensity drop only in theta2 (5.6-7.5 Hz) during REM sleep. Since these differences can be readily identified in the individual infant it is suggested that the assessment of the EEG may be useful for the early detection of delays in maturation. Brain maturation EEG spectrum sleep full-term infants premature infantsIntroduction infants have been described by DreyfusThe physiology and pathology of Brisac et al -(1956), Dreyfus-Brisac sleep in infants has attracted increasing (1962), Parmelee et al. (1968). These interest recently. Since infants and authors were able to predict the postyoung children spend most of the time conceptional age of the infant on the sleeping the EEG and polygraphic re-basis of visual inspection of the EEG. cording during sleep have become a new These and other studies confirmed the important diagnostic and research tool, dualistic nature of adult sleep discoverThe maturational changes of the EEG edbyAserinskyandKleitman (1953). during sleep in premature and full-term By means of the polygraphic record

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Section: Introductionmentioning

confidence: 99%

EEG Frequency Spectrum Characteristics of Sleep States in Full-Term and Preterm Infants¹

1975

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“…EEG data were reduced from analogue tape on the laboratory computer and organized into frequency distributions. A program based on models analyzing zero-line crossings (Legewie & Probst, 1969; Whitmore & Gordon, Note 2) was used to determine the percentage of crossings within the traditional EEG frequency bands.…”

Section: Methodsmentioning

confidence: 99%

Strategies of arousal control: Biofeedback, meditation, and motivation.

Cuthbert¹,

Kristeller²,

Simons³

et al. 1981

Journal of Experimental Psychology: General

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A series of four experiments assessed the effects of instructions to lower heart rate on heart rate change and general arousal reduction. Various conditions of biofeedback, cognitive load, incentive, knowledge of results, and the experimenter-subject relationship were tested. Experiment 1 compared physiological responses to the delivery of direct organ feedback (i.e., heart rate) with responses to electromyographic biofeedback from the frontalis muscle area and with responses to a nonfeedback tracking task. The results suggest that neither heart rate nor muscle tension feedback is an especially powerful method for achieving sustained reductions in heart rate. Furthermore, although some specificity of physiologic pattern is apparent, biofeedback is no more effective in lowering general activation level than simple instructions to relax accompanied by a general knowledge of results. The second experiment was designed to assess the role of cognitive load in arousal reduction. Heart rate biofeedback was compared with a procedure involving minimal external information processing--the secular meditation exercise of Wallace and Benson. The results indicated a clear superiority for the meditation strategy in effecting reductions in cardiac rate and lowering activation. However, in a third experiment, meditation subjects lowered heart rate much less than observed in the previous study, and this time the reduction did not exceed that achieved by feedback subjects. Subsequent analysis suggested that the quality of the subject-experimenter relationship (active-supportive vs. formal-distant) was a significant variable in accounting for outcome differences. The above hypothesis was supported by a fourth experiment. Under conditions of high subject-experimenter involvement, the superior meditation performance of Experiment 2 was reproduced; under low-involvement conditions the Experiment 3 result of no difference between training groups was obtained. The findings suggest that the effectiveness of any method for achieving relaxation (or physiological control) rests on a complex interaction between informational and motivational imperatives of the stimulus context and definable aspects of the interpersonal exchange between subject and experimenter. This research raises serious questions about the effectiveness of the usual biofeedback paradigm as an aid to arousal reduction and the cost efficiency of its applications in the clinical situation. Furthermore, these results demonstrate the great power in relaxation experiments of psychosocial and other moderator variables, and signal the practical difficulty of their control when these variables appear to be as potent in changing physiology as the primary training methods.

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“…In other applications of computers in biofeedback, Legewie and Probst (1969) used the computer (PDP-8L) to develop a new method of period-amplitude analysis of EEG activity. Beatty (1971) used a digital computer to conduct all phases of an experiment on the differential effects of response-contingent feedback vs. noncontingent feedback in the operant conditioning of alpha and beta wave activity.…”

Section: Recent Research Inautomated Biofeedback Systemsmentioning

confidence: 99%

Computers in biofeedback research and therapy

Kolotkin

Billingham

Feldman

1981

Behavior Research Methods & Instrumentation

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On-line analysis of EEG with a small computer (period-amplitude analysis)

Cited by 38 publications

References 3 publications

EEG Frequency Spectrum Characteristics of Sleep States in Full-Term and Preterm Infants¹

EEG Frequency Spectrum Characteristics of Sleep States in Full-Term and Preterm Infants¹

Strategies of arousal control: Biofeedback, meditation, and motivation.

Computers in biofeedback research and therapy

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On-line analysis of EEG with a small computer (period-amplitude analysis)

Cited by 38 publications

References 3 publications

EEG Frequency Spectrum Characteristics of Sleep States in Full-Term and Preterm Infants1

EEG Frequency Spectrum Characteristics of Sleep States in Full-Term and Preterm Infants1

Strategies of arousal control: Biofeedback, meditation, and motivation.

Computers in biofeedback research and therapy

Contact Info

Product

Resources

About

EEG Frequency Spectrum Characteristics of Sleep States in Full-Term and Preterm Infants¹

EEG Frequency Spectrum Characteristics of Sleep States in Full-Term and Preterm Infants¹