Abstract
An adaptive filter system has been developed whereby variable latency neuroelectric signals may be detected and separated from associated noise. The system is based on correlation-averaging techniques which are described in detail. The adaptive property of the system derives from iterative correlation and averaging of the data signals and permits the recognition of signals the specific waveshapes of which are not known in advance.
The system consitutes a general pattern recognition device. It has been shown to be applicable to the analysis of evoked potentials of variable latency as well as to the analysis of patterns of EEG activity. Further applications include the analysis of multiple, complex signals such as miniature potentials from single motor units in the spinal cord. The use of an adaptive filter such as this with convergence properties based on broad statistical considerations appears to have greater analytic power than have previous methods.
Sommaire
On a développé un filtre adapté à l’aide duquel il devient possible de détecter et de sortir du bruit de fond qui les accompagne, des signaux neurologique de latence variable. Le système est fondé sur les techniques de corrélation et de moyennage qui sont décrites en détail. La faculté d’adaptation du système découle de la corrélation itérative et du moyennage du signal de mesure et permet la reconnaissance de signaux dont la forme caractéristique n’est pas connue à l’avance.
Le système constitue un appareil universel pour la reconnaissance de forme. Il a été pour l’analyse de potentiels évoqués de latence variable et est également utilisable pour l’analyse des signaux de E.E.G. Il peut également être utilisé pour l’étude de signaux multiples et complexes tels que les potentiels d’unités motrices uniques dans la moelle épinière. Un tel filtre, dont les propriétés sont fondées sur de profondes considérations d’ordre statistique, parait avoir une puissance d’analyse supérieure à toute autre méthode antérieure.
Zusammenfassung
Ein anpassungsfähiges Filtersystem wurde entwickelt. Neuroelektrische Signale variabler Latenz können erkannt und von begleitendem Rauschen getrennt werden. Das System beruht auf Korrelations-Methoden, die in Einzelheiten geschildert werden. Die Anpaussngseigenschaft des Systems geht auf iterative Korrelation und Mittel-ertsbildung der Signaldaten zurück und erlaubt die Erkennung von Signalen, deren spezifische Wellenform im voraus nicht bekannt ist.
Das System stellt stellt ein allgemeines Mustererkennungsgerät dar. Die Anwendbarkeit auf die Analyse von ausgelösten Potentialen variabler Latenz und auf die Analyse von Mustern der EEG-Aktivität ist erwiesen. Weitere Anwendungsmöglichkeiten leigen in der Analyse mutipler, komplexer Signale wie Miniaturpotentiale von einzelnen motorischen Einheiten im Rückenmark. Anpassungsfähige Filter wie der beschriebene haben mit ihren Konvergenzeigenschaften, die auf umfassenden statistischen Betrachtungen basieren, größere analytische Möglichkeiten als die bisherigen Methoden.
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References
Barlow, J. S. andBrown, R. M. (1955) An analog correlator for brain potentials.Tech. Rep. 300, pp. 1–41. Research Laboratory of Electronics, M.I.T., Cambridge, Mass.
Casby, J. V., Siminoff, R. andHouseknecht, T. R. (1963) An analogue cross-correlator to study naturally induced activity in intact nerve trunks.J. Neurophysiol. 26, 432–448.
Communications Biophysics Group andSiebert, W. M. (1959).Processing Neuroelectric Data. Technology Press, Cambridge, Mass.
Cooley, J. W. andTukey, J. W. (1965) An algorithm for the machine calculation of complex fourier series.Math. Comput. 19, 297–301.
Davenport, W. B. andRoot, W. L. (1958)An Introduction to the Theory of Random Signlas. McGraw-Hill, New York.
Davisson, L. D. (1966) A theory of adaptive filtering.IEEE Trans. Info. Theory IT-12, 97–102.
Dern, H. andWalsh, J. B. (1963) InPhysical Techniques in Biological Research, Vol. VI (Edited byW. L. Nastuk), pp. 99–218. Academic Press, New York.
Gilden, L., Vaughan, Jr., H. G. andCosta, L. D. (1966) Summated human EEG potentials with voluntary movement.EEG clin. Neurophysiol. 20, 433–438.
Goldstein, Jr., M. H. andWeiss, T. T. (1959) InProcessing Neuroelectric Data (Edited byCommunications Biophysics Group of Research Laboratory of Electronics andW. M. Siebert), pp. 88–98. Technology Press, Cambridge, Mass.
Groginsky, H. L., Wilson, L. R. andMiddleton, D. (1966) Adaptive detection of statistical signals in noise.IEEE Trans. info. Theory IT-12, 337–347.
Hiltz, F. LeB. (1966) Cortical electrical response during a differential visual discrimination in the cat. Ph.D. Thesis (unpublished). Cornell University, Ithaca.
Jakowatz, C. V., Shuey, R. L. andWhite, G. M. (1961) InFourth London Symposium on Information Theory (Edited byC. Cherry), pp. 317–326. Butterworths, London.
Lee, Y. W. (1960)Statistical Theory of Communication. John Wiley, New York.
O’Leary, J. L. andBishop, G. H. (1938) The optically excitable cortex of the rabbit.J. comp. Neurol. 68, 423–478.
Regan, D. (1966) An apparatus for the correlation of evoked potentials and repetitive stimuli.Med. biol. Engng. 4, 169–177.
Rushworth, G. (1962) Observations on blink reflexes.J. neurol. Neurosurg. Psychiat. 25, 93–108.
Siebert, W. M. (1959) InProcessing Neuroelectric Data (Edited byCommunications Biophysics Group of Research Laboratory of Electronics andW. M. Siebert), pp. 66–87. Techology Press, Cambridge, Mass.
Stark, L. et al. (1961) Pattern recognition for electroencephalographic diagnosis.M.I.T., Res. Lab. Elect., QPR. 61, 215–219.
Turner, R. D. (1961) Operations research on recognition. Res. Publ. Af 19 (604)-6103, General Electric Co., Ithaca. (also ASTIA AD 272939).
Turner, R. D. (1964) Energy priming adaptive filter. Res. Publ. R64 ELC 67, General Electric Co., Ithaca.
Woody, C. D. (1962) Some aspects of information processing in the CNS. Honors Thesis (unpublished). Harvard Medical School, Boston.
Woody, C. D., Bello, R. D. andErvin, F. R. (1964) InData Acquisition and Processing in Biology and Medicine, Vol. 2 (Edited byK. Enslein), pp. 219–223, Pergamon Press, Oxford.
Woody, C. D. andSheriff, Jr., W. H. (1965) Computer analysis of non stimulus-locked neuroelectric signals.Proc. 18th ACEMB 7, 200.
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Woody, C.D. Characterization of an adaptive filter for the analysis of variable latency neuroelectric signals. Med. & biol. Engng. 5, 539–554 (1967). https://doi.org/10.1007/BF02474247
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DOI: https://doi.org/10.1007/BF02474247