Methods of Digital Information Processing in Clinical Neurophysiology

• REFERENCES

• 1 Akaike H. Autoregressive model fitting for control. Ann. Inst. Statist. Math 1971; 23: 163-180.
• 2 Bodenstein G, Praetorius H. M. Feature extraction from the electroencephalogram by adaptive segmentation. Proc. IEEE 1977; 65: 642-653.
• 3 Cadzow J. A. High performance spectral estimation – A new ARMA method. IEEE Trans. Acoust, Speech, Signal Proc, ASSP 1980; 28: 524-529.
• 4 Cham Y. T, Wood J. C. A new order determination technique for ARMA processes. IEEE Trans. Acoust., Speech, Signal Proc, ASSP 1984; 32: 517-521.
• 5 Childers D. G, Bloom P. A, Arroyo A. A. et al. Classification of cortical responses using features from single EEG records. IEEE Trans, biomed. Engrg, BME 1982; 29: 423-438.
• 6 Coppola R, Tabor R, Buchsbaum M. S. Signal to noise ratio and response variability measurements in single trial evoked potentials. Electroenceph. clin. Neurophysiol 1978; 44: 214-222.
• 7 Coppola R. Non-linear analysis applied to human evoked potentials. IEEE Trans. Biomed. Engrg, BME 1981; 28: 588
• 8 Donchin E, Tueting P, Ritter W, Heffley E. On the independence of the CNV and the P300 components of the human averaged evoked potential. Electroenceph. clin. Neurophysiol 1975; 38: 449-461.
• 9 Doyle D. J. Some comments on the Wiener filtering for the estimation of evoked potentials. Electroenceph. clin. Neurophysiol 1975; 38: 533-535.
• 10 Dumermuth J, Flühler H. Some modern aspects in numerical spectrum analysis of multichannel electroencephalographic data. Med. biol. Engrg J 1967; 319-331.
• 11 Duquesnoy A. J. Analysis of non-stationary electroencephalograms using a modified Kaiman filter. Utrecht: Institute of Medical Physics, TNO; 1975
• 12 Ferber G, Künkel H. Die Bedeutung der automatischen EEG-Analyse für die klinische EEG-Befundung. Wiss. Zeitschrift der Ernst-Moritz-Arndt-Universität Greifswald, Jahrgang XXVIII, Med. Reihe 1979; 1: 25-36.
• 13 Ferber G. Verfahren zur automatischen Beschreibung des EEG im Rahmen der klinischen Diagnostik. Diss. Univ. Hannover. 1983
• 14 Friedmann Y, Carmon A. Analysis of single cerebral evoked responses by adaptive filtering. Transact. San Diego biomed. Symp 1979; 233-238.
• 15 Gaillard A. W, Näätänen R. The effect of background slow potential shifts on event-related potentials. In Kornhuber H. H, Deecke L. (Eds.) Motivation, Motor and Sensory Processes of the Brain: Electrical Potentials, Behaviour and Clinical Use,. Amsterdam: Elsevier/ North-Holland, Biomedical Press; 1980: 364-367.
• 16 Gersch W. Spectral analysis of EEG’s by autoregressive decomposition of time series. Math. Biosc 1970; 7: 205-222.
• 17 Heinze H. J, Künkel H. ARMA-filte-ring of evoked potentials. Meth. Inform. Med 1984; 23: 29-36.
• 18 Heinze H. J, Künkel H. Evaluation of single epoch evoked potentials. Neuro-psychobiology 1984; 12: 68-72.
• 19 Hinrichs H. Quellencodierung zur effizienten Speicherung klinischer EEG-Signale. Diss. Univ. Hannover. 1984
• 20 Horst R. L, Donchin E. Beyond averaging IL Single trial classification of exogenous event related potentials using stepwise discriminant analysis. Electroen-ceph. clin. Neurophysiol 1980; 48: 113-126.
• 21 Hung G, Stark L. The kernel identification method (1970-1977) – review of theory, calculation, application, and interpretation. Math. Biosc 1977; 37: 135-190.
• 22 Kay S. M. A new ARMA spectral estimator. IEEE Trans. Acoust., Speech, Signal Proc. ASSP 1980; 28: 585-588.
• 23 Krausz H. I. Identification of nonlinear systems using random impulse train inputs. Biol. Cybernetics 1975; 19: 217-230.
• 24 Künkel H. Die Spektraldarstellung des EEG. EEG-EMG 1972; 3: 15-24.
• 25 Künkel H. Simultane Vielkanal-on-line-EEG-Analyse in Echtzeit. EEG-EMG 1972; 3: 29-38.
• 26 Lee Y. W, Schetzen M. Measurement of the Wiener kernels of a nonlinear system by cross-correlation. Int. J. Control 1965; 2: 237-254.
• 27 Lehmann D. Räumliche Analyse II: Spontan-EEG. Paper presented at the Deutsche EEG-Gesellschaft, 29. Jahrestagung Düsseldorf. 1984
• 28 Lopes da Silva F. H, Dijk A, Smits H. Detection of non-stationarities in EEGs using the autoregressive model – An application to EEGs of epileptics. In. Dolce G, Künkel H. (Eds) CEAN-computeri-zed EEG analysis. Stuttgart: Gustav Fischer Verlag; 1975
• 29 McGillem C. D, Aunon J. I. Measurements of signal components in single visually evoked brain potentials. IEEE Trans. biomed. Engrg BME 1977; 13: 232-241.
• 30 Parzen E. Some recent advances in time series modeling. IEEE Trans. Aut. Contr 1974; 19: 723-730.
• 31 Peters J. F, Knott J. R, Hamilton C. E. Further thoughts on measurement of the CNV. In. McCallum W. C, Knott J. R. (Eds.) The Responsive Brain. Bristol: Wright; 1976
• 32 Praetorius H. M, Bodenstein G, Creutzfeldt O. D. Adaptive segmentation of EEG records: A new approach to automatic EEG analysis. Electroenceph. clin. Neurophysiol 1977; 42: 84-94.
• 33 Reinis S, Landolf J. P, Weiss D. S. Cluster analysis of visual cortical responses evoked by moving lines. Electroenceph. clin. Neurophysiol 1984; 59: 195-203.
• 34 Reitz Cortical potentials in man evoked by noise modulated light. Ph. D. Diss. Univ. of Amsterdam; 1975
• 35 Senmoto S, Childers D. G. Adaptive decomposition of a composite signal of identical unknown wavelets in noise. IEEE Trans. System, Man, Cybernetics SMC 1972; 2: 59-66.
• 36 Squires K. C, Donchin E. Beyond averaging: The use of discriminant functions to recognize event-related potentials elicited by single auditory stimuli. Electroenceph. clin. Neurophysiol 1976; 41: 449-553.
• 37 Walter D. O. A posteriori “Wiener filtering” of average evoked responses. Electroenceph. clin. Neurophysiol. Suppl 1969; 27: 61-70.
• 38 Wiener N. Extrapolation, Interpolation and Smoothing of Time Series. New York: Wiley and Sons; 1949
• 39 Wiener N. Nonlinear Problems in Random Theory. New York: Wiley; 1958
• 40 Woody C. D. Characterisation of an adaptive filter for the analysis of variable latency neuroelectric signals. Med. biol. Engrg 1967; 5: 539-553.