Wave separation versus bandpass filtering: a comparative non-linear analysis of brain α-EEG signals with and without psychotropic drug treatment

Attempts to demonstrate low-dimensional attractor behaviour in the analysis of electroencephalographic (EEG) signals meet with difficulties that in part stem from a departure from single-system dynamics. In order to address this problem, the α-waves can be extracted by digital filtering or by wave separation; these two techniques are compared in order to specify the conditions in which finite impulse response (FIR) bandpass filters can be used. The comparison was made using 18 EEG records of 3 min duration under resting conditions (6 subjects, 3 records per subject: prior to apomorphine administration, then 90 min and 150 min post-treatment). No presence of low-dimensional dynamic episodes in α-signals was observed without digital processing. Sixty 5 s sections showing attractor behaviour were found after filtering and twenty five 5 s sections after wave separation. The mean correlation dimension was calculated for each experimental condition and for 4 subjects, in order to observe the temporal profile of the drug. When attractors were found after wave separation, bandpass filtering then also showed attractor behaviour, with the same temporal profile. However, the reverse is not true: attractors were found after bandpass filtering that were not present after wave separation; in this case the results deserve confirmation, although the temporal profiles for all cases in which attractors were found after filtering remained comparable.

[1]  Roger Cerf,et al.  Attractor characterization from scaled doublet structures: simulations for small data sets , 1995, Biological Cybernetics.

[2]  S. Lal Apomorphine in the evaluation of dopaminergic function in man , 1988, Progress in Neuro-Psychopharmacology and Biological Psychiatry.

[3]  David Ruelle,et al.  Deterministic chaos: the science and the fiction , 1995 .

[4]  R. Cerf,et al.  Wave-separation in complex systems. Application to brain-signals , 1993 .

[5]  F. H. Lopes da Silva,et al.  Chaos or noise in EEG signals , 1995 .

[6]  E. Lorenz Deterministic nonperiodic flow , 1963 .

[7]  F. Takens Detecting strange attractors in turbulence , 1981 .

[8]  A. Babloyantz Strange Attractors in the Dynamics of Brain Activity , 1985 .

[9]  Roger Cerf,et al.  Trans-embedding-scaled dynamics , 1991 .

[10]  David Ruelle,et al.  Chemical Kinetics and Differentiable Dynamical Systems , 1981 .

[11]  Mark R. Muldoon,et al.  Linear Filters and Non‐Linear Systems , 1992 .

[12]  F. H. Lopes da Silva,et al.  Chaos or noise in EEG signals; dependence on state and brain site. , 1991, Electroencephalography and clinical neurophysiology.

[13]  J. Battey,et al.  Differential Gene Expression of CCKA and CCKB Receptors in the Rat Brain , 1993, Molecular and Cellular Neuroscience.

[14]  P. Grassberger,et al.  Measuring the Strangeness of Strange Attractors , 1983 .

[15]  D. Ruelle,et al.  The Claude Bernard Lecture, 1989 - Deterministic chaos: the science and the fiction , 1990, Proceedings of the Royal Society of London. A. Mathematical and Physical Sciences.

[16]  Theiler,et al.  Spurious dimension from correlation algorithms applied to limited time-series data. , 1986, Physical review. A, General physics.

[17]  Alan V. Oppenheim,et al.  Discrete-Time Signal Pro-cessing , 1989 .

[18]  Theodore R. Bashore,et al.  Experimental Studies of Chaotic Neural Behavior: Cellular Activity and Electroencephalographic Signals , 1986 .

[19]  D. Grandy,et al.  Molecular diversity of the dopamine receptors. , 1993, Annual review of pharmacology and toxicology.

[20]  A Carlsson,et al.  The current status of the dopamine hypothesis of schizophrenia. , 1988, Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology.

[21]  R. Cerf,et al.  Attractor-Ruled Dynamics in Neurobiology: Does it Exist? Can it be Measured? , 1993 .

[22]  James Theiler,et al.  Using surrogate data to detect nonlinearity in time series , 1991 .