Spectral Analysis of EEG Patterns in Normal Adults Kyoung Gyu Choi, M.D., Ph.D. Department of Neurology, Ewha Medical Research Center, Ewha Womans University Medical College, Background: Recently, the quantitative analysis of biological signals has been possible due to the development of digital machines, new mathematical theories including the nonlinear time series, chaotic and dynamic theories, and convenient statistical tools. The characteristics of biological signals however, can not analyzed solely through visual interpretation or judgement. The author statistically analyzed the EEG data of normal adults by using spectral analysis. Methods: Quantitative EEG analysis was performed in 5 normal adults in order to analyze differences between the left and right hemispheres and interelectrode differences of the power spectrum of variable frequency bands. The results were statistically analyzed using SPSS. Results: The spectral powers of the left frontal delta, the right parietal theta and left frontal alpha were significantly higher than the opposite sides. In the analysis of interelectrode difference, the temporal beta band power was significantly higher than the frontal areas. There were no significant interelectrode differences in other frequency bands. C o n c l u s i o n s: The results of this study show that there are differences between the visually perceived EEG patterns and the frequency domain analysis. These findings suggest that EEG waves on recording papers are the results of interference among variable frequency bands. The continued analysis of EEG by using nonlinear parameters will be beneficial as a powerful tool in understanding the neural network dynamic in the brain. J Kor Neurol Ass 17(3):384 ~ 388, 1999 Key Words : Spectral analysis, Power spectrum, Electroencephalograph Kyoung Gyu Choi, M.D. 384 Copyright 1999 by the Korean Neurological Association
J Kor Neurol Ass / Volume 17 / May, 1999 385
Table 1. Mean power spectrum value of each electrode. Low Delta Theta Alpha Beta Gamma High Total Fp1-F3.610.690.196.148.160.296.304 2.408 F3-C3.588.648.206.242.192.258.300 2.432 C3-P3.578.590.252.364.296.378.304 2.768 P3-O1.492.666.298.306.278.338.336 2.718 FP2-F4.622.640.186.128.134.230.250 2.186 F4-C4.452.796.288.248.264.418.352 2.818 C4-P4.510.598.294.312.248.310.256 2.590 P4-O2.507.560.286.374.268.302.290 2.590 FP1-F7.596.680.194.150.174.344.294 2.426 F7-T3.482.554.270.384.314.458.274 2.736 T3-T5.452.674.312.380.344.428.322 2.914 T5-O1.458.638.304.370.310.364.244 2.486 FP2-F8.676.632.206.130.132.278.256 2.290 F8-T4.532.574.288.288.250.400.292 2.626 T4-T6.482.674.344.354.288.416.318 2.870 T6-O2.468.634.348.430.318.348.250 2.602 Fz-Cz.608.606.286.290.232.368.260 2.046 Cz-Pz.566.572.270.230.216.328.264 2.454 T1-A2.674.676.204.172.184.244.301 2.458 T2-A2.550.620.254.242.280.380.328 2.650 Low : < 0. 5, Delta:0.5-4, Theta:4-8, Alpha:8-12, Beta:12-20 Gamma : 20-40, High: >40 Table 2. The result of paired t-test of power spectra between left and right side. Wave bands delta theta low alpha * : p<0.05 Electrodes (P-value) Fp1-F3 Fp2-F4(0.040)* C4-P4 C3-P3(0.033)* Fp2-F8 Fp1-F7(0.044)* F7-T3 F8-T4(0.040)* Table 3. The result of oneway-anova between each electrode. Wave bands beta * : p<0.05, **: p<0.01 Electrodes (P-value) Fp1-F3 T3-T5(0.022)* FP2-F4 F7-T3(0.028)* Fp2-F4 T3-T5(0.003)** FP2-F4 T6-O2(0.022)* FP2-F8 T3-T5(0.003)** FP2-F8 F7-T3(0.025)* FP2-F8 T6-O2(0.019)* FP2-F8 T5-O1(0.032)* Figure 1. Examples of the diagram of spectral analysis on electrode F2-F4 (a) and T3-T4 (b). 386 J Kor Neurol Ass / Volume 17 / May, 1999
01. Baker GL, Gollub JP. Chaotic Dynamics: an introduction. 2nd ed. Cambridge, Cambridge Univ. Press 1996;27-35. 02. Gevins AS, Remond A. Methods Analysis of Brain Electrical and Magnetic Signals. Handbook of Electroencephalography and Clinical Neurophysiology(vol.1). Amsterdam, Elsevier 1987;541-582. 03. Higgins JR. Fast Fourier transform: an introduction with some minicomputer experiments. Am. J. Phys. 1976;44: 766-773. 04.,.., 1997;65-81. 05. Niedermeyer E, Lopes da Silva. Electroencephalography, basic principles, clinical applications and related fields. 3rd. ed. Baltimore, Williams and Wilkins 1993:4-25. 06. Steriade M, Deschenes M. The thalamus as a neuronal oscillator. Brain Res. Rev. 1988;8:1-63. 07. McCormick DA, Prince DA. ACh induces burst firing in thalamic reticular neurons by activation of K conduc- J Kor Neurol Ass / Volume 17 / May, 1999 387
tances. Nature 1986;319:402-405. 08. Llinas RR, Grace AA, Tarom Y. In vitro neurons in mammalian cortical layer 4 exhibit intrinsic oscillatory activity in the 10- to 50-frequency range. Proc. Natl. Acad. Sci. U.S.A. 1991;88:897-901. 09. Konopacki J, Bland BH, MacIver M, Roth SH. Cholinergic theta rhythm in transected hippocampal slices: Independent CA1 and dentate generators. Brain Res. 1987;436:21-22. 10. Alonso A, Llinias R. Subthreshold theta-like rhythmicity in stellate cells of entorhinal cortex layer II. N a t u r e 1989;342:175-177. 11. Lopes da Silva FH, Vos JE, Mooibroek, Van Rotterdam A. Relative contribution of intracortical and thalamo-cortical processes in the generation of alpha rhythms, revealed by partial coherence analysis. Electroence-phalo - gr. Clin. Neurophysiol. 1980;50:449-456. 12. Pfurtscheller G, Flotzinger D, Neuper C. Differentiation between finger, toe and tongue movements in man based on 40EEG. Electroencephalogr. Clin. Neurophysiol. 1994;90:456-460. 13. Lopes da Silva FH. EEG analysis: Theory and practice, In Electroencephalography, Basic Principles and Clinical applications and Related Fields. 3rd. ed. Baltimore, Williams and Willkins 1993;1097-1123. 14.,,.. 1998; 9(1):67-72. 15.,,,.. 1998;25(2): 408-416. 16. Juhasz C, Kamondi A, Szirmai I. Spectral EEG analysis following hemispheric stroke. Acta. Neurol. Scand. 1997; 96:397-400. 17. Schmid RG, Tirsch WS, Reitmeir P. Correlation of developmental neurological findings with spectral analytical EEG evaluations in pre-school age children. Electroencephalogr. Clin. Neurophysiol. 1997;516-527. 388 J Kor Neurol Ass / Volume 17 / May, 1999