Wavelet Transform of the EEG Reveals Differences in Low and High Gamma Responses to Elementary Visual Stimuli

Abstract

Multiunit electrophysiological studies indicate that oscillatory activity is common in the awake mammalian central nervous system. Synchronous 20–80 Hz oscillations, so called gamma rhythms, have been proposed as a possible fundamental physiological mechanism of binding neuronal activity underlying visual object recognition. The purpose of this study was to determine whether or not gamma band oscillatory activity in the human brain is modulated by attributes of elementary visual stimulation. The experiment was performed on 7 normal subjects. Sinusoidal gratings were presented over a range of spatial frequencies. Evoked potentials were recorded over 5 surface electrodes placed in a horizontal occipital chain across the back of the head. Discrete wavelet transform was performed on the first 200 msec following stimulus onset on the average data of 256 sweeps. Power was analyzed with ANOVA across conditions.

In our previous studies we have separated a “low” (14–28 Hz) and “high” (28–55 Hz) gamma band.* The current results indicate that both gamma bands to full-field stimulation have the highest power at the midline (inion) electrode to a spatial frequency of 5.5 cpd, which is the peak spatial frequency from foveal psychophysical data. However, the spatial frequency bandwidth is considerably narrower in the HG than in the LG band. Occipital spatial frequency tuning of the massed high gamma response is narrower than the tuning of individual cortical neurons. The bandwidth difference between low and high gamma band suggests that different frequency gamma range oscillations may represent not only different functional properties of visual processing, but may also reflect underlying differences in excitatory and postsynaptic inhibitory circuits shaping the contrast sensitivity of the human observer. Our study emphasizes the importance of elementary visual filter properties for gamma responses and the need to subdivide gamma frequency ranges according to functional properties.

Keywords

Discrete Wavelet Transform Electroencephalography Gamma Range Oscillatory Potentials Sinusoidal Gratings Spatial Frequency

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