In vision, investigation of the functional impact of brain oscillations has largely focused on attention. At a macroscopic level, this takes the form of oscillatory activity which can be recorded in humans by electroencephalography (EEG) or magnetoencephalography (MEG 5). There is growing evidence suggesting that functionally relevant neural communication takes the form of periodic synchronized firing by groups of neurons 1, 2, 3, 4. These findings and other aspects of the results validate random temporal sampling as a promising new method to study oscillatory visual mechanisms. Moreover, stimulus class can be reliably decoded from the power spectrum of individual classification images. Stimulus classes lead to distinct outcomes and the power spectra of classification images are highly generalizable across individuals. These classification images are also decomposed into their power and phase spectra.
Classification images reveal variations of perceptual effectiveness according to the temporal features of stimulus visibility. The technique is applied in visual recognition experiments using different stimulus classes (words, familiar objects, novel objects, and faces). This report describes the technique of random temporal sampling for the investigation of visual oscillatory mechanisms. Demonstrating the implications of this mode of operation for perceptual/cognitive function remains somewhat elusive. It is increasingly apparent that functionally significant neural activity is oscillatory in nature.
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