Steady state topography

Steady State Topography (abbreviated SST) is a methodology for observing and measuring human brain activity that was first described by Richard Silberstein and co-workers in 1990. While SST has been principally used as a cognitive neuroscience research methodology it has also found commercial application in the field of neuromarketing and consumer neuroscience in such areas as brand communication, media research and entertainment. In a typical SST study, brain electrical activity (electroencephalogram or EEG) is recorded while participants view audio visual material and/or perform a psychological task. Simultaneously, a dim sinusoidal visual flicker is presented in the visual periphery. The sinusoidal flicker elicits an oscillatory brain electrical response known as the Steady State Visually Evoked Potential (SSVEP). Task related changes in brain activity in the vicinity of the recording site are then determined from SSVEP measurements at that site. One of the most important features of the SST methodology is the ability to measure variations in the delay (latency) between the stimulus and the SSVEP response over extended periods of time. This offers a unique window into brain function based on neural processing speed as opposed to the more common EEG amplitude indicators of brain activity. Three specific features of the SST methodology make it a useful technique in cognitive neuroscience research as well as neuroscience-based communication research.

1. High temporal resolution: the SST methodology is able to continuously track rapid changes in brain activity over an extended period of time. This is an important feature as many changes in brain function associated with a cognitive task can occur in less than a second.

2. High signal-to-noise ratio and resistance to interference and ‘noise’. The SST methodology is able to tolerate high levels of noise or interference due to such things as head movements, muscle tension, blinks and eye movements. This makes SST well suited to cognitive studies where eye, head and body movements occur as a matter of course.

3. The high signal-to-noise ratio means that it is possible to work with data based on a single trial per individual as opposed to the typical situation encountered in event-related potential (ERP) or event related fMRI studies where there is a need to average multiple trials recorded from each individual to achieve adequate signal-to-noise ratio levels.

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