Steady state topography

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In neuroimaging, steady state topography (SST) is a methodology for observing and measuring human brain activity that was first described by Richard Silberstein and co-workers in 1990. [1] 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.

Contents

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). [2] [3] 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. [4] 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. [4] [5] 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 [1] 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.

Main paradigm

In applying the SST methodology audio visual material is presented simultaneously with a peripheral, spatially diffuse visual flicker [4] [6] and Fourier techniques are used to extract the amplitude and the phase of the SSVEP at the stimulus frequency. When the stimulus frequency is in the alpha frequency range (8 Hz – 13 Hz), the SSVEP can be recorded from the occipital region and also from other 'non-visual' regions such as the frontal and prefrontal cortex and the temporal and parietal cortex. [4] [7] [8] Most SST studies use a visual stimulus in the upper alpha frequency range (10 Hz – 13 Hz) or gamma frequency range (30 Hz – 100 Hz) to elicit the SSVEP. [9] [10] Changes in the SSVEP amplitude and phase coinciding with a cognitive task or other material such as a television advertisement are then interpreted as changes in regional brain activity associated with the cognitive task. SSVEP amplitude changes are interpreted in a similar fashion to changes in upper alpha EEG amplitude while changes in SSVEP phase are expressed as changes in SSVEP latency. An SSVEP latency reduction is interpreted physiologically as increased synaptic excitation in the neural networks generating the SSVEP implying increased regional brain activity and vice versa. [9]

Scientific and biomedical applications

The SST methodology has been used to examine normal brain function associated with visual vigilance, [1] [10] working memory, [11] [12] long-term memory, [13] [14] emotional processes, [5] [15] [16] as well as disturbed brain functions such as schizophrenia [9] [17] and attention deficit hyperactivity disorder [6]

Commercial applications

The SST methodology has been applied commercially in areas such as consumer neuroscience, neuromarketing, media and entertainment research. In this application area SST is used to measure second by second changes in brain activity associated with a wide range of communication media. By measuring brain activity at a number of scalp locations it is possible to estimate second by second changes in a number of relevant psychological parameters including, Long-term Memory Encoding, Engagement (sense of personal relevance), Motivational Valence (whether the material attracts or repels the viewer) as well as Emotional Intensity (arousal) and Visual Attention. Research indicates that a major SST indicator of advertising effectiveness is the level of long-term memory encoding of the key message or the brand in the advertisement. [5] [13] [16] [18] [19]

Twitter inc famously used SST technology to explore and test the power of the platform.

Related Research Articles

An evoked potential or evoked response is an electrical potential in a specific pattern recorded from a specific part of the nervous system, especially the brain, of a human or other animals following presentation of a stimulus such as a light flash or a pure tone. Different types of potentials result from stimuli of different modalities and types. Evoked potential is distinct from spontaneous potentials as detected by electroencephalography (EEG), electromyography (EMG), or other electrophysiologic recording method. Such potentials are useful for electrodiagnosis and monitoring that include detections of disease and drug-related sensory dysfunction and intraoperative monitoring of sensory pathway integrity.

<span class="mw-page-title-main">Functional neuroimaging</span>

Functional neuroimaging is the use of neuroimaging technology to measure an aspect of brain function, often with a view to understanding the relationship between activity in certain brain areas and specific mental functions. It is primarily used as a research tool in cognitive neuroscience, cognitive psychology, neuropsychology, and social neuroscience.

<span class="mw-page-title-main">Brain–computer interface</span> Direct communication pathway between an enhanced or wired brain and an external device

A brain–computer interface (BCI), sometimes called a brain–machine interface (BMI) or smartbrain, is a direct communication pathway between the brain's electrical activity and an external device, most commonly a computer or robotic limb. BCIs are often directed at researching, mapping, assisting, augmenting, or repairing human cognitive or sensory-motor functions. They are often conceptualized as a human–machine interface that skips the intermediary component of the physical movement of body parts, although they also raise the possibility of the erasure of the discreteness of brain and machine. Implementations of BCIs range from non-invasive and partially invasive to invasive, based on how close electrodes get to brain tissue.

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Beta waves, or beta rhythm, are a neural oscillation (brainwave) in the brain with a frequency range of between 12.5 and 30 Hz. Beta waves can be split into three sections: Low Beta Waves ; Beta Waves ; and High Beta Waves. Beta states are the states associated with normal waking consciousness.

<span class="mw-page-title-main">Neural oscillation</span> Brainwaves, repetitive patterns of neural activity in the central nervous system

Neural oscillations, or brainwaves, are rhythmic or repetitive patterns of neural activity in the central nervous system. Neural tissue can generate oscillatory activity in many ways, driven either by mechanisms within individual neurons or by interactions between neurons. In individual neurons, oscillations can appear either as oscillations in membrane potential or as rhythmic patterns of action potentials, which then produce oscillatory activation of post-synaptic neurons. At the level of neural ensembles, synchronized activity of large numbers of neurons can give rise to macroscopic oscillations, which can be observed in an electroencephalogram. Oscillatory activity in groups of neurons generally arises from feedback connections between the neurons that result in the synchronization of their firing patterns. The interaction between neurons can give rise to oscillations at a different frequency than the firing frequency of individual neurons. A well-known example of macroscopic neural oscillations is alpha activity.

Theta waves generate the theta rhythm, a neural oscillation in the brain that underlies various aspects of cognition and behavior, including learning, memory, and spatial navigation in many animals. It can be recorded using various electrophysiological methods, such as electroencephalogram (EEG), recorded either from inside the brain or from electrodes attached to the scalp.

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In neurology and neuroscience research, steady state visually evoked potentials (SSVEP) are signals that are natural responses to visual stimulation at specific frequencies. When the retina is excited by a visual stimulus ranging from 3.5 Hz to 75 Hz, the brain generates electrical activity at the same frequency of the visual stimulus.

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Consumer neuroscience is the combination of consumer research with modern neuroscience. The goal of the field is to find neural explanations for consumer behaviors in individuals both with or without disease.

The P3a, or novelty P3, is a component of time-locked (EEG) signals known as event-related potentials (ERP). The P3a is a positive-going scalp-recorded brain potential that has a maximum amplitude over frontal/central electrode sites with a peak latency falling in the range of 250–280 ms. The P3a has been associated with brain activity related to the engagement of attention and the processing of novelty.

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<span class="mw-page-title-main">Brain activity and meditation</span>

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<span class="mw-page-title-main">High-frequency oscillations</span>

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<span class="mw-page-title-main">Alexander T. Sack</span>

Alexander T. Sack is a German neuroscientist and cognitive psychologist. He is currently appointed as a full professor and chair of applied cognitive neuroscience at the Faculty of Psychology and Neuroscience at Maastricht University. He is also co-founder and board member of the Dutch-Flemish Brain Stimulation Foundation, director of the International Clinical TMS Certification Course, co-director of the Center for Integrative Neuroscience (CIN) and the Scientific Director of the Transcranial Brain Stimulation Policlinic at Maastricht University Medical Centre.

References

  1. 1 2 3 Silberstein, R. B., Schier, M. A., Pipingas, A., Ciorciari, J., Wood, S. R. and Simpson D. G. (1990) Steady state visually evoked potential topography associated with a visual vigilance task. Brain Topography 3: 337-347.
  2. ↑ Regan, D., (1989). Human Brain Electrophysiology: Evoked Potentials and Evoked Magnetic Fields in Science and Medicine. Elsevier, New York.
  3. ↑ Vialatte, F, Maurice, M, Dauwels, J., Cichocki, A. (2010) Steady-state visually evoked potentials: Focus on essential paradigms and future perspectives. Progress in Neurobiology 90: 418–438.
  4. 1 2 3 4 Silberstein, R. B. (1995) Steady state visually evoked potentials, brain resonances and cognitive processes. In P. L. Nunez. Neocortical dynamics and human EEG rhythms. Oxford University Press. New York. 1995 pp 272-303.
  5. 1 2 3 Gray M, Kemp AH, Silberstein RB, Nathan PJ (2003) Cortical neurophysiology of anticipatory anxiety: an investigation utilizing steady state probe topography (SSPT). Neuroimage. 20:975-986.
  6. 1 2 Silberstein, R. B., Farrow, M. A., Levy, F, Pipingas, A., Hay, D. A., Jarman, F.C.(1998). Functional brain electrical; activity mapping in boys with attention deficit hyperactivity disorder. Archives of General Psychiatry 55:1105-12.
  7. ↑ Silberstein, R. B., Ciorciari, J. and Pipingas, A. (1995) Steady-state visually evoked potential topography during the Wisconsin card sorting test. EEG and Clin. Neurophysiol. 96: 24-35.
  8. ↑ . Srinivasan, R., Bibi, F.A., Nunez, P.L., (2006) Steady-state visual evoked potentials: distributed local sources and wave-like dynamics are sensitive to flicker frequency. Brain Topogr. 18 (3), 167–187.
  9. 1 2 3 Silberstein, R. B., Line, P., Pipingas, A., Copolov, D., Harris, P. (2000) Steady-state visually evoked potential topography during the continuous performance task in normal controls and schizophrenia. Clinical Neurophysiology. 111:850-857.
  10. 1 2 Nield, G., Silberstein R. B., Pipingas, A., Simpson, D. G. and Burkitt, G.(1998) Effects of visual vigilance task on gamma and alpha frequency range steady state potential (SSVEP) topography. Brain Topography Today. Eds Y. Koga, K. Nagata & H. Hirata. Elsevier Science. pp189-194.
  11. ↑ Silberstein RB, Nunez PL, Pipingas A, Harris P, Danieli F. (2001) Steady state visually evoked potential (SSVEP) topography in a graded working memory task. International Journal of Psychophysiology. 42:125-38.
  12. ↑ . Ellis KA, Silberstein RB, Nathan PJ. (2006) Exploring the temporal dynamics of the spatial working memory n-back task using steady state visual evoked potentials (SSVEP) Neuroimage. 31:1741-51.
  13. 1 2 Silberstein, R. B., Harris, P. G., Nield, G. A., Pipingas, A. (2000) Frontal steady-state potential changes predict long term recognition memory performance. International Journal of Psychophysiology. 39:79-85.
  14. ↑ Macpherson, H, Pipingas, A, Silberstein, R B. (2009) A steady state visually evoked potential investigation of memory and ageing. Brain and Cognition. 69:571 – 579.
  15. ↑ Kemp AH, Gray MA, Eide P, Silberstein RB, Nathan PJ. (2002) Steady-state visually evoked potential topography during processing of emotional valence in healthy subjects. Neuroimage. 17:1684-92.
  16. 1 2 Kemp A., Gray M., Silberstein R.B., Nathan P.J. (2004). Augmentation of serotonin enhances pleasant and suppresses unpleasant electrophysiological responses to visual emotional stimuli. Neuroimage. 22:1084-96.
  17. ↑ Line, P, Silberstein, R B, Wright, JJ and Copolov D.(1998) Steady State Visually Evoked Potential Correlates of Auditory Hallucinations in Schizophrenia. Neuroimage. 1998;.8:370-376.
  18. ↑ Rossiter, J. R., Silberstein, R. B., Harris, P. G., Nield, G. (2001) Brain-imaging detection of visual scene encoding in long-term memory for TV commercials. Journal of Advertising Research. 41: 13-21.
  19. ↑ Silberstein, R.B. Nield, G.E. (2008) Brain activity correlates of consumer brand choice shift associated with television advertising. Int. J. Advertising. 2008; 27: 359 – 380
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