Among the various spectral analysis techniques, Fourier transform (FT) is traditionally the preferred method because it is time-shift invariant in both the time and frequency domains.
However, induced activity can be detected using spectral analysis, in which EEG recordings are decomposed into a number of frequency (sinusoidal) components, such as delta (0-3Hz), theta (4-7Hz), alpha (8-12Hz), beta (12-30 Hz), gamma (30-50 Hz), and high gamma (80-150 Hz). gamma oscillations), which might have a different phase in each single measurement and therefore would cancel one another in time-locked averaging.
An example of induced activity is oscillatory activity (e.g. These responses are called induced activity. In addition to time-locked responses, there may also be signals in the EEG that are related to stimulus processing without a well-defined temporal relation to the event. Time-locked averaging can be either stimulus-locked or response-locked. Because most noise occurs randomly, time-locked averaging techniques can greatly reduce the noise while preserving the event-related signals in the EEG. Time-locked averaging techniques are usually used to detect evoked activities, which are time-locked to the presentation of stimuli. The major techniques to detect event-related potentials can be divided into two categories, time-locked averaging techniques and spectral analysis techniques. To detect these low-amplitude potentials against the ongoing background EEG, EKG (cardiac artifacts), EMG (muscle activation artifacts) and other biological signals and ambient noise, repeated stimulus presentations and signal processing techniques (e.g., averaging) are required in ERP studies. The amplitudes of ERPs tend to be low, ranging from less than a microvolt to several microvolts, compared to tens of microvolts for spontaneous EEG. By combining with resting-state fMRI, generators of spontaneous EEG activities can be localized ( Salek-Haddadi, Friston, Lemieux, & Fish, 2003).Įvent-related potentials (ERPs) are associated with specific stimuli or thoughts. In addition, spontaneous EEG may hold the key to unraveling the patterns of functional connectivity and synchronicity among brain regions underlying the states of consciousness (also known as the default network) ( Mantini, Perrucci, Del Gratta, Romani, & Corbetta, 2007). Recently, there are growing interests in examining how the background brain activities as measured by spontaneous EEG affect current cognitive activities ( Ergenoglu et al., 2004 Romei et al., 2008). Spontaneous EEG has long been used in clinical settings to evaluate seizure disorders, and has not been used often in cognitive neuroscience research (but see Williamson, Kaufman, Lu, Wang, & Karron, 1997). Spontaneous EEG reflects neuronal responses that occur unprovoked, i.e., in the absence of any identifiable stimulus, with or without behavioral manifestations. The recorded EEG signals usually reflect two types of brain activities, spontaneous and event-related activities. Scalp EEG represents the aggregates of post-synaptic currents of millions of neurons. Most EEG systems used in cognitive neuroscience research today employ 64 to 256 electrodes. Usually, EEG is collected from tens to hundreds of electrodes positioned on different locations on the scalp. Finally, we discuss challenges and future directions in neuroeconomics.įirst discovered about a century ago, EEG measures electrical activities of the brain from electrodes placed on the scalp. We then present several studies on risky decision making, intertemporal choice, and social decision making, to illustrate how neuroimaging techniques can be used to advance our knowledge on decision making. In this article, we first provide an overview of brain imaging techniques, focusing on the recent developments in multivariate analysis and multi-modal data integration. A nascent field called neuroeconomics has recently emerged as a result of the enormous success of applications of functional brain imaging techniques in the study of human decision-making. By combining functional brain imaging with sophisticated experimental designs and data analysis methods, functions of brain regions and their interactions can be examined. Advanced noninvasive neuroimaging techniques such as EEG and fMRI allow researchers to directly observe brain activities while subjects perform various perceptual, motor, and/or cognitive tasks.