Electroencephalography (EEG)

Scalp electroencephalography (EEG) can be used to record ongoing oscillatory brain activity, stimulus-evoked brain potentials (event-related brain potentials, ERP). The recorded signals correspond to variations in scalp potentials, hypothesized to predominantly result from sudden and synchronized changes in postsynaptic activity, occurring in the apical dendrites of pyramidal neurons. Another approach is EEG "frequency tagging", in which a sensory stimulus is periodically repeated at a constant frequency, thereby eliciting a periodic change in the EEG signal which can be identified in the frequency domain.

Event-related potentials (ERPs)

To extract evoked potentials from the ongoing, non event-related, electrical brain activity, the event is usually repeated such as to allow the averaging of successive peristimulus EEG recordings. The principle underlying time-domain averaging techniques is that averaging successive EEG epochs should cancel out the contribution of signals which are not ‘time-locked’ or ‘stationary’ to the onset of the event while it should preserve evoked activity which is assumed to occur with a constant time-delay. The fraction of the signal which is cancelled-out by the averaging procedure is often referred to as ‘additive noise’.

Event-related potentials typically consist of a series of voltage polarity changes, observed as peaks and troughs in the average waveform. These potentials can be classified according to their relative timing to stimulus onset, their polarity, and their magnitude. In most cases, each individualized ERP deflection corresponds to neural activity arising from several temporally overlapping sources. As ERPs provide a high temporal resolution, they can be used to characterize the chronometry of the different neural processes involved in perception. Indeed, depending on their modality, sensory stimuli elicit a series of sensory or exogenous ERP peaks which reflect the initial processing occurring in modality-specific cortical areas. Following these peaks, later components may be recorded, which are thought to reflect more integrative and endogenous aspects of perception.

More about EEG signal processing :

• Event-related brain potentials : standard-averaging and the additive-noise model

• Time-frequency analysis of ERPs using the Wavelet transform

• Blind source separation (BSS) of ERPs using Independent Component Analysis (ICA)

• Single-trial analysis of event-related brain potentials

• Spatio-temporal filtering using ICA, Time-frequency filtering using the wavelet transform, and multiple linear regression analysis

• Source localisation

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