Encyclopedia of Personality and Individual Differences

Living Edition
| Editors: Virgil Zeigler-Hill, Todd K. Shackelford

Electroencephalography (EEG)

  • Tamara Paulo TavaresEmail author
Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-28099-8_748-1

Keywords

Electrical Activity Beta Activity Alpha Wave Interictal Epileptiform Discharge Sagittal Midline 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Definition

Electroencephalography is a neurophysiological technique used to record brain activity.

Introduction

Electroencephalography (EEG) is a widely recognized neurophysiological technique used to directly measure the electrical activity of the brain. The history of EEG began in 1875 with Richard Carlton who successfully recorded electrical activity from the cerebrum of rabbits and monkeys. Subsequently, Hans Berger recorded electrical activity from the human scalp in 1924 (Freeman 2013). Berger used the German word “elektrenkephalogramm” to describe the recorded activity and suggested that the brain’s electrical activity was dependent on its functional status (Siuly et al. 2016). Currently, EEG signals are most commonly recorded extracranially with electrodes placed on the scalp; however, EEG signals can also be recorded directly from the cortical surface using subdural electrodes, or subcortically with surgically implanted depth electrodes. This chapter will provide a brief overview of the mechanisms of extracranial EEG and its utility in research and in clinical practice for epilepsy.

The Basis of EEG: Neuronal Activity

A neuron is composed of dendrites, a cell body, axon, and terminal buttons. The dendrites receive electrical inputs from other cells through synapses and propagate the signal to the cell body. The axon propagates the electrical signal down the length of the neuron where it reaches the terminal buttons which connect to other cells. At rest, a neuron has an unequal distribution of negative and positive ions intracellularly and extracellularly, resulting in a resting membrane potential of approximately −70 mV. When an electrical signal arrives at a neuron, ion channels open allowing positively charged extracellular ions to move into the cell. Once the membrane potential reaches a threshold level (−50 mV), an action potential is generated leading to further depolarization (to 30 mV). Action potentials are propagated down the axon reaching the terminal buttons and causing the release of neurotransmitters into the synapse. Neurotransmitters bind to receptors on the connecting cell (postsynaptic membrane) where they may cause an excitatory postsynaptic potential (EPSP), making it more likely that the postsynaptic cell will have an action potential, or an inhibitory postsynaptic potential (IPSP), making it less likely that the postsynaptic cell will have an action potential. As action potentials are relatively short in duration (<2 ms; Ebersole et al. 2003), EEG signals are derived from the summed EPSPs and IPSPs. In some cases, specifically during synchronous events such as sleep transients and seizure activity, action potentials can contribute to the EEG signal (Olejniczak 2006).

EEG: Advantages and Disadvantages

EEG is able to detect brief electrical changes (in the order of milliseconds), leading to its excellent temporal resolution (Stern and Engel 2013). In addition, EEG recordings are noninvasive and accessible at a relatively low cost in comparison to other neuroimaging methods (Freeman 2013). These advantages make it possible to conduct research experiments or clinical work on a broader group of individuals, who may have contraindications such as a metallic implant for magnetic resonance imaging (MRI). However, extracranial EEG has poor spatial resolution since the recording is based on a group of neurons, and the signal is attenuated due to the distance and the presence of resistive tissue between the recording electrodes and the neurons. The detection of EEG signals requires a synchronization of approximately 108 neurons within an area of 6–10 cm2 (Olejniczak 2006; Stern and Engel 2013). Therefore, EEG recordings reflect electrical activity of numerous neurons over a large area. Additionally, in clinical practice, EEG requires the categorization of signals as abnormal or normal based on visual interpretation (Stern and Engel 2013), leading to increased subjectivity of the recorded electrical activity.

EEG Electrodes and Placement

EEG recordings are completed using high conductance electrodes adhered to the scalp using electrode recording gel in order to increase the conductivity with the skull (Freeman 2013). The electrodes are placed on the skull following the standard international 10–20 electrode system, which indicate the distance between the electrodes (Siuly et al. 2016). Specifically, the electrodes are placed at 10 and 20% of the distance of the sagittal midline, defined by the nasion and inion, and the coronal midline, defined by the region that the ears attach to the scalp (Stern and Engel 2013).

The designation of electrodes consists of letters and numbers indicating the position of the electrode on the scalp. Specifically, odd numbers identify electrodes on the left hemisphere and even numbers identify electrodes placed on the right hemisphere (Tatum 2008). Larger numbers reflect greater distances from the sagittal midline (Stern and Engel 2013). Electrodes placed on the frontopolar, frontal, temporal, occipital, central, parietal, and midline are designated FP, F, T, O, C, P, and Z, respectively (Tatum 2008). Additional electrodes may also be included to increase spatial resolution; for example, temporal, frontotemporal, or sphenoidal electrodes can be added to increase the localization of the signal within the temporal cortex (Tatum 2008).

EEG Recordings

EEG recordings are displayed as channels representing the differences between two electrodes. The EEG recordings can be organized based on a variety of different montages such as referential, bipolar, average, and laplacian. The referential montage consists of a designated electrode to which all other electrodes are compared to; the designated electrode is often the Cz (central midline) or mastoid electrodes (Stern and Engel 2013). The bipolar montage consists of channels representing the difference between two adjacent electrodes (Stern and Engel 2013). The average reference montage uses the averaged signal as the common reference for all channels (Siuly et al. 2016), except for the frontopolar and anterior temporal electrodes as they are susceptible to eye movement artifacts (Misulis and Abou-Khalil 2014). Finally, the laplacian montage uses the weighted average of electrodes as the reference (Siuly et al. 2016).

Patterns of EEG recordings can reflect different behavioral states resulting in five main wave patterns: alpha, beta, theta, delta, and gamma.

Alpha waves comprise electrical activity occurring at 8–13 Hz. The frequency of the alpha wave increases to 8 Hz by the age of 3 years, then stabilizes during adolescence (Ebersole et al. 2003). In late adulthood, the frequency of alpha waves decreases, likely related to changes in cerebral metabolic rate (Ebersole et al. 2003). During a relaxation state, alpha waves occur bilaterally in the occipital lobes (Misulis and Abou-Khalil 2014; Siuly et al. 2016). The alpha rhythm is also found anteriorly during periods of drowsiness (Tatum 2008). Hemispheric asymmetries in the voltage that are greater than 50% are often indicative of abnormalities (Tatum 2008).

Beta waves comprise electrical activity often occurring at 18–25 Hz (Ebersole et al. 2003; Tatum 2008). Beta waves often occur during drowsiness and sleep (Tatum 2008). Beta activity is found in infants but not evident in young children. In late adulthood, the amplitude of beta waves is usually lower, especially among males (Ebersole et al. 2003). Beta activity of 25 μV is considered abnormal but has no clinical utility as it is not specific to a disorder (Ebersole et al. 2003). Certain drugs such as barbiturates, chloral hydrate, and benzodiazepines increase beta activity (Tatum 2008).

Theta waves comprise electrical activity occurring at 4–7 Hz. During periods of concentration and mental tasks, increased theta activity is found in the frontal lobes (Tatum 2008). Frontal theta activity is increased in children and young adults during enhanced emotional states; however, there is no consensus regarding a threshold indicative of abnormal activity (Ebersole et al. 2003).

Delta waves comprise electrical activity occurring at <4 Hz (Tatum 2008). There is a gradual increase in delta waves as the individual progresses from stage 2 to stage 4 of the sleep cycle (Misulis and Abou-Khalil 2014).

Gamma waves: Low gamma waves comprise electrical activity occurring at 30–60 Hz and have been related to attention and sensory perception. On the other hand, high gamma rhythms occur at 80–120+ Hz (Freeman 2013).

Research and Clinical Usages

EEG has a variety of clinical and research implications. Changes in EEG recordings in response to the presence of a stimulus are known as event-related potentials (ERPs) and can be used to study various cognitive processes. The ERPs can be aligned to the onset of the stimulus (stimulus-locked waveform), or it can be aligned to the onset of the behavioral response (response-locked waveform; Amodio et al. 2014). Stimulus-locked ERPs may reflect automatic processes and perceptual and attentional functions in response to the stimulus. Response-locked ERPs may reflect the cognitive processes associated with producing and regulating behavioral responses (Amodio et al. 2014). Although ERP signals can be aligned to behavioral responses, it is important to note that ERPs may be involved in a variety of cognitive processes, and it cannot be assumed that an ERP signal is involved in a single behavioral or cognitive construct (Amodio et al. 2014).

In addition to research, EEG is used widely in clinical settings as a diagnostic procedure for neurological disorders such as epilepsy. Epilepsy is associated with interictal epileptiform discharges (IEDs) which are: (1) paroxysmal, (2) include abrupt changes in polarity, (3) have duration of less than 200 ms, and (4) recorded from more than one electrode (Ebersole et al. 2003). EEG recordings can help identify epilepsy syndromes, which assist in determining the prognosis and treatment options (Noachtar and Remi 2009). Specifically, IEDs can help determine whether the epilepsy is localized or generalized and help distinguish between idiopathic and symptomatic epilepsy (Ebersole et al. 2003). Additionally, IEDs may help localize the epileptogenic zone which is important when considering surgical treatment (Tatum 2008). Although IEDs are associated with an epilepsy diagnosis, the absence of these EEG signals does not preclude epilepsy diagnosis, as activity within subcortical structures may not be evident at the level of the scalp (Tatum 2008).

Conclusion

EEG is a neurophysiological technique used to directly measure and assess neuronal activity with remarkable temporal resolution. EEG signals can be measured extracranially, directly on the cortical surface, or subcortically. Furthermore, the extracted signals can be used to study cognitive processes during experimental paradigms, and it can also be applied in clinical settings to diagnose and monitor neurological disorders such as epilepsy.

Cross-References

References

  1. Amodio, D. M., Bartholow, B. D., & Ito, T. A. (2014). Tracking the dynamics of the social brain: ERP approaches for social cognitive and affective neuroscience. Social Cognitive and Affective Neuroscience, 9(3), 385–393. doi: 10.1093/scan/nst177.CrossRefPubMedGoogle Scholar
  2. Ebersole, J. S., Pedley, T. A., Lippincott, W., & Wilkins. (2003). Current practice of clinical electroencephalography (Vol. 3). Philadelphia: Lippincott Williams & Wilkins.Google Scholar
  3. Freeman, W. J. (2013). Imaging brain function with EEG. New York: Springer.CrossRefGoogle Scholar
  4. Misulis, K. E., & Abou-Khalil, B. (2014). Atlas of EEG, seizure semiology, and management (Vol. 2). Oxford, New York: Oxford University Press.Google Scholar
  5. Noachtar, S., & Remi, J. (2009). The role of EEG in epilepsy: A critical review. Epilepsy & Behavior, 15(1), 22–33. doi: 10.1016/j.yebeh.2009.02.035.CrossRefGoogle Scholar
  6. Olejniczak, P. (2006). Neurophysiologic basis of EEG. Journal of Clinical Neurophysiology, 23(3), 186–189. doi: 10.1097/01.wnp.0000220079.61973.6c.CrossRefPubMedGoogle Scholar
  7. Siuly, S., Li, Y., & Zhang, Y. (2016). EEG signal analysis and classification: Techniques and applications. Cham: Springer International Publishing.CrossRefGoogle Scholar
  8. Stern, J. M., & Engel, J. (2013). Atlas of EEG patterns (Vol. 2). Philadelphia: Wolters Kluwer/Lippincott Williams & Wilkins.Google Scholar
  9. Tatum, W. O. I. V. (2008). Handbook of EEG interpretation. New York: Demos Medical Pub.Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Graduate Program in Neuroscience, The Brain and Mind InstituteUniversity of Western OntarioLondonCanada

Section editors and affiliations

  • Julie Schermer
    • 1
  1. 1.The University of Western OntarioLondonCanada