The Procedure and Uses of the EEG Test

The Electroencephalogram (EEG) is a cost-effective, scientifically-proven method to examine brain activity linked to multiple neurocognitive processes that underlie human behavior. The non-invasive nature of EEG makes it an optimal assessment tool to detect abnormal brain activity in different types of patients. The results of the EEG test provide accurate knowledge of the human brain while gaining deeper insights into underlying neural mechanisms of cognition. In this post, we highlight some of these applications, procedures, and advantages of using EEG recordings in research and medical settings.

What is an EEG test?

To better understand what an EEG test is, first we must understand what an EEG is. 

Our brains contain neurons that are deeply interconnected through synaptic mechanisms. When thousands of these brain cells fire at the same time, they generate small currents that can be detected on the head surface with a right device (Jung & Berger, 1979). An EEG is an electrophysiological recording device that measures, in real-time, the electrical field that results from neural activity (microvolt signal). 

A typical EEG system uses one or multiple sensors (electrodes usually in the shape of small metal discs) placed in different sites on the head, usually according to the 10/20 system landmarks (Jasper, 1958). These sensors are capable of accurately picking up the activity of neurons in different parts of the brain in the time frame in which brain processes occur (e.g., 80-400ms).

EEG devices have two clear advantages (Cohen, 2011): 

1.  Excellent time resolution, allowing non-invasive access to brain activity within tens of milliseconds. This is an important asset of EEG considering that changes in the electrical activity in your brain happen much faster than the blink of an eye.
2. Relatively inexpensive and adaptable to different contexts compared with other brain assessment devices (e.g., MRI or magnetic resonance imaging). Today´s EEG systems are portable, allowing for more ecological data collection in real-world environments (e.g., at a participant’s home).

The EEG test is the procedure of recording and monitoring brain activity with an electroencephalogram, following specific steps to gather meaningful data for the specific purpose of its application.

EEG test insights

EEG test results can shed light on possible abnormalities in the brain electrical activity. However, to perform an accurate analysis, it is important to have a basic understanding of what normal/abnormal activity means in EEG (Medithe and Nelakuditi, 2016). 

• Normal brain activity

The electroencephalography is characterized by typical neural rhythms oscillating at specific frequencies known as brain waves (Kumar and Bhuvaneswari, 2012). These signals are recognized by their morphology and frequency. Usually, typical brain waves observed in an EEG register are categorized as Alpha (8-15 Hz), Beta (16-30 Hz), Theta (4-7 Hz), Delta (1-3 Hz), and Gamma (>30Hz). 

Normal EEG data shows regular and symmetrical brain wave traces, often characterized by a dominant frequency in a specific scalp location. For instance, in a normal awake person, the Alpha rhythm is detected in posterior head regions while Beta rhythm is typically localized in frontal areas. Normally, this observed EEG pattern varies according to the level of alertness, fatigue or cognitive engagement (Sazgar and Young, 2019).

• Abnormal EEG activity

An EEG test is considered to be abnormal when the pattern of brain activity shows unusual features which do not match the person's level of alertness, neurodevelopment stage, and other neurobiological factors. Some signs of abnormal EEG include inappropriate scalp location of frequencies or irregularities in brain wave amplitudes. 

For example, a persistent “slowing” activity in the EEG background of an awake adult person is not normal (Sazgar and Young, 2019). Another typical example of abnormal waveform patterns linked to a neurological disorder is seizures. Epileptic seizures are unusual EEG patterns caused by severely disturbed changes in brain activity. They are often characterized by focal or generalized sequences of sharps and spikes components (Emmady and Anilkumar, 2020; Medithe and Nelakuditi, 2016). 

However, abnormal EEG results do not always prove that the person has a brain disorder, nor does a normal EEG guarantee the absence of brain pathology. For this reason, an EEG test should not be used to diagnose and is used alongside other clinical tests and inquiries. 

What is an EEG test used for?

The information obtained from EEG tests is highly valuable and has many potential applications. In medical applications, EEG serves to detect patterns of electrical activity that may be associated with certain brain conditions or neurological disorders. To ensure a correct EEG results interpretation and management of patients, a comprehensive understanding of brainwaves and minimal technical qualification is required. 

EEG tests are also very useful in the field of research. Specifically, to study the neural mechanisms of human cognition under different experimental conditions. There are well-accepted neuroscientific theories on how the EEG signals link to cognitive and emotional processing (Cohen, 2011; Dvorak et al., 2018). EEG is a preferred technique to study subtle brain processes not directly driven by explicit behavior, such as inhibitory responses triggered automatically during car driving (De Sanctis et al., 2012; Zander et al., 2017).

Here are some specific applications of EEG in clinical and research settings. 

1. The EEG test as a neurological assessment method 

When using an EEG test in medical settings, the main objective is to help doctors to establish a proper diagnosis and treatment plan for a potential medical condition. Through expert analysis of EEG data, and in conjunction with a clinical examination, an EEG test can help identify several neurological disorders which include:

• Seizure/Epilepsy: through the analysis of epileptiform patterns (e.g., 3-Hz spike-and-wave) shown by the EEG traces (Westmoreland, 1996), doctors can identify the seizure locus and, in some cases, the type of epilepsy.
• ADHD: neural markers associated with signs of inattention can also be detected with an EEG test. Abnormal EEG patterns linked to this attention deficit refers to an increase in the Theta/Beta Ratio over frontal and central areas (Barry et al., 2003; Lenartowicz & Loo, 2014).
• Sleep disorders: EEG testing is typically adopted to determine sleep quality and diagnose a possible related disorder. Recommended, for example, for patients suspected of having specific types of sleep anomalies that include sleep deprivation, insomnia, and hypersomnia among others.(Verma et al., 2016; Zhu, Li & Wen, 2014).
• Other uses in health care include detection of neurological impairments such as head injuries, strokes, tumors and dementia. Also used to monitor neural activity during brain surgery, coma states, or to confirm brain death (Teplan, 2002). 

2. The EEG test as a brain research method

EEG is an excellent research tool in the study of neural correlates and mechanisms of cognitive functions. Insights provided by EEG help scientists to understand the human brain, and findings have valuable applications in clinical, epidemiological, and public health settings.

Often, researchers use EEG to study the following two aspects: 

• Spontaneous EEG: the investigation of brain changes associated with certain mental states such as cognitive workload or fatigue (Charbonnier et al., 2016). 
• Task-related EEG activity: the investigation of evoked or event-related potentials elicited in response to a visual/auditory stimuli presentation, or contingent with performance of a specific cognitive task (Luck, 2014). Examples of stimuli used to study evoked responses include the presentation of words, images and sounds in different task contexts.

By focusing on contributions to cognition and brain health domains, EEG is particularly enlightening to address the following areas of research:

• Brain-Computer Interfaces: This field of research relies on EEG to detect changes in the neural patterns produced with internal brain events (e.g., preparation for an action) or in response to external stimulus (Pfurtscheller & Lopes da Silva, 1999; Zhu et al., 2010). Current applications include developing neuro-bionic technology for restoring natural body functions in patients with brain injury (e.g., implanted robotic exoskeleton for tetraplegic patients; Rosenfeld & Wong, 2017).

• Cognitive Sciences: Involves the study of underlying brain correlates linked to cognitive domains such as perception, attention, memory, learning and emotion. Studied EEG features of interest include identification of the time course, ERPs waveforms and dominant brain frequencies characterizing cognitive performance and skills (Pietto et al., 2018; Woodman, 2010).

• Clinical Psychology and Neuropsychology: EEG approaches provide knowledge about specific neural markers consistent with affective, neurological and neurodevelopmental disorders. Obtained empirical EEG results are essential to guide clinicians and therapists in developing effective interventions (e.g., Lau-Zhu et al., 2019).

• Social Psychology and Educational Sciences: Knowing underlying brain mechanisms linked to social interaction and teaching-learning processes allows researchers to explore different layers of human behavior (attitudes, communication, social bias, motivation, individual differences, etc.) while expanding the scope of research towards more naturalistic environments (e.g., social interaction in virtual reality; Parsons, Gaggioli & Riva, 2017).

• Consumer Behavior / Neuromarketing Research: EEG-based metrics, combined with behavioral observations and other biometrics (e.g., galvanic skin response), are optimal ways to study unconscious drivers of consumers’ purchase decisions. Product and brand preferences can be analyzed by estimating the mental workload (theta/alpha ratio), engagement (beta/alpha + theta activity) and emotional valence (alpha-asymmetry) elicited across different purchase situations (Bazzani et al., 2020; Cherubino et al., 2019).

Procedures and Risks of the EEG test

The process of an EEG test is safe and entails almost no risks or side effects and little discomfort for participants . However, EEG procedures may differ depending on the context: 

In typical clinical routines, patient-friendly EEG procedures tend to be a thumb rule.. The preparation is painless but tends to take at least 20 minutes for experienced EEG technicians. 

Patients called to perform an EEG test, should always follow the previous specific medical advice on the days or hours before the test as this health information might differ from other patients. Exactly the same should be followed by participants called to perform an EEG test for research purposes. Once in the research lab, clinic unit, or doctor office, the EEG test preparation steps typically include: 

1. Set up: Head preparation, electrode placement on the scalp and impedance lowering
2. Signal Quality Check: Signal quality testing and calibration
3. Instructions to the patient may include lying still and relax, close/open eyes, breathe deeply, stimuli attention, etc. Other typical activation procedures include fast eyes opening and closing for several times, photic stimulation (e.g. staring at flashing lights), breathing deeply or rapidly (hyperventilation), and sleep deprivation (staying up the entire night before the EEG test).
4. Recording interval during which data is collected
5. Disassembling: Remove the electrodes from the patient scalp after the test is completed.

In the context of research, procedures are quite similar but adopted EEG systems often include a much larger number of electrodes attached to the participant´s scalp, which may enlarge considerably the preparation time and total duration of the session. Also, instructions to volunteers may vary according to the requirements of the experimenter. For instance, the participant may be asked to perform mental engaging activities like reading sentences, watching images, answering queries, or listening to sounds/music among other tasks (Pivik et al, 1993). 

Recent technological developments have greatly simplified EEG testing. For instance, many EEG devices do not require the use of a gel or conductive substances, so there is no need to wash the head after the session. These advances have also improved the time and complexity of the EEG setup, allowing technicians without such extensive prior EEG experience to collect quality brain data with ease.

Conclusions

An EEG test is a brain assessment method in which it is recorded brain activity with an EEG device, allowing high time accuracy in a relatively non-invasive and inexpensive way. The EEG procedure is easy and entails no particular risk for most patients and participants. 

There are many practical applications of the EEG test that include clinical and research contexts. In medical settings, it is mostly used to help diagnose neurological conditions such as seizure disorders. EEG provides a worthwhile, efficient, and safe tool to assess many types of patients.

In research settings, EEG is an efficient method to study brain-behavior relationships linked to many cognitive processes. When combined with other biometric techniques, EEG test results are highly insightful in understanding human cognition and emotion in a more comprehensive way.

About the author

Cristina Gil-López, Ph.D. - Researcher at the Psychology and Neurocognition institute (LPNC), University of Grenoble (France) / Associated Research Scientist at Polytechnic University of Valencia (Spain)

Cristina Gil-López is an experienced neuroscientist with an extensive career in human cognition research in academic and industrial settings. Her research includes the study of EEG patterns of visual perception, language processing, and emotion. She also investigates the effects of non-invasive brain stimulation on cognitive enhancement. Cristina is passionate about the secrets of the human brain, committed to high-quality research and the applied use of neurotechnology.

LinkedIn, Scholar, homepage, https://cristinagillopez.com/

You might also be interested in

• How Deep Learning is changing machine learning AI in EEG data processing
• EEG Neurotechnology for Human Enhancement and Rehabilitation
• Bringing BCIs to the user’s home for neurorehabilitation and assistive applications
• The Wet EEG Cap & Differences Between Water-Based, Saline and Gel EEG caps
• The Ultimate Guide of EEG Tech Features
• EEG Connectivity Layer and Other Features of EEG Headsets Explained
• Main features of the EEG amplifier explained
• EEG Synchronization With Other Biosensors (EEG, ECG, EMG, eye tracking, etc.), and Software
• How to Select a Dry-EEG Headset for your Research Application
• What is QEEG brain mapping and normative databases?

References

• Barry, R. J., Clarke, A. R., & Johnstone, S. J. (2003). A review of electrophysiology in attention-deficit/hyperactivity disorder: I. Qualitative and quantitative electroencephalography. Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 114(2), 171–183. https://doi.org/10.1016/s1388-2457(02)00362-0.
• Bazzani A, Ravaioli S, Trieste L, Faraguna U and Turchetti G (2020) Is EEG Suitable for Marketing Research? A Systematic Review. Front. Neurosci. 14:594566. doi: 10.3389/fnins.2020.594566
• Charbonnier, Roy, Bonnet, Campagne (2016). EEG index for control operators’ mental fatigue monitoring using interactions between brain regions. Expert Systems with Applications, Volume 52, 2016, Pages 91-98, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2016.01.013.
• Cherubino, P., Martinez-Levy, A. C., Caratù, M., Cartocci, G., Di Flumeri, G., Modica, E., Rossi, D., Mancini, M., & Trettel, A. (2019). Consumer Behaviour through the Eyes of Neurophysiological Measures: State-of-the-Art and Future Trends. Computational intelligence and neuroscience, 2019, 1976847. https://doi.org/10.1155/2019/1976847
• Cohen (2011). It’s about time. Frontiers in Human Neuroscience, 5(2), 1–15. 
• https://doi.org/10.3389/fnhum.2011.00002
• De Sanctis, P., Butler, J. S., Green, J. M., Snyder, A. C., & Foxe, J. J. (2012). Mobile brain/body imaging (MoBI): High-density electrical mapping of inhibitory processes during walking. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2012, 1542–1545. https://doi.org/10.1109/EMBC.2012.6346236
• Dvorak, D., Shang, A., Abdel-Baki, S., Suzuki, W., & Fenton, A. A. (2018). Cognitive Behavior Classification From Scalp EEG Signals. IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 26(4), 729–739. https://doi.org/10.1109/TNSRE.2018.2797547
• Gevins, A., & Smith, M. E. (2007). Electroencephalography (EEG) in neuroergonomics. In R. Parasuraman & M Rizzo (Eds.), Neuroergonomics: The brain at work (pp. 15-31). Oxford: Oxford University Press. 
• Lau-Zhu, A., Lau, M., & McLoughlin, G. (2019). Mobile EEG in research on neurodevelopmental disorders: Opportunities and challenges. Developmental cognitive neuroscience, 36, 100635. https://doi.org/10.1016/j.dcn.2019.100635
•  Lenartowicz, A., & Loo, S. K. (2014). Use of EEG to diagnose ADHD. Current psychiatry reports, 16(11), 498. https://doi.org/10.1007/s11920-014-0498-0
• Luck, S. (2014). An introduction to the event-related potential technique. Cambridge, MA: MIT Press. 
• Luck, S. J., & Kappenman, E. S. (Eds.). (2012). The Oxford handbook of event-related potential components (pp. 654-664). New York, NY: Oxford University Press
• Medithe, J.W., & Nelakuditi, U.R. (2016). Study of normal and abnormal EEG. 2016 3rd International Conference on Advanced Computing and Communication Systems (ICACCS), 01, 1-4. 10.1109/ICACCS.2016.7586341
• Parsons, T. D., Gaggioli, A., & Riva, G. (2017). Virtual Reality for Research in Social Neuroscience. Brain sciences, 7(4), 42. https://doi.org/10.3390/brainsci7040042
• Pietto ML, Gatti M, Raimondo F, Lipina SJ, Kamienkowski JE (2018) Electrophysiological approaches in the study of cognitive development outside the lab. PLoS ONE 13(11): e0206983. https://doi.org/10.1371/journal.pone.0206983
• Pivik, R. T., Broughton, R. J., Coppola, R., Davidson, R. J., Fox, N., & Nuwer, M. R. (1993). Guidelines for the recording and quantitative analysis of electroencephalographic activity in research contexts. Psychophysiology, 30(6), 547–558. https://doi.org/10.1111/j.1469-8986.1993.tb02081.x
• Rosenfeld, J. V., & Wong, Y. T. (2017). Neurobionics and the brain-computer interface: current applications and future horizons. The Medical journal of Australia, 206(8), 363–368. https://doi.org/10.5694/mja16.01011 
• Sazgar M., Young M.G. (2019) Normal EEG Awake and Sleep. In: Absolute Epilepsy and EEG Rotation Review. Springer, Cham. https://doi.org/10.1007/978-3-030-03511-2_6
• Teplan, M. (2002). FUNDAMENTALS OF EEG MEASUREMENT.
• Westmoreland B. F. (1996). Epileptiform electroencephalographic patterns. Mayo Clinic proceedings, 71(5), 501–511. https://doi.org/10.4065/71.5.501
• Woodman G. F. (2010). A brief introduction to the use of event-related potentials in studies of perception and attention. Attention, perception & psychophysics, 72(8), 2031–2046. https://doi.org/10.3758/APP.72.8.2031
• Verma, R., Nagar, K. K., Garg, R. K., Uniyal, R., Sharma, P. K., & Pandey, S. (2016). Study of Sleep Disorders and Polysomnographic Evaluation among Primary Chronic Daily Headache Patients. Journal of neurosciences in rural practice, 7(Suppl 1), S72–S75. https://doi.org/10.4103/0976-3147.196469
• Zander, T. O., Andreessen, L. M., Berg, A., Bleuel, M., Pawlitzki, J., Zawallich, L., Krol, L. R., & Gramann, K. (2017). Evaluation of a Dry EEG System for Application of Passive Brain-Computer Interfaces in Autonomous Driving. Frontiers in human neuroscience, 11, 78. https://doi.org/10.3389/fnhum.2017.00078
• Zhu, G., Li, Y., & Wen, P. P. (2014). Analysis and classification of sleep stages based on difference visibility graphs from a single-channel EEG signal. IEEE journal of biomedical and health informatics, 18(6), 1813–1821. https://doi.org/10.1109/JBHI.2014.2303991
• Zhu, D., Bieger, J., Garcia Molina, G., & Aarts, R. M. (2010). A survey of stimulation methods used in SSVEP-based BCIs. Computational Intelligence and Neuroscience, 2010, 702357.doi.org/10.1155/2010/70235