What is EEG and what is it used for?

In this post, we will cover what is an electroencephalogram (EEG), how does EEG work, what are the fascinating uses of this brain-sensing device, and what types of EEG systems are available today.

What is EEG? 

Electroencephalography, or EEG, is a noninvasive means by which to measure electrical activity in the brain. First invented in 1929, EEG now comes in a variety of forms and is used for diverse purposes, including diagnostic tests, scientific research, and a growing number of consumer applications. (Berger 1929). 

How EEG Works

To understand how an electroencephalogram (EEG) works, it is useful to understand some basics about how the brain itself works. Brain activity consists of a flurry of electrical signals flowing through cells, called neurons. When a neuron “fires,” or becomes active, an electric current ripples down the cell. And when many neurons fire at the same time, sensors on the scalp can detect this shift in voltage—a process that forms the basis of EEG. A typical EEG cap consists of many sensors—small metal discs, called electrodes—which monitor signals from a number of locations around the head.

EEG charts comprise a series of wavy lines, which represent rising and falling voltages within different groups of neurons. Often referred to as “brain waves,” these ripples are measured in hertz, or cycles per second, and are classified according to their frequency. Brain wave categories include: delta (0.5-4 hz), alpha (8-12 hz), beta (12-35 hz), theta (4-8hz), and gamma (32-100 hz) waves. (Abhang 2016) By tracking when and in what brain regions these wave types appear, researchers and physicians can glean important insights about brain function.

What is EEG Used For?

EEG in Medical Applications

1. Epilepsy

Because EEG records brain activity in real time, the technique can be useful in diagnosing certain neurological conditions. In particular, doctors have long used EEG to evaluate suspected cases of epilepsy and other seizure disorders (Smith 2005). Diagnostic tests may involve the presentation of flashing lights, which can trigger seizures in people with photosensitive epilepsy. In addition to detecting and classifying seizure types, EEG may be used to monitor patients between epileptic episodes, or to predict and control seizures.

2. Sleep

The diagnosis of sleep disorders represents another major application of EEG. Each phase of sleep is characterized by the emergence of particular brain wave patterns, with delta waves indicative of the deepest sleep. (Campbell 2010) By evaluating EEG test results, researchers can therefore determine sleep quality and diagnose related disorders. While sleep and seizure diagnosis are the most common clinical uses of EEG, they are by no means the only ones. Researchers are now exploring the potential of EEG to augment the diagnosis of certain psychiatric conditions, such as ADHD.

3. ADHD

Physicians typically diagnose ADHD, like other psychiatric disorders, through a clinical interview. This process may be supplemented with an EEG study, though the best biomarker for diagnosis remains a matter of contention (Amadou 2020, Kiiski 2019, Saad 2015) Here, it should be noted that electroencephalography alone cannot diagnose ADHD; and such tests should always be coupled with a more exhaustive evaluation. In the future, EEG may be used to assist in the diagnosis of other disorders, including depression, Alzheimer’s disease, and schizophrenia—though work in this area currently remains experimental. (Cassani 2018, de Aguilar Neto 2019, Oh 2019). 

If you receive medical advice to undergo this type of evaluation, you should follow your doctor’s instructions regarding how to prepare for an EEG. For example, you may be asked to wash your hair prior to the visit, as styling products can interfere with scalp recordings. There are no side effects associated with EEG tests.

EEG in Research Applications

In addition to its diagnostic potential, EEG has tremendous research value. Indeed, the technology has been used to explore brain function for nearly a century, and has been applied across diverse corners of psychology and neuroscience. Cognitive psychologists, for instance, frequently use EEG to investigate neural correlates of basic cognitive functions, such as emotion, language, attention, and learning. Likewise, some social psychologists use EEG results to augment analysis of group behavior and social cognition. 

Increasingly, researchers are looking to EEG not just to diagnose disorders, but to restore function in individuals suffering from paralysis or neurodegenerative disease, or to enhance existing human capabilities. This can be achieved via what is known as a brain-computer interface, or BCI, which translates the brain’s electrical signals into action. EEG BCIs create a direct connection between the brain and some sort of external device, such as a computer or a robotic arm, granting new levels of control to paralyzed users. Further, there exists substantial momentum in the field of recreational BCIs, which would allow healthy users to control a computer screen using thought alone. (Vasiljevic 2020)

EEG for Consumers 

Historically, brain scanning techniques have been large and expensive, thus limiting use to the confines of a research lab. By contrast, the latest EEG devices are portable and relatively inexpensive—features that allow scientists to use the technology in more natural and diverse environments(Mavros 2016). These traits also facilitate the use of electroencephalography beyond academic settings, such as for market research or educational applications (Amin 2020, Poulsen 2017).

The past decade has seen major growth in the consumer neurotech industry. There now exists dozens of brain wearables, with applications ranging from neurofeedback to hands-free gaming. Products in this category vary dramatically with respect to reliability and cost. (Pathirana 2018; Grummett 2015) As such, prospective customers should apply a healthy dose of skepticism to any seemingly-outlandish marketing claims.

Types of EEG

As we have seen, the term EEG can refer to a wide range of products and practices. In all cases, electrodes are attached to your scalp and the process sheds light on the electrical activity in your brain. Beyond that, however, its technical features can vary tremendously.

For example, whereas research and clinical grade tools can have as many as 64 electrodes, consumer devices may comprise as few as three sensors placed in specific brain areas. Additionally, the electrodes may be dry or wet. The latter class refers to electrodes that require a conducting substance (e.g., gel, saline or water-based EEGs). (Liao 2012) 

These neurotech devices It can also be wired or wireless, with the latter using Bluetooth technology to relay data to a nearby device. Finally, EEG systems use a range of software protocols to filter, process and analyze brain data.

 As the technology advances, additional varieties are sure to emerge. Indeed, despite being a relatively old field, EEG science is remarkably active, with exciting new innovations arriving each year.   

About the author

Caitlin Shure, PhD

Caitlin Shure is a scholar and writer exploring the intersection of neuroscience, technology, and society. Her research investigates the cultural and ethical implications of modern neurotechnology, as well as the historical provenance of these tools. Based in New York City, Caitlin writes about brain-computer interfaces, neuroethics, and other brain-related fields.

www.caitlinshure.com | LinkedIn | Twitter

Bitbrain solutions

Bitbrain develops and commercializes innovative devices and software tools for real-time monitoring of EEG (dry and semi-dry EEG), biosignals (ExG, GSR, RESP, TEMP, etc.), movement activity (EMG, IMUs, etc.) and eye-tracking (screen-based and mobile).

Our systems are employed by scientists in a wide range of research applications, such as neuroscience, psychology, education, human factors, market research and neuromarketing, and brain-computer interfacing. 

You might be interested in

• What is QEEG brain mapping and normative databases?
• Main features of the EEG amplifier explained
• All about EEG artifacts and filtering tools
• EEG Synchronization With Other Biosensors (EEG, ECG, EMG, eye tracking, etc.), and Software
• EEG Connectivity Layer and Other Features of EEG Headsets Explained

References

• Abhang, P. A., Gawali, B. W., & Mehrotra, S. C. (2016). Technological basics of EEG recording and operation of apparatus. In: Introduction to EEG-and speech-based emotion recognition. Academic Press. Pgs 19-50.
• Adamou, M., Fullen, T., & Jones, S. L. (2020). EEG for Diagnosis of Adult ADHD: A Systematic Review With Narrative Analysis. Frontiers in Psychiatry, 11, 871.
• Amin, C. R., Hasin, M. F., Leon, T. S., Aurko, A. B., Tamanna, T., Rahman, M. A., & Parvez, M. Z. (2020, December). Consumer Behavior Analysis using EEG Signals for Neuromarketing Application. In 2020 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 2061-2066). IEEE.
• Berger, H. (1929). Über das elektrenkephalogramm des menschen. European Archives of Psychiatry and Clinical Neuroscience, 87(1):527–570.
• Campbell, I. G. (2009). EEG recording and analysis for sleep research. Current protocols in neuroscience, 49(1), 10-2.
• Cassani, R., Estarellas, M., San-Martin, R., Fraga, F. J., & Falk, T. H. (2018). Systematic review on resting-state EEG for Alzheimer’s disease diagnosis and progression assessment. Disease markers, 2018.
• de Aguiar Neto, F. S., & Rosa, J. L. G. (2019). Depression biomarkers using non-invasive EEG: A review. Neuroscience & Biobehavioral Reviews, 105, 83-93.
• Grummett, T. S., Leibbrandt, R. E., Lewis, T. W., DeLosAngeles, D., Powers, D. M. W., Willoughby, J. O., ... & Fitzgibbon, S. P. (2015). Measurement of neural signals from inexpensive, wireless and dry EEG systems. Physiological measurement, 36(7), 1469.
• Kiiski, H., Bennett, M., Rueda‐Delgado, L. M., Farina, F. R., Knight, R., Boyle, R., ... & Whelan, R. (2020). EEG spectral power, but not theta/beta ratio, is a neuromarker for adult ADHD. European Journal of Neuroscience, 51(10), 2095-2109.
• Liao, L. D., Lin, C. T., McDowell, K., Wickenden, A. E., Gramann, K., Jung, T. P., ... & Chang, J. Y. (2012). Biosensor technologies for augmented brain–computer interfaces in the next decades. Proceedings of the IEEE, 100(Special Centennial Issue), 1553-1566.
• Mavros, P., Austwick, M. Z., & Smith, A. H. (2016). Geo-EEG: towards the use of EEG in the study of urban behaviour. Applied Spatial Analysis and Policy, 9(2), 191-212.
• Oh, S. L., Vicnesh, J., Ciaccio, E. J., Yuvaraj, R., & Acharya, U. R. (2019). Deep convolutional neural network model for automated diagnosis of schizophrenia using EEG signals. Applied Sciences, 9(14), 2870.
• Pathirana, S., Asirvatham, D., & Johar, G. (2018, July). A critical evaluation on low-cost consumer-grade electroencephalographic devices. In 2018 2nd International Conference on BioSignal Analysis, Processing and Systems (ICBAPS) (pp. 160-165). IEEE.
• Poulsen, A. T., Kamronn, S., Dmochowski, J., Parra, L. C., & Hansen, L. K. (2017). EEG in the classroom: Synchronised neural recordings during video presentation. Scientific reports, 7(1), 1-9.
• Saad, J. F., Kohn, M. R., Clarke, S., Lagopoulos, J., & Hermens, D. F. (2018). Is the theta/beta marker for ADHD inherently flawed?. Journal of attention disorders, 22(9), 815-826.
• Smith, S. J. M. (2005). EEG in the diagnosis, classification, and management of patients with epilepsy. Journal of Neurology, Neurosurgery & Psychiatry, 76(suppl 2), ii2-ii7.
• Vasiljevic, Gabriel Alves Mendes, and Leonardo Cunha de Miranda. "Brain–computer interface games based on consumer-grade EEG Devices: A systematic literature review." International Journal of Human–Computer Interaction 36, no. 2 (2020): 105-142