What is EEG and what is it used for?

What is EEG and what is it used for?

5 Min.
Light
By Caitlin Shure and Javier Mínguez
March 21, 2024

In this post, we'll explore the electroencephalogram (EEG): what it is, how it works, its fascinating applications, and the various types of EEG systems available today.

Electroencephalography (EEG) is a non-invasive method used to measure electrical activity in the human brain. Invented in 1929, this technique has evolved into various forms and is now employed for diverse purposes, including diagnostic tests, scientific research, and an increasing array of consumer applications  (Berger, 1929). 

How does an EEG work?

To comprehend the functioning of an electroencephalogram, it's beneficial to grasp some fundamental concepts about the brain's operation.

The activity of the brain is characterized by a surge of electrical signals coursing through brain cells known as neurons. When a neuron is activated or "fires," an electrical current cascades down the cell. When numerous neurons fire simultaneously, sensors on the scalp can perceive this voltage shift—a mechanism that underpins electroencephalography. An EEG cap consists of numerous tiny sensors, known as EEG electrodes, that track electrical impulses and signals originating from various regions of the brain.

While the routine EEG is usually performed in a fixed location, ambulatory EEG enables the patient to use the device while carrying out their everyday tasks or conduct a prolonged EEG from the comfort of their home.

Versatile EEG research

EEG charts comprise a series of wavy lines, which represent rising and falling voltages within different groups of neurons. The range of this voltage signals is in the order of micro Volts. They are 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 patterns 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 studying when and where different brain waves occur, scientists and doctors can learn important information about how the brain works.

Brain Waves Bitbrain 0

What is an EEG used for?

EEG functions are:

  1. Identifying neurological disorders: EEG is instrumental in diagnosing diseases like epilepsy, sleep disorders, brain tumors, and brain injuries by identifying unusual brain activity patterns.
  2. Monitoring brain function: In intensive care units, EEG is employed to observe the brain function of patients suffering from traumatic brain injury, stroke, or other neurological disorders.
  3. Planning treatment: EEG results can steer treatment choices, such as epilepsy medication management, by offering information about the location and intensity of unusual brain activity.
  4. Research: EEG is extensively utilized in neuroscience research for studying brain function, cognitive processes, and neurological disorders. It offers crucial insights into brain activity during various tasks, behaviors, and consciousness states.

In conclusion, electroencephalography is an essential tool in clinical practice, research, and devising treatment strategies for various neurological conditions.

EEG in Medical Applications

1. Epilepsy

An EEG test can be instrumental in identifying specific brain disorders in health care. Physicians have traditionally employed this technique to assess potential instances of epilepsy and other seizure disorders (Smith, 2005). Diagnostic procedures might include exposure to flickering or flashing lights, a stimulus that can induce seizures activity in individuals with photosensitive epilepsy. Beyond identifying and categorizing different types of seizures, EEG data can also serve to track patients in the intervals between epileptic events, or to forecast and manage seizures.

Seizures Classification Table

2. Sleep

Electroencephalography plays a significant role in diagnosing sleep disorders. The normal sleep structure divides into two broad phases, including Non-rapid Eye Movement sleep (NREM) and Rapid Eye Movement sleep (REM), which alternate throughout the night. Based on a well-differentiated pattern of EEG changes, NREM is divided further into four stages: stage I, stage II, stage III - IV  (Nayak and Anilkumar, 2020; Patel, Reddy, Araujo, 2020). By analyzing an sleep study outcomes, scientists can assess sleep quality and identify associated disorders.

Sleep Eeg Graph 1

3. ADHD

Physicians typically diagnose ADHD, like other psychiatric disorders, through a clinical interview. Experts are still debating the most reliable biomarker for diagnosis. This process may also involve an EEG test (Amadou, 2020), (Kiiski, 2019), and (Saad, 2015). Electroencephalography by itself is not sufficient to diagnose ADHD and it should always be used in conjunction with a more comprehensive assessment. 

In the future, EEG procedures could potentially be used to assist in detecting other disorders like depression, Alzheimer's disease, and schizophrenia. Studies in this area are still in the preliminary phase (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 scan. For example, you may be asked to wash your hair prior to the visit, as styling products can interfere with scalp recordings. There are minimal risks and side effects associated with EEG tests.

EEG in Research Applications

In addition to its diagnostic potential, EEG monitoring has tremendous research value. Indeed, this technique has been used to explore brain function for nearly a century, and has been applied across diverse corners of psychology and neuroscience. For example, cognitive psychologists often employ this technique to explore the neural associations of fundamental cognitive abilities, such as emotion, language, attention, and learning. Similarly, certain social psychologists utilize EEG findings to enhance their examination of group dynamics and social understanding. 

Researchers are utilizing electroencephalogram results not just for diagnosing disorders. They are also exploring its usage to restore functionality in individuals suffering from paralysis, movement disorders or neurodegenerative diseases. Also to enhance existing human capabilities. This can be achieved via a brain-computer interface (BCI), which translates the brain's electrical signals into action.

EEG-based brain computer interfaces create a non-invasive and direct connection between the brain and an external device. Connection with a computer or a robotic arm grants new levels of control to paralyzed users. The next Figure displays a BCI typical operation.

Eeg Based Bci Bitbrain

There exists substantial momentum also in recreational usage to 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 technologies 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 observed, the term EEG encompasses a broad spectrum of products and procedures. In every instance, electrodes are affixed to your scalp, providing insight into your brain's electrical activity. Nevertheless, its technical characteristics can differ significantly.

For instance, while research and clinical grade instruments can contain up to 64 electrodes, consumer gadgets might only include three sensors positioned in particular brain regions. Furthermore, the electrodes could be either dry or wet. The latter category pertains to electrodes that need a conductive material (for example, gel, saline or water-based EEGs). See (Liao 2012).

Recent technological developments have significantly streamlined EEG testing. Many modern EEG devices eliminate the need for electrolytic gel, and thus the requirement to wash the head after the session. Examples are the Bitbrain Versatile EEG (use of tap water), Minimal EEG (dry metal sensors) or Textile EEG (dry textiles sensors). Additionally, adaptations have been made to conduct EEG tests for kids, such as the Bitbrain Versatile EEG.

These advancements have not only improved the time and complexity of EEG setup, but also made it easier for technicians without extensive prior EEG experience to collect high-quality EEG brain data effortlessly.

Versatile EEG, Minimal EEG and Neuroheadband EEG

These neurotech devices It can also be wired or wireless, with the latter using Bluetooth technology to relay data to a nearby device. A recent summary of wearable devices in the market can be found here (Niso, 2023).

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

About the authors

Caitlin Shure (www.caitlinshure.com | (LinkedIn) She is a scholar and writer exploring the intersection of neuroscience, technology, and society.

Javier Mínguez (LinkedIn, scholar, Twitter) Associate professor of the University of Zaragoza and co-founder of Bitbrain. 

Bitbrain solutions

Bitbrain specializes in developing innovative devices with excellent usability for multimodal monitoring, encompassing semi-dry EEG, dry-EEG, and textile-EEG systems, as well as biosignals (ExG, GSR, RESP, TEMP, IMUs, etc.), and eye-tracking solutions (screen-based and mobile platforms).

The software tools facilitate the design of experiments, effortless data gathering with over 35 synchronized sensor types, and extensive data analysis covering a broad spectrum of emotional and cognitive biometrics.

Bitbrain's platforms offer interconnectivity with other systems through LSL, ePrime, Matlab, or Python, providing flexibility and compatibility for diverse research and application needs.

Our systems are used by scientists in high-impact and peer-reviewed publications in a wide range of research applications, including neuroscience, psychology, education, human factors, market research and neuromarketing, and brain-computer interfacing

You might be interested in

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