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Why use EEG for research?

Imagine you created a short video and you want to find out which parts of the video people found engaging. Typically, you would just ask them. Maybe you would use a survey. But the most common answer might be “I’m not sure exactly” or “I can’t remember”. Conducting research on human perception using subjective measures alone can be riddled with uncertainty that measuring neurophysiological responses can propose to overcome. EEG devices are uniquely positioned as an easily accessible, cost-effective tool that can enhance research related to human perception. As a result, it is fast becoming a key tool in psychology, neuromarketing and BCI.

What’s EEG?

Electroencephalography (EEG) is the measurement of electrical activity elicited by brain cells, which are called neurons. It is a safe and non-invasive method using electrodes placed on the scalp. EEG devices used for this purpose can vary from single channel commercial devices to 256 channel medical grade systems. You can read more details about what EEG is and different EEG devices here.

What are the benefits of EEG? 

High temporal resolution

Image shows spatiotemporal and temporal resolutions of EEG, MEG, fNIRS, fMRI and PET. Due to its high temporal resolution, EEG is able to index pre-conscious processes.

Due to its high temporal resolution, EEG is able to index pre-conscious processes.

EEG’s biggest strength over other neuroimaging methods is its temporal resolution, i.e., the ability to measure rapid brain responses in the range of milliseconds. Other brain imaging methods such as fMRI (functional magnetic resonance imaging), require a second or more after presenting the stimuli of interest. Further, behavioral tasks designed to avoid uncertainties in subjective responses typically rely on reaction times and button press responses. These can take up to a second, which is very slow when considering that the brain is able to produce many complex neurophysiological processes at a millisecond timescale. Thus, due to its high temporal resolution, EEG is able to index pre-conscious processes which would otherwise go unrecognized with mere self-report and response-based tasks. 

Affordability and mobility

Sports Science: Paxton Lynch undergoes the pressure test with Emotiv Insight EEG Headset.

EEG devices have become cost effective and wireless, allowing researchers to conduct research in the field, instead of bringing participants into the lab. While both EEG and MEG (Magnetoencephalography) have high temporal resolution, EEG is the more accessible research tool due to being low cost, and mobile which enables human behavior to be studied in controlled or natural settings. Alternative neuroimaging methods (e.g., MEG, MRI and PET) require high maintenance cost and participants have to be brought into the hospital or laboratory setting to conduct these studies. In sharp contrast, almost any setting can be converted into an EEG “lab”. (See  Park et al. review1 on how mobile EEG can be used in improving sports performance in the field)

In-house or remote studies

EEG does not necessarily have to be laboratory based with a single device. With advancements in affordable, commercial EEG devices, home users are able to record EEG on themselves. The EmotivLABS platform allows researchers to conduct their experiments online with EMOTIV headsets, which have been validated against research-grade devices2,3. Read about our pilot online EEG study here or about one of our partnerships where EMOTIV users participated in an at-home study to assess a presentation software here.  

What can we measure with EEG?

Most commonly researchers use either the voltage amplitudes at time points of interest after the onset of a stimulus (i.e. event-related potentials, or ERPs) or the amount of oscillations (of brain waves) in the EEG per second (i.e. time-frequency analysis).

These two domains allow us to answer different research questions related to behavior. Further, with the progression of sophisticated machine learning algorithms we can begin to decode mental states in response to stimuli of interest. For example, with the development of algorithms validated for attention we can now answer questions such as “Which part of my video captured more attention” easily. 

Caveats to consider

It is important to remember, we can’t exactly read thoughts with EEG. So, the stimuli being compared ideally needs to be matched on every aspect except for the variable of interest itself. Thus, a well-designed experimental task is the cornerstone of good EEG research. Second, EEG devices can pick up interference from electrical equipment and EEGs can also be susceptible to movement which can introduce unwanted artifacts into the recording. Thus, raw EEGs reflect whole brain responses that need to be cleaned and processed before any inference can be made related to perception of the stimuli.

Additionally, the brain activity at a single electrode records activity from the whole brain and its location does not precisely indicate the source of the activity directly (e.g. an increased activity at a frontal electrode does not mean the frontal lobe generated this response). Methods such as source reconstruction4 of EEG response can be used for this purpose to determine the source at the scalp level. To determine deeper sources with more confidence, neuroimaging methods such as MEG or fMRI coupled with EEG could be considered.

EEG in current research

EEG is currently used in a multitude of ways aiding researchers not only in psychology and medical fields but in brain computer interfaces, neurofeedback and in understanding consumer behavior in fields such as neuromarketing. 

Medical or Clinical Neuroscience

EEG is predominantly used in medical fields to improve diagnosis and treatment. For instance, the most common use of EEG is in the diagnosis of epilepsy and detection of seizures5 and in sleep studies to detect sleep abnormalities6. In psychiatry and clinical neuroscience, EEG is currently being used to identify objective markers of disorders which otherwise rely on subjective clinical assessments. Techniques such as quantitative EEG (qEEG) in which the  amount of oscillations are calculated and mapped over the scalp are being used to characterize the changes in the brain caused by various psychiatric disorders7. Machine learning applied to the classification of healthy vs disordered brains is also paving the way for more objective methods of diagnosis8,9.

Neuromarketing

Certainly, understanding consumer behavior is at the heart of Neuromarketing. The most common use of EEG in this field is to determine less salient and engaging aspects of advertisements10, products or services with the objective of improving them.

EMOTIV x Neuromarketing –  Future of Consumer Behavior at L’Oreal’s Luxury Lab.

EEG oscillations are also used to identify if there is subconscious brand/product recall11. Other uses include neuropricing, where behavioral tasks with EEG are used to find optimal pricing strategies for products12.

EMOTIV x Neuromarketing – How the brain reacts to different pricing suggestions.

General Neuroscience Research

This type of research involves understanding how the brain functions (e.g., how our brain processes visual or auditory stimuli) and how different parts of the brain communicate with each other. It also involves understanding the relationship between the brain and disorders (e.g., autism spectrum disorder or schizophrenia). This encompasses multiple fields that include social, affective, computational, and cognitive domains. 

Brain computer interfaces(BCI)

BCI research aims to translate mental commands into an external action, by integrating EEG with computing devices. Using mental commands to type out a word document, to move a wheelchair and even to move prosthetic limbs are some of the current developments in BCI being used to improve the quality of life of people with disabilities13.

Brain computer interfaces (BCI) - The stunning creations from John, an 8-year-old boy with cerebral palsy, at brainpaintbyjohn on Instagram
Brain computer interfaces (BCI) – The stunning creations from John, an 8-year-old boy with cerebral palsy, at brainpaintbyjohn on Instagram

Another revolution is in the music industry where musicians/singers are using their thoughts to create music (see our related post here)

Brain computer interfaces (BCI) – EMOTIV’s EPOC headset & the iconic TONTO synthesizer are the perfect match.

Overall, the use of EEG offers the promise to venture beneath the surface level understanding of human behavior. It’s cost-effectiveness and high accessibility makes it a useful tool across multiple disciplines where processes ranging from improving user-experiences to advancing therapeutics can be made by going deeper than simple subjective self-reports and decoding human behavior objectively with the use of EEG.

Brain computer interfaces (BCI) – EMOTIV x Rodrigo Hubner Mendes, driving F1 car using mental commands

Article by
Roshini Randeniya, Research Officer, EMOTIV Research Pty. Ltd

References

1. Park, J. L., Fairweather, M. M. & Donaldson, D. I. Making the case for mobile cognition: EEG and sports performance. Neurosci. Biobehav. Rev. 52, 117–130 (2015).

2. Kotowski, K., Stapor, K., Leski, J. & Kotas, M. Validation of Emotiv EPOC+ for extracting ERP correlates of emotional face processing. Biocybern. Biomed. Eng. 38, 773–781 (2018).

3. Badcock, N. A. et al. Validation of the Emotiv EPOC EEG system for research quality auditory event-related potentials in children. PeerJ 3, e907 (2015).

4. Michel, C. M. et al. EEG source imaging. Clin. Neurophysiol. 115, 2195–2222 (2004).

5. Noachtar, S. & Rémi, J. The role of EEG in epilepsy: A critical review. Epilepsy Behav. 15, 22–33 (2009).

6. Aldrich, M. S. & Jahnke, B. Diagnostic value of video‐EEG polysomnography. Neurology 41, 1060–1060 (1991).

7. Prichep, L. S. & John, E. R. QEEG profiles of psychiatric disorders. Brain Topogr. 4, 249–257 (1992).

8. Khodayari-Rostamabad, A., Reilly, J. P., Hasey, G. M., de Bruin, H. & MacCrimmon, D. J. A machine learning approach using EEG data to predict response to SSRI treatment for major depressive disorder. Clin. Neurophysiol. 124, 1975–1985 (2013).

9. Čukić, M., López, V. & Pavón, J. Classification of Depression Through Resting-State Electroencephalogram as a Novel Practice in Psychiatry: Review. J. Med. Internet Res. 22, e19548 (2020).

10. Ohme, R., Reykowska, D., Wiener, D. & Choromanska, A. Analysis of neurophysiological reactions to advertising stimuli by means of EEG and galvanic skin response measures. J. Neurosci. Psychol. Econ. 2, 21–31 (2009).

11. Shaari, A., Syafiq, M., Mikami, O. & M.A, M. K. Electroencephalography (EEG) Application in Neuromarketing-Exploring the Subconscious Mind ELECTROENCEPHALOGRAPHY (EEG) APPLICATION IN NEUROMARKETING-EXPLORING THE SUBCONSCIOUS MIND. 14, (2020). (Neuromarketing)

12. Nigdelis, V. & Tsolaki, M. Neuropricing: Perspectives of brain reactions to price exposure. Hell. J. Nucl. Med. 20, 196–203 (2017).

13. Abiri, R., Borhani, S., Jiang, Y. & Zhao, X. Decoding Attentional State to Faces and Scenes Using EEG Brainwaves. Complexity 2019, 1–10 (2019).

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