Electroencephalography involves the use of multiple sensors to measure and record brain activity. This technology has many applications in neuroscience and psychology, and ongoing technological advancements promise to further expand the list of potential uses.
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Electroencephalography (EEG) is a non-invasive technique that allows the electrical activity of the brain to be recorded. This can generate insights into neurological disorders, cognitive functions, and emotional states. As such, EEGs are commonly performed in fields such as cognitive psychology, neuroscience, and neurolinguistics.
This article will explore the different types of sensors used in EEG devices and their importance.
Components of EEGs
An EEG records brain electrical activity using electrodes placed on the scalp. The electrodes detect electrical fluctuations produced by neurons firing in response to stimuli, emotions, and thoughts. The electrical activity is presented as a brainwave pattern and offers insights into the brain’s functioning.
There are several different types of electrodes used in EEG:
These electrodes are used alongside a conductive gel to establish a strong connection with the scalp. Although they offer good signal quality due to low impedance, wet electrodes require more preparation time.
Dry electrodes are not used with a conductive gel, making them more comfortable and convenient. However, they often have higher impedance, which reduces the quality of the signal.
These sensors contain built-in amplifiers near the scalp. This minimizes signal degradation and interference during transmission, resulting in high-quality data.
When using passive electrodes, the signal is amplified at the end of the electrical wire. These electrodes tend to consume less power than active electrodes but produce a lower-quality signal.
No matter the type of electrode used, proper sensor placement is crucial for obtaining an accurate signal. As a result, EEG electrodes are positioned according to international standards, such as the 10-20 system. This ensures that sensor placement remains consistent across different individuals, making it easier to compare data.
However, it can be difficult to achieve good electrical contact between the electrodes and the scalp, due to factors such as skin impedance, hair, and head movements. Advancements in the design of EEG equipment have helped to tackle these problems and improve the reliability of EEG data.
Interference from sources such as cardiac signal, eyeball movement, or environmental noise can also be problematic, potentially distorting the EEG signal. Signal processing can help to reduce the effects of interference by using complex algorithms to identify and remove noise.
Real-Time Monitoring Using Sensors
EEG sensors are strategically positioned on the scalp to capture electrical activity from specific regions of the brain. The sensors are highly sensitive, allowing them to detect subtle electrical changes, which are recorded and processed in real-time.
In clinical settings, EEG sensors are used to monitor and diagnose neurological disorders, including sleep disorders. Each stage of sleep is associated with a characteristic pattern of brain activity. By carrying out an EEG while a patient sleeps, the markers of each sleep stage can be studied. This allows for the determination of sleep quality and diagnosis of sleep disorders.
EEG sensors are also used in the diagnosis and management of seizure disorders. For example, EEG sensors can help to determine seizure type and epilepsy syndrome in epileptic patients, which then informs the choice of antiepileptic medications and aids in the assessment of prognosis.
EEG sensors are even available to consumers in the form of wearable devices that can monitor stress levels, track sleep patterns, and enhance meditation experiences. This has increased the accessibility of this technology, allowing a larger number of people to benefit from the insights provided by the sensors.
Advancements and Future Directions
The rapid pace of technological innovation has brought about significant advancements in EEG sensors in recent years.
One of the most notable developments is the use of EEG sensors in brain-computer interfaces (BCIs), where they measure brain activity to enable direct communication between the brain and external devices. This has revolutionized the lives of people with motor impairments, allowing them to communicate, control computers, and move robotic arms using their thoughts.
There are some challenges associated with EEG-based BCIs. For example, it is difficult to develop BCIs that work reliably in all patients, and environmental noise can significantly reduce performance. However, if these issues are resolved in the future, this technology could enable patients to control assistive robots that help them perform daily tasks around the house.
Recently, EEG sensors have even been incorporated into virtual reality (VR) headsets. Researchers at The University of Texas at Austin have created non-invasive, spongy EEG sensors, which they attached to the top strap and forehead pad of a VR headset. Using these sensors, the brain’s electrical activity can be recorded while an individual is in VR.
The use of EEG sensors in VR technology has many potential applications, including in the treatment of anxiety disorders, or for measuring the attention and stress levels of aviators during a flight simulation.
EEG sensors are extremely valuable tools in scientific research, clinical diagnosis, and consumer wearables that give people the tools to understand and optimize their well-being.
Although there are challenges associated with using each type of EEG sensor, ongoing advancements and technological innovations continue to alleviate these issues and expand the potential uses of EEG sensors in various fields.
References and Further Reading
Herwig, U., et al. (2003) Using the international 10-20 EEG system for positioning of transcranial magnetic stimulation. Brain Topography, 16, pp. 95-99. doi.org/10.1023/B:BRAT.0000006333.93597.9d
Repovs, G. (2010) Dealing with noise in EEG recording and data analysis. Informatica Medica Slovenica, 15(1), pp. 18-25.
(2023) Modified virtual reality tech can measure brain activity. [Online] EurekAlert. Available at: https://www.eurekalert.org/news-releases/997768
Smith, S.J. (2005) EEG in the diagnosis, classification, and management of patients with epilepsy. Journal of Neurology, Neurosurgery & Psychiatry. dx.doi.org/10.1136/jnnp.2005.069245
Värbu, K., et al. (2022) Past, present, and future of EEG-based BCI applications. Sensors, 22(9), p. 3331. doi.org/10.3390/s22093331
Shad, E.H.T., et al. (2020) Impedance and noise of passive and active dry EEG electrodes: a review. IEEE Sensors Journal, 20(24), pp. 14565-14577. doi.org/10.1109/JSEN.2020.3012394