How to build a high sensitivity EEG headset for continual monitoring?

How to build a high sensitivity EEG headset for continual monitoring?

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After having a conversation with a couple of the more popular "consumer" level EEG makers such as Versus, I have found that their monitoring abilities are either limited or altogether absent. The devices utility, while cool from a 3rd party controlled training standpoint, is neutered when it comes to just getting raw data and plotting it out in a manner that allows one to make their own interpretations and calls.

I would like to build my own device that can monitor from the very low < 0.5 hz all the way up to at least 200 hz. My goal is to feed that into my own software in order to keep a running graph for analysis, etc.

How would one go about building an EEG that has this level of sensitivity?

There are a few commercial EEG makers that do allow monitoring of raw EEG data. For instance, I know the Muse headband and OpenBCI allow you to do this with little trouble. These both support sampling rates up to 250 Hz. (The OpenBCI in principle could have a higher sampling rate, but i don't know anyone who has done this.)

The Emotiv EPOC also has an option to get raw EEG data. You have to pay more for the research version of the SDK to get the data officially. However, someone has reverse engineered their protocol, so you can access the raw data that way. This has a sampling rate of up to 128 Hz.

That said, if you still want to build your own EEG, OpenBCI has documented all of their plans, so a good start would be to replicate their product.

There is also the older, but still useful, OpenEEG project, which also has open plans for an EEG device.

New EEG electrode set for fast and easy measurement of brain function abnormalities

The electrode set is easy to use and it is suitable for, for example, quick measurements in emergency care and long-term monitoring taking place within hospitals.

A new, easy-to-use EEG electrode set for the measurement of the electrical activity of the brain was developed in a recent study completed at the University of Eastern Finland. The solutions developed in the PhD study of Pasi Lepola, MSc, make it possible to attach the electrode set on the patient quickly, resulting in reliable results without any special treatment of the skin. As EEG measurements in emergency care are often performed in challenging conditions, the design of the electrode set pays particular attention to the reduction of electromagnetic interference from external sources.

EEG measurements can be used to detect such abnormalities in the electrical activity of the brain that require immediate treatment. These abnormalities are often indications of severe brain damage, cerebral infarction, cerebral haemorrhage, poisoning, or unspecified disturbed levels of consciousness. One of the most serious brain function abnormalities is a prolonged epileptic seizure, status epilepticus, which is impossible to diagnose without an EEG measurement. In many cases, a rapidly performed EEG measurement and the start of a proper treatment significantly reduces the need for aftercare and rehabilitation. This, in turn, drastically improves the cost-effectiveness of the treatment chain.

Although the benefits of EEG measurements are indisputable, they remain underused in acute and emergency care. A significant reason for this is the fact that the electrode sets available on the markets are difficult to attach on the patient, and their use requires special skills and constant training. This new type of an electrode set is expected to provide solutions for making EEG measurements feasible at as an early stage as possible.

The EEG electrode set was produced using screen printing technology, in which silver ink was used to print the conductors and measurement electrodes on a flexible polyester film. The EEG electrode set consists of 16 hydrogel-coated electrodes which, unlike in the traditional method, are placed on the hair-free areas of the patient's head, making it easy to attach. The new EEG electrode set significantly speeds up the measurement process because there is no need to scrape the patient's skin or to use any separate gels. As the electrode set is flexible and solid, the electrodes get automatically placed in their correct places. Furthermore, there is no need to move the patient's head when putting on the EEG electrode set, which is especially important in patients possibly suffering from a neck or skull injury. Due to the fact that the disposable electrode set is easy and fast to use, it is particularly well-suited to be used in emergency care, in ambulances and even in field conditions. Thanks to the materials used, the electrode set does not interfere with any magnetic resonance or computed tomography imaging the patient may undergo.

Unlike in the traditional EEG method, the EEG electrode set is placed below the hairline.

The performance of the electrode set was tested by using various electrical tests, on several volunteers, and in real patient cases. The results were compared to those obtained by traditional EEG methods.

The PhD study also focused on the use of screen printing technology solutions to protect electrodes against electromagnetic interference. The silver or graphite shielding layer printed to the outer edge of the electrode set was discovered to significantly reduce external interference on the EEG signal. This shielding layer can be easily and cost-efficiently introduced to all measurement electrodes produced with similar methods. Protecting the electrode with a shielding layer is beneficial when measuring weak signals in conditions that contain external interference.

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EEG Coherence Metrics for Vigilance: Sensitivity to Workload, Time-on-Task, and Individual Differences

The vigilance decrement in performance is a significant operational issue in various applied settings. Psychophysiological methods for diagnostic monitoring of vigilance have focused on power spectral density measures from the electroencephalogram (EEG). This article addresses the diagnosticity of an alternative set of EEG measures, coherence between different electrode sites. Coherence metrics may index the functional connectivity between brain regions that supports sustained attention. Coherence was calculated for seven pre-defined brain networks. Workload and time-on-task factors primarily influenced alpha and theta coherence in anterior, central, and inter-hemispheric networks. Individual differences in coherence in inter-hemispheric, left intro-hemispheric and posterior networks correlated with performance. These findings demonstrate the potential applied utility of coherence metrics, although several methodological limitations and challenges must be overcome.

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How to build a high sensitivity EEG headset for continual monitoring? - Psychology

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For stopping, the mean SSRT was significantly longer for the G > S condition (288 ± 26 ms) than the S > G condition (249 ± 46 ms), t(13) = 3.6, p < 0.005 ( Figure 2a ). This pattern of behavior confirmed that the reward manipulation was influencing subjects’ stopping behavior.

A) Reaction time in milliseconds on Go trials and failed stop trials. B) The stop signal delay (SSD) and stop signal reaction time (SSRT). G > S = going quickly earned 10 points and successful stopping earned 1 point S > G = successful stopping earned 10 points and going quickly earned 1 point. ** indicates significance at p < 0.01 and *** indicates significance at p < 0.001.

The mean SSD was significantly shorter for the G > S condition (63 ± 39 ms) than the S > G condition (291 ± 121 ms), t(13) = 7.6, p < 0.001 ( Figure 2a ). The mean p(inhibit) was also significantly smaller for the G > S condition (0.35 ± 0.14) than the p(inhibit) for the S > G condition (0.58 ± 0.05), t(13) = 5.7, p < 0.001, and the mean failed stop RT was significantly slower for the S > G condition (420 ± 20 ms) than the G > S condition (328 ± 9 ms), t(13) = 5.7, p < 0.0001 ( Figure 2b ). The pattern of p(inhibit) and failed stop RT also replicated the results of the previous study (Leotti & Wager, 2010). However, the p(inhibit) was relatively low in the G > S condition, and although every subject provided at least 44 successful stop trials, the resulting SSRT estimate may be unreliable (Verbruggen & Logan, 2009b).

The mean Go RT was significantly faster for the G > S condition (373 ± 36 ms) than the S > G condition (494 ± 78 ms), t(13) = 6.1, p < 0.001 ( Figure 2b ). The mean total points earned for the G > S condition was 1205 ± 249 and for the S > G condition was 1148 ± 66, and this was not a significant difference t(13) = 0.92, p = 0.4. Combined, this pattern of Go RT and points earned indicates that participants were modifying their proactive control strategies in accordance with the different point contingencies, i.e. they favored stopping over going in the stop-rewarded condition and vice versa.

Three distinct ERP components were identified for successful and failed stop trials in both task conditions. These included the N2, the P3, and interestingly, the posterior visual N1. The fronto-central N2 peaked around 210 ms after the stop signal, centered over Cz ( Figure 3 ). The P3 component peaked around 300 ms after the stop signal, with a fronto-central topography, centered over Cz ( Figure 3 ). The posterior occipito-parietal N1 peaked first, approximately 190 ms after the onset of the stop signal, centered over electrode Oz ( Figure 4 ).

A) The average ERP measured at electrode Cz is presented for successful and failed stop trials for the two different task conditions. The N2 for failed stop trials and the P3 for all trial types are depicted. B) The ERP difference wave for successful - failed stopping is presented for the two different task conditions and shows a prominent difference between the two task conditions from

200 to 300 ms after the stop signal. C) Scalp topographies for the peak amplitude of the fronto-central P3 are presented for successful and failed stop trials for each condition along with the difference and t-value maps. SS = successful stop FS = failed stop G > S = going quickly earned 10 points and successful stopping earned 1 point S > G = successful stopping earned 10 points and going quickly earned 1 point.

A) The average ERP measured at electrode Oz is presented for successful and failed stop trials for the two different task conditions. B) The ERP difference wave for successful – failed stopping is presented for the two different task conditions and indicates that there were no condition differences in the N2. C) Scalp topographies for the peak amplitude of the occipito-parietal N1 are presented for successful and failed stop trials for each condition along with the difference and t-value maps. The emergence of the fronto-central N2 is also visible for failed stopping. SS = successful stop FS = failed stop G > S = going quickly earned 10 points and successful stopping earned 1 point S > G = successful stopping earned 10 points and going quickly earned 1 point.

For each subject, we derived the minimum (N1 and N2) and maximum (P3) peak amplitude of the ERPs at their corresponding electrode sites for successful and failed stop trials in each of the two task conditions. We ran separate ANOVA for each of the three peak ERP amplitudes with the factors condition (S > G vs. G > S) and type of stop trial (successful vs. failed).

Notably, we did not observe a fronto-central N2 component for successful stop trials in either the S > G or G > S condition, quantified at electrode Cz. However, we did observe an N2 at electrode Cz emerging around 200 ms for failed stop trials in both reward conditions, ( Figure 3a ). The N2 peak amplitude on failed stop trials was larger for the S > G than the G > S condition, t(13) = 3.6, p < 0.005.

As predicted, the P3 amplitude was larger for successful vs. failed stopping and was also larger for the S > G than G > S condition, F(1,13) = 117.3, p < 0.0001 and F(1,13) = 8.3, p < 0.05, respectively ( Figure 3a ). Moreover, there was a significant interaction, F(1,13) = 16.3, p < 0.01 ( Figure 3c ). Follow up t-tests indicated that the P3 amplitude difference between successful and failed stop trials was larger for the S > G condition than the G > S condition, t(13) = 4.0, p < 0.01, two-tailed.

The posterior N1 showed a much larger amplitude for the S > G condition for both successful and failed stop trials, F(1,13) = 25.7, p < 0.0001 ( Figure 4a and b ). Interestingly, in contrast to the chronologically later fronto-central P3, there was no main effect of successful vs. failed stopping and no interaction.

For our exploratory analysis of coherence between electrodes Cz and Oz on Go trials, PLV was significantly greater at 12 Hz for the S > G condition than the G > S condition (p < 0.05, FDR corrected) ( Figure 5 ). This difference emerged shortly following Go stimulus onset and persisted for approximately 150 ms. Importantly, this is well before the mean Go RT for the G > S condition (373 ms), and the S > G condition (494 ms). There were no other significant differences between the two task conditions at any other time point or frequency.

A) Phase coherence was calculated between electrodes Cz and Oz for the 373 ms following the target Go arrow for the S > G (top) and G > S (bottom) conditions. B) There was significantly greater phase coherence for the S > G than the G > S condition at 12 Hz, and this lasted for approximately 150 ms following Go target onset.

A wearable new technology moves brain monitoring from the lab to the real world

Imagine if a coach could know which moments of competition a certain player might peak, or if a truck driver had objective data telling him his body and mind were too tired to continue driving.

Traditionally, measuring alertness or mental fatigue requires interrupting a natural moment to intervene in an artificial setting. But Penn neuroscientist Michael Platt and postdoc Arjun Ramakrishnan have created a tool to use outside the lab, a wearable technology that monitors brain activity and sends back data without benching a player or asking a trucker to pull over.

The platform is akin to a Fitbit for the brain, with a set of silicon and silver nanowire sensors embedded into a head covering like a headband, helmet, or cap. The device, a portable electroencephalogram (EEG), is intentionally unobtrusive to allow for extended wear, and, on the backend, powerful algorithms decode the brain signals the sensors collect. Though it’s still in the early stages, the technology has potential applications from health care to sports performance and customer engagement.

Building a working prototype
“This all grew out of our desire as a group—and my strong conviction—to get neuroscience out of the lab and into the hands of people who could use it to reach their full potential,” says Platt, a Penn Integrates Knowledge Professor with appointments in the School of Arts and Sciences, Perelman School of Medicine, and Wharton School. “We naively thought that we could just take advantage of current market-ready solutions that were out there.”

But the more options the team tested, the more obvious it became that nothing was quite what they wanted. Most lacked high-quality sensors overall or had sensors whose quality dropped quickly once the wearer began moving. In early 2017, they decided to build their own portable EEG, getting a boost from a National Science Foundation-funded seed grant allocated by Penn’s Singh Center for Nanotechnology.

“We struggled for six or seven months to make a working sensor,” says Ramakrishnan, who has been part of the Platt Labs for three years. “We finally had our first working prototype in December 2017.”

The successful version, designed in part by research engineer Naz Belkaya, was made of a combination of silver and a silicon-like material called polydimethylsiloxane (PDMS). Silver is flexible, sensitive, and conductive PDMS is stretchy and can bend, properties similar to skin. Placing the PDMS on top of the silver nanowires made the product essentially antimicrobial and prevented the need to use gel to adhere it to the skin. This meant the sensors could comfortably stay in place for long periods of time.

With what they felt was a strong sensor technology in hand, Platt and Ramakrishnan began talking with PCI Ventures, a branch of the Penn Center for Innovation aimed at guiding University faculty through the process of starting a company. The licensing team at PCI helped them file a provisional patent for the product (originally called NanoNeuroScope), and Cogwear, LLC, was born in May 2018. In early 2019, the company hired a CEO, Patrick Wood, with the objective to figure out how to scale up production and which direction to aim first.

“There are huge numbers of potential applications,” Wood says. “That’s really a wonderful starting point for a company. We have great momentum.”

Sports performance and engagement
Platt and colleagues have already made strides testing their EEG technology in the sports arena. During the spring of 2019, a team led by Platt Labs postdoc Scott Rennie worked with Penn Rowing to study group chemistry, trust, communication, and brain synchrony—crucial for an activity that hinges on coordinated movement.

In a gym, the researchers put the sensors on the athletes, then analyzed them rowing on single, randomly selected machines next to teammates but on unlinked machines and on linked machines. EEG and heartrate readings showed that physiological syncing was unsurprisingly highest when teammates maneuvered on tethered machines and nearly as high when they trained next to each other untethered.

Rowing isn’t the only sport for which brain data may be useful. This summer, the researchers worked with a professional soccer team in the United Kingdom to evaluate the players’ focus during training drills, susceptibility to stress under pressure, and ability to predict and outwit opponents. An upcoming study with Penn Wrestling will measure fatigue’s influence on the neural signals underlying decision-making and on communication between wrestler and coach. Wood sees strong potential for a numbers-driven sport like baseball.

“You’ve got all players’ previous statistics, weight, dimension, all kinds of metrics, but you may need an additional data point about how mentally fit they are to withstand the pressure of standing at the plate about to hit the ball,” Wood says. “You may need to know more about each player before you can start comparing them.”

Beyond sports, Platt and team are testing the technology’s ability to determine engagement in group activities like a haunted house at Eastern State Penitentiary or a business conference put on by an enterprise solution company like SAP. “We did a brief pilot study in Las Vegas measuring brain activity and heart rate for people walking through an SAP trade show,” Platt says. “We found that heart rate didn’t vary at all it didn’t move. But measures of engagement from EEG data showed really interesting peaks and troughs. For the most part, people were not very engaged, brain-wise, except when talking to other people.”

This past May, the researchers conducted a larger study with SAP. For a focus group attending a conference, Platt’s team found that brain data helped predict which booths and activities people would visit. Much like with the pilot, social interactions seemed to maximize engagement.

“The current gold standard is emailing attendees a survey after the conference, which is a poor measure of engagement,” he explains. “We already have exciting results showing social interactions move the needle more than nonsocial ones and that we can, perhaps, make other predictions based on brain activity.”

A future in health care and beyond
Down the line, Platt and Ramakrishnan say they could see the health care industry employing this technology both for physical applications like in-home seizure monitoring for children and for mental health, to watch for changes in state of mind that might indicate anxiety or depression, for instance.

“About 40% of college-going students are anxious or depressed in the U.S. This is a staggering number,” Ramakrishnan says. The challenge is that people aren’t often self-aware regarding their own mental health situation, he adds. Backed by a Brain & Behavior Research Foundation grant, the Platt Labs team is working on a take-home kit that includes games and the portable EEG, which could objectively track several days of a person’s emotional peaks and valleys. An already-completed lab component showed that Platt’s team could identify participant levels of anxiety with about 84% accuracy using novel algorithms that combined EEG-based features with heart rate variability and skin conductance.

“This is the direction in which health care is going in general,” Ramakrishnan says. “There is a lot of promise for this sort of approach.”

All of the technology applications the research team has so far tested have focused on large-scale institutional use. But eventually any individual might be able to purchase an EEG product centered on the sensor technology, an addition to the ecosystem of individualized data-collection tools like smartwatches and sleep-monitoring apps already on the market.

“At its core, the advance we’re making here is the sensor technology, but in reality we leverage all the expertise we’ve developed over the last 25 years in terms of understanding and decoding brain signals,” Platt explains. “Then we can leverage those signals to make predictions about performance, user experience, customer engagement, all sorts of things. That’s the crux of actually monitoring the brain it allows us insights into function or dysfunction that people can’t or won’t self-report.”

Arjun Ramakrishnan and Scott Rennie are postdoctoral researchers in the Platt Labs. Naz Belkaya is a research engineer in the Platt Labs. Patrick Wood is the CEO of Cogwear, LLC.

Penn Center for Innovation team members who participated in the development of Cogwear include Neal Lemon, who helped Platt’s team file provisional and non-provisional patents Ryan Mendoza, who helped launch Cogwear and Jordana Barmish, who acted as the company’s interim CEO for Cogwear’s first nine months.

Funding for the project came from National Science Foundation (NSF) Grant NNCI-1542153 the P&S Fund and the Brain and Behavior Research Foundation FabNet™ award sponsored by Ben Franklin Technology Partners Penn Health-Tech and an NSF-funded Penn I-Corps grant.

Brain monitoring takes a leap out of the lab with first-of-its-kind dry EEG system

Bioengineers and cognitive scientists have developed the first portable, 64-channel wearable brain activity monitoring system that’s comparable to state-of-the-art equipment found in research laboratories.

The system is a better fit for real-world applications because it is equipped with dry EEG sensors that are easier to apply than wet sensors, while still providing high-density brain activity data. The system comprises a 64-channel dry-electrode wearable EEG headset and a sophisticated software suite for data interpretation and analysis. It has a wide range of applications, from research, to neuro-feedback, to clinical diagnostics.

The researchers’ goal is to get EEG out of the laboratory setting, where it is currently confined by wet EEG methods. In the future, scientists envision a world where neuroimaging systems work with mobile sensors and smart phones to track brain states throughout the day and augment the brain’s capabilities.

“This is going to take neuroimaging to the next level by deploying on a much larger scale,” said Mike Yu Chi, a Jacobs School alumnus and CTO of Cognionics who led the team that developed the headset used in the study. “You will be able to work in subjects’ homes. You can put this on someone driving.”

The researchers from the Jacobs School of Engineering and Institute for Neural Computation at UC San Diego detailed their findings in an article of the Special Issue on Wearable Technologies published recently in IEEE Transactions on Biomedical Engineering.

They also envision a future when neuroimaging can be used to bring about new therapies for neurological disorders. “We will be able to prompt the brain to fix its own problems,” said Gert Cauwenberghs, a bioengineering professor at the Jacobs School and a principal investigator of the research supported in part by a five-year Emerging Frontiers of Research Innovation grant from the National Science Foundation. “We are trying to get away from invasive technologies, such as deep brain stimulation and prescription medications, and instead start up a repair process by using the brain’s synaptic plasticity.”


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In 10 years, using a brain-machine interface might become as natural as using your smartphone is today, said Tim Mullen, a UC San Diego alumnus, now CEO of Qusp and lead author on the study. Mullen, a former researcher at the Swartz Center for Computational Neuroscience at UC San Diego, led the team that developed the software used in the study with partial funding from the Army Research Lab.

For this vision of the future to become a reality, sensors will need to become not only wearable but also comfortable, and algorithms for data analysis will need to be able to cut through noise to extract meaningful data. The paper, titled “Real-time Neuroimaging and Cognitive Monitoring Using Wearable Dry EEG,” outlines some significant first steps in that direction.

EGG headset

The EEG headset developed by Chi and his team has an octopus-like shape, in which each arm is elastic, so that it fits on many different kinds of head shapes. The sensors at the end of each arm are designed to make optimal contact with the scalp while adding as little noise in the signal as possible.

Researchers spent four years perfecting the recipe for the sensors’ materials. Sensors designed to work on a subject’s hair are made of a mix of silver and carbon deposited on a flexible substrate. This material allows sensors to remain flexible and durable while still conducting high-quality signals–a silver/silver-chloride coating is key here. Sensors designed to work on bare skin are made from a hydrogel encased inside a conductive membrane. These sensors are mounted inside a pod equipped with an amplifier, which helps boost signal quality while shielding the sensors from interferences from electrical equipment and other electronics.

Next steps include improving the headset’s performance while subjects are moving. The device can reliably capture signals while subjects walk but less so during more strenuous activities such as running. Electronics also need improvement to function for longer time periods–days and even weeks instead of hours.

Software and data analysis

The data that the headset captured were analyzed with software developed by Mullen and Christian Kothe, another former researcher at the Swartz Center for Computational Neuroscience and currently CTO of Qusp. First, brain signals needed to be separated from noise in the EEG data. The tiny electrical currents originating from the brain are often contaminated by high amplitude artifacts generated when subjects move, speak or even blink. The researchers designed an algorithm that separates the EEG data in real-time into different components that are statistically unrelated to one another. It then compared these elements with clean data obtained, for instance, when a subject is at rest. Abnormal data were labeled as noise and discarded. “The algorithm attempts to remove as much of the noise as possible while preserving as much of the brain signal as possible,” said Mullen.

But the analysis didn’t stop there. Researchers used information about the brain’s known anatomy and the data they collected to find out where the signals come from inside the brain. They also were able to track, in real time, how signals from different areas of the brain interact with one another, building an ever-changing network map of brain activity. They then used machine learning to connect specific network patterns in brain activity to cognition and behavior.

“A Holy Grail in our field is to track meaningful changes in distributed brain networks at the ‘speed of thought’,” Mullen said. “We’re closer to that goal, but we’re not quite there yet.”

Both Chi and Mullen have created start-ups focused on commercialization of brain technology, including some components featured in this study. Chi’s company, Cognionics, sells the headset to research groups. The device also is popular with specialists in neuro-feedback, who map the brain to later influence behavior. The ultimate goal is to get the headset into the clinic to help diagnose a range of conditions, such as strokes and seizures.

Mullen’s start-up, Qusp, has developed NeuroScale, a cloud-based software platform that provides continuous real-time interpretation of brain and body signals through an Internet application program interface. The goal is to enable brain-computer interface and advanced signal processing methods to be easily integrated with various everyday applications and wearable devices.

Under joint DARPA funding, Cognionics is creating an improved EEG system, while Qusp is developing an easy-to-use graphical software environment for rapid design and application of brain signal analysis pipelines.

“These entrepreneurial efforts are integral to the success of the Jacobs School and the Institute for Neural Computation to help take neurotechnology from the lab to practical uses in cognitive and clinical applications,” said Cauwenberghs, who is co-founder of Cognionics and serves on its Scientific Advisory Board.

Putting Patients First

At Nihon Kohden, every employee is driven by the idea that each patient touched by our products could be a member of our own family.

This belief is the origin of our commitment to the quality and reliability of our technology and services. It is also the reason we develop our products with our premium-as-standard philosophy and design it to work across the healthcare continuum, giving providers the ability to deliver care without compromise.

Because we know that every time one of our products is used, a person’s life is at stake.

And we understand just how precious every life is.

Search Methodology


In recent years, the number of portable, low-cost EEG-based systems available on the market has increased (Wei et al., 2018). Research examining the use of low-cost EEG systems has focused on the continuous recording of EEG data and/or the replication of larger EEG analytical systems using portable devices. In this review, we surveyed research papers that described the use of low-cost EEG devices, focusing on the devices where the headset was below $1,000 USD in price, independent of licensing fees: the InteraXon Muse, the Neurosky MindWave, the Emotiv Epoc, the Emotiv Insight, and the OpenBCI. These devices represent a sample of widely-used commercial models. Although other devices and suppliers have been used (Li and Chung, 2015), the search was focused on those non-invasive EEG devices that were below $1000, not marketed as medical devices, accessible to consumers, prominent in the hobbyist community, and have provided tools or options for brain-computer interface (BCI) applications. Table 1 presents a comparison of these commercial, low-cost EEG headsets. Most low-cost headsets use dry electrodes, which are more convenient for casual users. Similarly, most headsets come bundled with software that includes research tools, open-source software, and additional hardware (Lin et al., 2014 Farnsworth, 2017).

Watch the video: Tutorial How to Don the Wearable Sensing DSI 24 Dry EEG Headset (May 2022).


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