2.2.  Emotiv

Emotiv is another manufacturer of consumer-grade EEG devices, focusing on multichannel measurements with wet electrodes. Given their capabilities, Emotiv devices allow more complex tasks, such as creating and executing mental commands [19]^[15], or facial expression detection [20]^[16], 3D functional mind maps for data visualization [21]^[17]. Three Emotive systems reached the mainstream market: EPOC+, INSIGHT, and EPOC FLEX [22]^[18]. EPOC+ is a device focused on academic research, contains electrodes that are placed around the scalp and several built-in motion gyro, accelerometer, and magnetometer sensors. The company also released a cheaper alternative, called INSIGHT, with a lower number of channels, semidry electrodes, and was designed for everyday usage rather than for academic research. Finally, the lineup is completed by EPOC FLEX, a medical-grade product build with multiple sensors and other features, including a control box for wireless customization [22]^[19].

Consumer-grade products from Emotiv were used in different studies. For example, in 2013, Emotiv EPOC was used by Badcock et al.^[20] [23] to make research auditory event-related potentials (ERP) in adults and children [24]^[21]. The two studies were later followed by further ERP investigation using consumer^[22] [25] and medical [26] ^[23] data acquisition procedures and equipment.

The EPOC system was repeatedly compared with medical-grade systems; Debener et al. represented some modifications to the Emotiv system and combined the hardware with a medical-grade EEG electrode cap^[24] [27] in 2012, then, in 2017, Barham et al. did the same modifications into Emotiv EPOC to evaluate it against a medical device [25]^[22]. Both ERP and MMN waveforms obtained from the Emotiv system shown similar results to the medical-grade device and to the results reaching in the Badcock et al. study [23]^[20]. The total data obtained from the modified gadget was higher than the one from the original report made by Badcock et al., mostly due to more accurate electrode placement and higher electrode quality.

Regardless of positive indications, there have also been reports about low-quality EEG data and poor performance regarding Emotiv EPOC. Duvinage studied the performance of Emotiv EPOC for P300-based applications^[25][26] [28,29] and against advanced neurotechnology (ANT), a medical-grade EEG system [30]^[27]. The overall performance and SNR of Emotiv EPOC were poorer than the ones produced by ANT. Similar to MindWave, the Emotiv EPOC system was used to validate attention and meditation results along with the blinking recognition [1]^[1]; Emotiv led to 75.6% accuracy for blinking recognition and 60.5% accuracy for classification of focusing or relaxing. During the tests, only a small minority of subjects have been remarked a discomfort after a long period of gadget usage, but this was mentioned before in another research [31]^[28].

To summarize, when compared to medical-grade devices in scientific research data collection, the Emotiv EPOC system faces considerable challenges in data quality, sensor location, and electrode number. While not specifically highlighted by the prior studies, Emotiv EPOC does not need a flexible EEG cap. Therefore it does not require custom fitting; also, due to its form factor, it would allow the longer subject screening. The modified Emotiv EPOC [25^[22],27], which was previously mentioned, has been proven as useful and also improved regarding competing against its competitors.

2.3. InteraXon

Muse, manufactured by InteraXon, is a headband with inbuilt brainwave sensors designed to facilitate meditation. In contrast to the products from NeuroSky and Emotiv, the fitting of electrodes from Muse can be adjusted to the head of the user. The most sensitive areas of the head are located under the Muse’s electrodes, which help to obtain the emotion-specific signals [32]^[29]. In addition to measuring and monitoring the meditative state of the user, Muse also has a mobile application for meditation training (https://choosemuse.com/muse-app/). Following the launch of Muse, InteraXon expanded their product range with Muse 2, which offers more features (body movement, heart rate, and respiratory rate) and a software development kit (SDK) (https://github.com/NeuroJS/angular-muse), as well as a strong open-source base in GitHub (https://github.com/kowalej/BlueMuse).

In a study aimed at evaluating their accuracy in measuring ERPs, the Muse products^[30] [33] were used to obtain N200 and P300 ERP components in the oddball visual task and stimulate positivity during a reward-learning task. The results were compared to the signals resulted from Brain Vision actiCHamp System, which is considered to be the standard medical system. The study concluded that, while the performance of the two systems was relatively similar, the actiCHamp does deliver more accurate results. In the wider domain, there is a concern about data quality that Muse’s configuration may not meet the criteria dictated by the guidelines for ERP’s to study cognition (which includes specific requirements for formulation of the study, subjects profile, stimuli and responses, A/D signal conversion, signal analysis, non-cerebral artifact, presentation of data, and so on) [34]^[31], requiring one nonpolarized Ag/AgCl electrode for measuring the slow changes in potential and multiple electrodes which should note overlapping ERP components, as well as an adequate rejection ratio of the amplifier. The Muse headband is fitted with conductive silicon-rubber (dry) electrodes, which should be placed in four different locations. This access, focusing on the frontal part of the head, though not comparable with other consumer-grade EEG systems, allows data collection and validation, as presented by [33] ^[30] and, with some adjustments, can produce higher quality signals.

In contrast to the medical-grade devices, the method to accurately label the time of experimental stimulations has been a major concern for researchers, as event markers are often needed to be created separately. In comparison to wired connections, Bluetooth is rather unstable and can introduce random delay variations [35,36]^[32][33].

The EEG data acquisition by Muse was compared to few medical-grade gadgets [6]^[6]. The data obtained from Muse shows an increase in broadband power and more signals, performance, and electrode placements, suggesting lower reliability. In addition, the data displayed by Muse has poor consistency between multiple measurements for the same subjects in comparison to other products (B-Alert, Enobio, and MindWave). The reason behind the inconsistency could be due to the mobile electrodes of the headband, which can be misplaced, an issue also confirmed by the conclusions of [33]^[30].

2.4. OpenBCI

OpenBCI was introduced to the market in 2013 as open-source hardware and firmware research alternative. Unlike consumer-grade EEG products, OpenBCI hardware mostly serves as an amplifier, where the activities detected in the brain (EEG), muscles (EMG), and heart (EKG) can be measured by using the sensors equipped on the schematic OpenBCI printed circuit boards and interpreted using software named Processing. Due to its focus, OpenBCI has a limited market share and is more appropriate for users who have a background in engineering. A number of studies validated OpenBCI against traditional EEG systems. In 2016, Frey represented EEG signals obtained from OpenBCI’s board versus a medical-grade EEG amplifier (g.tec g.USBamp) [37]^[34]. According to the obtained results, OpenBCI performance (measured using a combination of subjective, questionary-based, and objective, physiology-based indicators) was identical to the traditional EEG amplifier from g.tec for both P300 speller and EEG-based workload tracking by using the n-back task [38]^[35]. In a similar study [39]^[36], OpenBCI, equipped with Texas Instruments ADS1299, was compared to Compumedics Neuroscan NuAmps, a medical-grade EEG system original; the comparative analysis confirmed that the two systems produced similar results in terms of brain activity and frequency analysis. Regardless of these similarities, MRCPs are not normally benchmarked against the EEG applications, as they are designed entirely to help people with muscular disability to improve their quality of life and require movement, which can also cause incidents of cable swaying and undesirable head movements.

2.5. Other Products

Due to growing demand and rapid technology development, a wide range of non-research products were developed and released on the market. A BCI-based game controlling tool—Neural Impulse Actuator—was launched to replace the usage of a keyboard and mouse. Two other developers, X wave [12]^[12] and Melon [13]^[13], proposed products aiming to monitor user attention. More recently, a number of products appeared in the wellness industry by integrating the EEG data collection platforms: MyndPlay used NeuroSky TGAM technology to develop their headband; mostly, the company sells its apps. This application is usually used for entertainment purposes; however, it additionally touches brain training and educational areas. Aurora Dreamband [14]^[14], which is designed as a headband with built-in sensors, has the ability to detect the user’s motion and track their sleep pattern. FocusBand [15] ^[5] development was initiated in 2009 and finally was launched in 2016. In the beginning, the headset and app were manufactured with the goal to assist golfers to monitor and note their mental state and physical performance from the mentioned neurofeedback. To allow users to observe their performance in the working environment and sleeping condition, FocusBand expanded its targeted market of wearable headsets releasing NeuroSelf Care Business and NeuroSleep. The SenzeBand device is an EEG headband which has four channels and two reference sensors that can track the attention, focus, mental workload, and relaxation levels. Additionally, the company provides apps/games for mental training, managing stress, and keeping track of brain activities. The manufacturer of SenzeBand—Chief Scientist of Neuro—is an IEEE fellow, who as a professor at Nanyang Technological University, has been publishing numerous papers on neurofeedback/brain training game [7,40,41]^[7][37].

3. Discussion

From the analysis conducted in the previous section, we summarize the following key findings concerning the review of studies in the area of applicability the consumer-grade EEG products.

A comprehensive review was provided for the leading products, based on the existing, published research studies. Given the emphasis for these devices is placed on the application domains rather than obtaining accurate readings, information about their technical characteristics is somewhat limited, describing the measurement range rather than the sensitivity or error levels. Currently, products from the four main manufacturers, called, NeuroSky, Emotiv, InteraXon, and OpenBCI, of consumer-grade EEG devices are available for purchase. Among the four, NeuroSky products appear the most successful in seizing the market due to lower prices, simplicity, and ease of use, including a single-channel EEG sensor. Unlike NeuroSky, Emotiv devices focused on the cognition and gaming fields due to two main reasons—data collection across 14 channels and being more sensitive than NeuroSky, providing it with the ability to simultaneously examine more brain regions and signals. An interesting alternative to EEG devices has been the one provided by Muse. Primarily designed for meditation, recent Muse devices have an accelerometer, heart rate, and respiratory rate sensors built-in, all of which made the product more attractive and allowed consumers to monitor more physiological factors. Comparing to the alternative ready-to-wear devices, OpenBCI offers a less user-friendly product, which is essentially a circuit board. Its open-end design (open-source hardware, firmware, and GUI) made it very appealing for engineering-related research studies in the BCI field. The focus for OpenBCI has been indeed the acquisition and processing of signals rather than its usability. The full list of the products with their characteristics is presented in Table 1.

4. Conclusion

These low-cost devices can be counted as a tangible outcome of the advancement in medical technology and BCI, which has made a great leap into the consumer market. The lower price means increasing accessibility and realizability, which simplifies for researchers to study attention and subjects’ mental state without going deeply into electronics and engineering questions. These devices may unlock several functions for public usage, be extended into education and gaming industries, but their functionality is limited in comparison to the medical-grade counterparts. Some grave disadvantages of these products from NeuroSky include low sensitivity because of the presence of a single channel in addition to inflexible location and electrode type. The EEG sensors of these gadgets responsible for reading attention and meditation conditions, due to their portability aspects, are likely to yield less accurate readings, but they can easily be adapted to evaluate student attention during a classroom or to trigger some events in the application. While NeuroSky was used in a small number of studies, its products have been used in many high-ranking publications, especially as part of educational studies. In contrast, Emotiv, with its 14 channels, brings better usage of signals while retaining its portability and was particularly preferred for validation studies. Muse is a rather new device, primarily aimed at meditation, but already being used in a wide variety of studies, especially if requiring a larger number of channels. At the opposite end of the consumer-grade product, OpenBCI has high adaptability in a circuit board, requires prior understanding of electronics, bio-signal processing, and brain anatomy/physiology, but its full functionality and open source materials permit the users to develop and expand their applications and usage more freely and flexibly. Since it can be used with standard Ag/AgCl electrodes and accommodate up to 16 channels, the highest quality signals among the consumer-grade gadgets can technically be acquired using OpenBCI.

Reference (we'll rearrange the references after you submitted it)

1. Maskeliunas, R.; Damasevicius, R.; Martisius, I.; Vasiljevas, M. Consumer-grade EEG devices: Are they usable for control tasks? PeerJ 2016, 4, e1746.
2. Baar, E.; Baar-Eroglu, C.; Karaka, S.; Schrmann, M. Gamma, alpha, delta, and theta oscillations govern cognitive processes. Int. J. Psychophysiol. 2001, 39, 241–248.
3. Teplan, M. Fundamentals of EEG measurement. Measur. Sci. Rev. 2002, 2, 1–11.
4. Van Der Stelt, O.; Belger, A. Application of electroencephalography to the study of cognitive and brain functions in schizophrenia. Schizophr. Bull. 2007, 33, 955–970.
5. Herrmann, C.S.; Struber, D.; Helfrich, R.F.; Engel, A.K. EEG oscillations: From correlation to causality. Int. J. Psychophysiol. 2016, 103, 12–21.
6. Ratti, E.; Waninger, S.; Berka, C.; Ruffini, G.; Verma, A. Comparison of medical and consumer wireless EEG systems for use in clinical trials. Front. Hum. Neurosc. 2017, 11, 398.
7. Lim, C.G.; Lee, T.S.; Guan, C.; Fung, D.S.S.; Zhao, Y.; Teng, S.S.W.; Zhang, H.; Krishnan, K.R.R. A brain-computer interface-based attention training program for treating attention deficit hyperactivity disorder. PLoS ONE 2012, 7, e46692.
8. Ma, Y.; Zhang, S.; Qi, D.; Luo, Z.; Li, R.; Potter, T.; Zhang, Y. Driving drowsiness detection with EEG using a modified hierarchical extreme learning machine algorithm with particle swarm optimization: A pilot study. Electronics 2020, 9, 775.
9. Ilyas, M.; Othmani, A.; Fournier, R.; Nait-ali, A. Auditory perception based anti-spoofing system for human age verification. Electronics 2019, 8, 1313.
10. Zhang, H.L.; Lee, S.; Li, X.; He, J. EEG self-adjusting data analysis based on optimized sampling for robot control. Electronics 2020, 9, 925.
11. Nakanishi, I.; Maruoka, T. Biometrics using electroencephalograms stimulated by personal ultrasound and multidimensional nonlinear features. Electronics 2019, 9, 24.
12. XWave. Available online: https://www.eyecomtec.com/3405-XWave/ (accessed on 5 November 2020).
13. Melon: A Headband and Mobile App to Measure Your Focus. Available online: https://www.kickstarter.com/projects/806146824/melon-a-headband-and-mobile-app-to-measure-your-fo (accessed on 5 November 2020).
14. Aurora. Available online: https://sleepwithaurora.com/ (accessed on 5 November 2020).
15. FocusBand. Available online: https://focusband.com/ (accessed on 5 November 2020).
16. Roesler, O.; Bader, L.; Forster, J.; Hayashi, Y.; Heßler, S.; Suendermann-Oeft, D. Comparison of EEG Devices for Eye State Classification. In Proceedings of the AIHLS, Kusadasi-Aydin, Turkey, 19–22 October 2014.
17. Arai, K.; Mardiyanto, R. Real time blinking detection based on gabor filter. IJHCI 2010, 1, 33–45.
18. Abo-Zahhad, M.; Ahmed, S.M.; Abbas, S.N. A novel biometric approach for human identification and verification using eye blinking signal. IEEE Signal Proc. Lett. 2014, 22, 876–880.
19. Mental Commands. Available online: https://www.emotiv.com/knowledge-base/training-mental-commands/ (accessed on 5 November 2020).
20. Facial Expression Detections. Available online: https://www.emotiv.com/knowledge-base/facial-expression-detections/ (accessed on 5 November 2020).
21. Applications. Available online: https://www.emotiv.com/product-category/applications/ (accessed on 5 November 2020).
22. Headset Comparison Chart. Available online: https://www.emotiv.com/comparison/ (accessed on 5 November 2020).
23. Badcock, N.A.; Mousikou, P.; Mahajan, Y.; De Lissa, P.; Thie, J.; McArthur, G. Validation of the Emotiv EPOC EEG gaming system for measuring research quality auditory erps. PeerJ 2013, 1, e38.
24. Badcock, N.A.; Preece, K.A.; de Wit, B.; Glenn, K.; Fieder, N.; Thie, J.; McArthur, G. Validation of the Emotiv EPOC EEG system for research quality auditory event-related potentials in children. PeerJ 2015, 3, e907.
25. Barham, M.; Clark, G.M.; Hayden, M.J.; Enticott, P.G.; Conduit, R.; Lum, J.A. Acquiring research-grade ERPS on a shoestring budget: A comparison of a modified Emotiv and commercial Synamps EEG system. Psychophysiology 2017, 54, 1393–1404.
26. Schiff, S.; Casa, M.; Di Caro, V.; Aprile, D.; Spinelli, G.; De Rui, M.; Angeli, P.; Amodio, P.; Montagnese, S. A low-cost, user-friendly electroencephalographic recording system for the assessment of hepatic encephalopathy. Hepatology 2016, 63, 1651–1659.
27. Debener, S.; Minow, F.; Emkes, R.; Gandras, K.; De Vos, M. How about taking a low-cost, small, and wireless EEG for a walk? Psychophysiology 2012, 49, 1617–1621.
28. Duvinage, M.; Castermans, T.; Petieau, M.; Hoellinger, T.; Cheron, G.; Dutoit, T. Performance of the Emotiv Epoc headset for P300-based applications. Biomed. Eng. Online 2013, 12, 56.
29. Duvinage, M.; Castermans, T.; Dutoit, T.; Petieau, M.; Hoellinger, T.; De Saedeleer, C.; Seetharaman, K.; Cheron, G. A P300-based quantitative comparison between the Emotiv Epoc headset and a medical EEG device. Biomed. Eng. 2012, 765, 2012–2764.
30. ANTneuro. Available online: https://www.ant-neuro.com/ (accessed on 5 November 2020).
31. Harrison, T. The Emotiv Mind: Investigating the Accuracy of the Emotiv Epoc in Identifying Emotions and its Use in an Intelligent Tutoring System; Honors Report; University of Canterbury: Canterbury, New Zealand, 2013.
32. Lei, X.; Liao, K. Understanding the influences of EEG reference: A large-scale brain network perspective. Front. Neurosci. 2017, 11, 205.
33. Krigolson, O.E.; Williams, C.C.; Norton, A.; Hassall, C.D.; Colino, F.L. Choosing muse: Validation of a low-cost, portable EEG system for ERP research. Front. Neurosci. 2017, 11, 109.
34. Picton, T.; Bentin, S.; Berg, P.; Donchin, E.; Hillyard, S.; Johnson, R.; Miller, G.; . Ritter, W.; Ruchkin, D.; Rugg, M.; et al. Guidelines for using human event-related potentials to study cognition: Recording standards and publication criteria. Psychophysiology 2000, 37, 127–152.
35. Shorey, R.; Miller, B.A. The Bluetooth technology: Merits and limitations. In Proceedings of the IEEE International Confrence on Personal Wireless Communications, Hyderabad, India, 17–20 December 2000.
36. Luque, J.R.; Moron, M.J.; Casilari, E. Analytical and empirical evaluation of the impact of gaussian noise on the modulations employed by bluetooth enhanced data rates. EURASIP J. Wirel. Comm. Netw. 2012, 2012, 94.
37. Frey, J. Comparison of a consumer grade EEG amplifier with medical grade equipment in BCI applications. In Proceedings of the International BCI Meeting, Asilomar, CA, USA, 30 May–3 June 2016.
38. Muhl, C.; Jeunet, C.; Lotte, F. EEG-based workload estimation across affective contexts. Front. Neurosci. 2014, 8, 114.
39. Rashid, U.; Niazi, I.; Signal, N.; Taylor, D. An EEG experimental study evaluating the performance of Texas Instruments ads1299. Sensors 2018, 18, 3721.