Relevant bibliographies by topics / NeuroSky MindWave

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'NeuroSky MindWave'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "NeuroSky MindWave"

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MANSOOR, M., B. NAZ, R. JAFFARI, and A. ANSARI. "Brain Controlled Wheelchair with Neurosky Mindwave." SINDH UNIVERSITY RESEARCH JOURNAL -SCIENCE SERIES 51, no. 04 (December 10, 2019): 709–12. http://dx.doi.org/10.26692/surj/2019.12.112.

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Rieiro, Héctor, Carolina Diaz-Piedra, José Miguel Morales, Andrés Catena, Samuel Romero, Joaquin Roca-Gonzalez, Luis J. Fuentes, and Leandro L. Di Stasi. "Validation of Electroencephalographic Recordings Obtained with a Consumer-Grade, Single Dry Electrode, Low-Cost Device: A Comparative Study." Sensors 19, no. 12 (June 23, 2019): 2808. http://dx.doi.org/10.3390/s19122808.

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The functional validity of the signal obtained with low-cost electroencephalography (EEG) devices is still under debate. Here, we have conducted an in-depth comparison of the EEG-recordings obtained with a medical-grade golden-cup electrodes ambulatory device, the SOMNOwatch + EEG-6, vs those obtained with a consumer-grade, single dry electrode low-cost device, the NeuroSky MindWave, one of the most affordable devices currently available. We recorded EEG signals at Fp1 using the two different devices simultaneously on 21 participants who underwent two experimental phases: a 12-minute resting state task (alternating two cycles of closed/open eyes periods), followed by 60-minute virtual-driving task. We evaluated the EEG recording quality by comparing the similarity between the temporal data series, their spectra, their signal-to-noise ratio, the reliability of EEG measurements (comparing the closed eyes periods), as well as their blink detection rate. We found substantial agreement between signals: whereas, qualitatively, the NeuroSky MindWave presented higher levels of noise and a biphasic shape of blinks, the similarity metric indicated that signals from both recording devices were significantly correlated. While the NeuroSky MindWave was less reliable, both devices had a similar blink detection rate. Overall, the NeuroSky MindWave is noise-limited, but provides stable recordings even through long periods of time. Furthermore, its data would be of adequate quality compared to that of conventional wet electrode EEG devices, except for a potential calibration error and spectral differences at low frequencies.
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Quino Ortiz, Bryan, José de Jesús Moreno Vázquez, Aldo Rafael Sartorius Castellanos, Antonia Zamudio Radilla, and Marcia Lorena Hernández Nieto. "Metodología de conexión utilizando NeuroSKY Mindwave MW003 con MATLAB." EPISTEMUS 13, no. 27 (December 1, 2020): 7–12. http://dx.doi.org/10.36790/epistemus.v13i27.110.

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En la actualidad el ímpetu por comprender el funcionamiento del encéfalo ha motivado a compañías como Neurosky en crear e innovar diademas para la obtención de señales encefalográfícas de bajo costo y gran exactitud, enfocadas a la venta para todo tipo de usuario. En el presente trabajo se mostrará la metodología de conexión de la diadema Neurosky MindWave MW003 efectuando el proceso de recepción, envío y configuración inalámbrica (Bluetooth) con el computador, haciendo uso de la librería Thinkgear.h impartida por la empresa Neurosky, realizando un explicación breve y concisa para el uso del dispositivo, estableciendo las características, métodos de operación y funciones principales para su conexión, utilizando la herramienta MATLAB R2015B, el proceso se describe sistemáticamente enfocándose a usuarios inexpertos en la resolución de sus dudas, así mismo contribuir al usuario experimentado en lenguajes de alto nivel en la creación de nuevas aplicaciones.
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Maskeliunas, Rytis, Robertas Damasevicius, Ignas Martisius, and Mindaugas Vasiljevas. "Consumer grade EEG devices: are they usable for control tasks?" PeerJ 4 (March 22, 2016): e1746. http://dx.doi.org/10.7717/peerj.1746.

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We present the evaluation of two well-known, low-cost consumer-grade EEG devices: the Emotiv EPOC and the Neurosky MindWave. Problems with using the consumer-grade EEG devices (BCI illiteracy, poor technical characteristics, and adverse EEG artefacts) are discussed. The experimental evaluation of the devices, performed with 10 subjects asked to perform concentration/relaxation and blinking recognition tasks, is given. The results of statistical analysis show that both devices exhibit high variability and non-normality of attention and meditation data, which makes each of them difficult to use as an input to control tasks. BCI illiteracy may be a significant problem, as well as setting up of the proper environment of the experiment. The results of blinking recognition show that using the Neurosky device means recognition accuracy is less than 50%, while the Emotiv device has achieved a recognition accuracy of more than 75%; for tasks that require concentration and relaxation of subjects, the Emotiv EPOC device has performed better (as measured by the recognition accuracy) by ∼9%. Therefore, the Emotiv EPOC device may be more suitable for control tasks using the attention/meditation level or eye blinking than the Neurosky MindWave device.
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Salih, Thair A., and Yasir M. Abdal. "Brain computer interface based smart keyboard using neurosky mindwave headset." TELKOMNIKA (Telecommunication Computing Electronics and Control) 18, no. 2 (April 1, 2020): 919. http://dx.doi.org/10.12928/telkomnika.v18i2.13993.

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H, Hakimi, M., Salleh, S. M, Ainul, H. M. Y, Ngali, M. Z, Ismail, A. E, Rahman, M. N. A, and Mahmud, W. M. A. W. "Ice Bath Therapy on Athletes Recovery Response Using EEG." International Journal of Engineering & Technology 7, no. 4.30 (November 30, 2018): 438. http://dx.doi.org/10.14419/ijet.v7i4.30.22361.

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Sport recovery system is an integral aspect to help athletes adapt faster to training. This is an important process of physical preparation by reducing fatigue where the athletes can ready for the next competition or training. However, most of an athlete doing training without having the fully recovery after the training and can affect their performance. The cold bath water immersion is the one of common technique to recover from the fatigue. In this study, Neurosky mindwave is use to extract the brain wave of an athlete to know the response of an athlete when perform the cold water immersion. The responses of an athlete include meditation which is in alpha wave that state in relax condition and beta wave that is in fatigue condition in sport. The raw brain wave signal that extract using Neurosky mindwave is analysed using Matlab in terms of time domain. After that, Fast Fourier Transform (FFT) will use to analysed in terms of frequency domain. This project used alpha and beta band to collect the data. The analysis have made based on the peak value in frequency domain to know the best time for cold water immersion and best cold bath temperature.
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Bohm Machado, Giovanni, and Leandro Krug Wives. "Leitura de ondas cerebrais como ferramenta para escolha das melhores práticas pedagógicas por parte dos docentes: um estudo quase experimental com estudantes do ensino superior." RENOTE 17, no. 3 (December 31, 2019): 61–70. http://dx.doi.org/10.22456/1679-1916.99427.

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O objetivo deste estudo foi identificar, dentre três métodos/ferramentas/formas distintas de ensino, quais despertam maior interesse e retém a atenção dos estudantes do ensino superior. Com essa finalidade, foi realizado um quase experimento com 57 estudantes de curso superior de uma Universidade privada da região metropolitana de Porto Alegre entre os anos de 2018 e 2019. Foi utilizado um aparelho medidor de ondas cerebrais chamado Mindwave NeuroSky para registrar os níveis de atenção e concentração em diferentes tipos de aulas: expositivas, vídeo e gamificadas. Após testes estatísticos de comparação entre os grupos estudados, os principais resultados indicaram maior efetividade para vídeo-aulas e aulas gamificadas, com aulas expositivas ficando em último lugar.
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Sittiprapaporn, Phakkharawat, and Shao-Chin Chang. "Electroencephalographic study of real-time arithmetic task recognition in learning disabilities children." Asian Journal of Medical Sciences 10, no. 1 (December 11, 2018): 43–46. http://dx.doi.org/10.3126/ajms.v10i1.21035.

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Background: Mathematics form is universally understood in modern society. It is natural to ask whether there is neurophysiological evidence for putative cognitive components of the task and the practice in mental arithmetic will lead to a detectable transition in primary locus of brain activity. Aims and Objectives: The purpose of this study was to determine the effect of real-time arithmetic recognition task for cognitive performance and electroencephalographic activities. Materials and Methods: While practicing the real-time arithmetic recognition task named SpeedMath developed by NeuroSky, Inc., electroencephalographic activities were also recorded by using the commercial lightweight electroencephalographic device, Mindwave Mobile, NeuroSky, Inc. Eight participants included learning disabilities children participating in this study. All participants were instructed and trained to practice the simple the arithmetic recognition task which included arithmetic and mathematic skills. Results: The results showed that both alpha and beta frequency bands were increased with statistically significant at the 0.05 level. Conclusions: The real-time arithmetic recognition task might improve the real-time arithmetic recognition task and performance. Asian Journal of Medical Sciences Vol.10(1) 2019 43-46
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Brilian, Ahmad Hayam, Handayani Tjandrasa, and Chastine Fatichah. "PENGENALAN SANDI MORSE DARI SINYAL ELECTROENCEPHALOGRAM YANG DIREKAM PERANGKAT NEUROSKY MINDWAVE MENGGUNAKAN DYNAMIC TIME WARPING." JUTI: Jurnal Ilmiah Teknologi Informasi 14, no. 1 (January 1, 2016): 63. http://dx.doi.org/10.12962/j24068535.v14i1.a511.

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R, Jeevareha, and Tharini M. "EEG Based Brain Controlled Keypad and Devices." International Research Journal of Multidisciplinary Technovation 2, no. 3 (May 30, 2020): 27–33. http://dx.doi.org/10.34256/irjmt2035.

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In our society there are many people suffer by paralytic diseases which holds them with several disabilities like unable to talk and unable to move physically and unable to express their everyday basic needs, but can still use their eyes and sometimes move their heads. This Project is under the principle of Brain Computer Interface (BCI). Our model helps them to do all their basic needs just from their wheelchair. All applications are integrated in the wheelchair, such as they can drive their wheelchair on their own using either meditation/attention or eye blink, Keypad for communication, Emergency commands for requesting their basic need such as food, water, restroom etc., Home automation for operating appliances, all those applications are done by having eye blinks using EEG device- Neurosky mindwave mobile.
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Dissertations / Theses on the topic "NeuroSky MindWave"

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Žiemys, Tadas. "Paveikslų įvertinimo prognozavimo tyrimas naudojant Neurosky Mindwave įrenginį." Master's thesis, Lithuanian Academic Libraries Network (LABT), 2012. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2012~D_20120620_113900-37800.

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Darbo pradžioje apžvelgiamos vartotojo sąsajos, kokios yra ateities perspektyvos smegenų-kompiuterio sąsajoms, kokie tyrimai atliekami šioje srityje. Aprašoma, kas yra elektroencefalografija (EEG), kaip atsiranda elektriniai impulsai smegenyse ir ką nagrinėja neuroestetikos mokslas. Magistrinio darbo tikslas buvo ištirti, kokiu tikslumu galima prognozuoti, ar subjektui patiks paveikslas. Prognozės yra atliekamos iš subjekto EEG signalų, kurie yra gaunami naudojant Neurosky MindWave įrenginį. Duomenų klasifikavimui yra naudojamas vienasluoksnis neuroninis tinklas sukurtas su MATLAB programine įranga. Darbo metu buvo sukurta iPad kompiuteriui skirta programa, kuri vaizduoja stimulus ir įrašinėja EEG duomenis iš Neurosky MindWave įrenginio. Stimulams yra naudojami įvairių laikotarpių, skirtingų autorių tapybos darbai. Tyrimas parodė, kad įmanoma prognozuoti vartotojo pasirikimą tiksliau negu atsitiktinumas. Klasifikatoriaus tikslumas daugiausia duomenų turinčiam subjektui siekia iki 74%. Tuo tarpu apmokant dirbtinį neuroninį tinklą visų subjektų duomenimis, klasifikatoriaus tikslumas yra labai panašus į atsitiktinumą – 55%.
The aim of this thesis is to study the accuracy of prediction of subject’s preferences when observing various paintings. Prediction is caried out using subject’s EEG signals which are produced using Neurosky MindWave device. To clasify data one layer artificial neuron network is used. The network is a MATLAB application. For this study application for iPad is created. This application represents and records EEG data from Neurosky MindWave device. In order to produce stimuli subjects are presented with paintings by various artists from different epoches. The study showed that it is posible to predict the preference with accuracy greater than a mere chance. The classifier’s accuracy for the subject with most data is up to 74% (average 64.58%). For other subjects’ the accuracy of prediction of 58% is reached.
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Anandani, Vijay. "Autonomous vehicle control using electroencephalography signals extracted from NeuroSky MindWave device." Thesis, California State University, Long Beach, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10182137.

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The current project presents the hardware implementation and experimental testing of a system that uses electroencephalography (EEG) signals to control the motions of a vehicle through a brain-computer interface device. The user's brain activity is monitored continuously by the NeuroSky MindWave headset, and the EEG signals are processed and provided as inputs to the vehicle control system. The brain functions of interest are the user's attention level, meditation level and ocular blink rate. The values of these signals are transmitted to a microcontroller, which will command the vehicle's motor to initiate motion, stop, or change direction based on the user's brain activity. The current project can find a significant number of applications, since about 17% of the population have disabilities and one million people use wheelchairs, including manually and electrically powered chairs.

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Vélez, Luis, and Guillermo Kemper. "Algorithm for Detection of Raising Eyebrows and Jaw Clenching Artifacts in EEG Signals Using Neurosky Mindwave Headset." Smart Innovation, Systems and Technologies, 2021. http://hdl.handle.net/10757/653818.

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El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.
The present work proposes an algorithm to detect and identify the artifact signals produced by the concrete gestural actions of jaw clench and eyebrows raising in the electroencephalography (EEG) signal. Artifacts are signals that manifest in the EEG signal but do not come from the brain but from other sources such as flickering, electrical noise, muscle movements, breathing, and heartbeat. The proposed algorithm makes use of concepts and knowledge in the field of signal processing, such as signal energy, zero crossings, and block processing, to correctly classify the aforementioned artifact signals. The algorithm showed a 90% detection accuracy when evaluated in independent ten-second registers in which the gestural events of interest were induced, then the samples were processed, and the detection was performed. The detection and identification of these devices can be used as commands in a brain–computer interface (BCI) of various applications, such as games, control systems of some type of hardware of special benefit for disabled people, such as a chair wheel, a robot or mechanical arm, a computer pointer control interface, an Internet of things (IoT) control or some communication system.
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Johansson, Claes. "Hjärnvågsavläsning i spel : En undersökning om användbarheten av hjärnvågsavläsning som direkt kontrollmetod för spel." Thesis, Högskolan i Skövde, Institutionen för kommunikation och information, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-5952.

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BCI (Brain Computer Interface) för användning i spel har börjat dyka upp på konsument-marknaden. Dessa använder ofta EEG för att mäta spelarnas avslappningsnivåer och den spelare som lyckas nå högsta meditativa tillstånd samlar poäng. I detta arbete undersöks huruvida det går att använda BCI för mer direkt kontroll av en specifik spelmekanik i kombination med konventionella spelkontroller. För undersökningen har två versioner av ett spel skapats med avsikten att mäta skillnaden i hjärnaktiviteten hos spelare som bara spelar, jämfört med spelare som aktivt försöker åstadkomma ett specifikt sinnestillstånd för att direkt kontrollera en funktion i spelet. På grund av studiens begränsade omfattning kunde inga definitiva slutsatser dras men det finns indikationer på att MindWave, som var utrustningen som användes i denna studie, inte är lämplig som direkt kontrollmetod tillsammans med andra kontroller. Studien skulle kunna fungera som en pilotstudie för en mer omfattande undersökning inom ämnet.
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Mendes, Gabriel Alves Vasiljevic. "Brain-computer interface games based on consumer-grade electroencephalography devices: systematic review and controlled experiments." PROGRAMA DE P?S-GRADUA??O EM SISTEMAS E COMPUTA??O, 2017. https://repositorio.ufrn.br/jspui/handle/123456789/24003.

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Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico (CNPq)
Brain-computer interfaces (BCIs) are specialized systems that allow users to control a computer or a machine using their brain waves. BCI systems allow patients with severe physical impairments, such as those suffering from amyotrophic lateral sclerosis, cerebral palsy and locked-in syndrome, to communicate and regain physical movements with the help of specialized equipment. With the development of BCI technology in the second half of the 20th century and the advent of consumer-grade BCI devices in the late 2000s, brain-controlled systems started to find applications not only in the medical field, but in areas such as entertainment. One particular area that is gaining more evidence due to the arrival of consumer-grade devices is the field of computer games, which has become increasingly popular in BCI research as it allows for more user-friendly applications of BCI technology in both healthy and unhealthy users. However, numerous challenges are yet to be overcome in order to advance in this field, as the origins and mechanics of the brain waves and how they are affected by external stimuli are not yet fully understood. In this sense, a systematic literature review of BCI games based on consumer-grade technology was performed. Based on its results, two BCI games, one using attention and the other using meditation as control signals, were developed in order to investigate key aspects of player interaction: the influence of graphical elements on attention and control; the influence of auditory stimuli on meditation and work load; and the differences both in performance and multiplayer game experience, all in the context of neurofeedback-based BCI games.
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Book chapters on the topic "NeuroSky MindWave"

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Sahu, Mridu, Praveen Shukla, Aditya Chandel, Saloni Jain, and Shrish Verma. "Eye Blinking Classification Through NeuroSky MindWave Headset Using EegID Tool." In Advances in Intelligent Systems and Computing, 789–99. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5113-0_65.

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Macruz, Andrea, Ernesto Bueno, Gustavo G. Palma, Jaime Vega, Ricardo A. Palmieri, and Tan Chen Wu. "Measuring Human Perception of Biophilically-Driven Design with Facial Micro-expressions Analysis and EEG Biosensor." In Proceedings of the 2021 DigitalFUTURES, 231–41. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-5983-6_22.

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AbstractThis paper investigates the role technology and neuroscience play in aiding the design process and making meaningful connections between people and nature. Using two workshops as a vehicle, the team introduced advanced technologies and Quantified Self practices that allowed people to use neural data and pattern recognition as feedback for the design process. The objective is to find clues to natural elements of human perception that can inform the design to meet goals for well-being. A pattern network of geometric shapes that achieve a higher level of monitored meditation levels and point toward a positive emotional valence is proposed. By referencing biological forms found in nature, the workshops utilized an algorithmic process that explored how nature can influence architecture. To measure the impact, the team used FaceOSC for capture and an Artificial Neural Network for micro-expression recognition, and a MindWave sensor manufactured by NeuroSky, which documented the human response further. The methodology allowed us to establish a boundary logic, ranking geometric shapes that suggested positive emotions and a higher level of monitored meditation levels. The results pointed us to a deeper level of understanding relative to geometric shapes in design. They indicate a new way to predict how well-being factors can clarify and rationalize a more intuitive design process inspired by nature.
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Vélez, Luis, and Guillermo Kemper. "Algorithm for Detection of Raising Eyebrows and Jaw Clenching Artifacts in EEG Signals Using Neurosky Mindwave Headset." In Proceedings of the 5th Brazilian Technology Symposium, 99–110. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57566-3_10.

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Nieva, Eduardo G., María F. Peralta, and Diego A. Beltramone. "Home Automation by Brain-Computer Interface." In Advanced Research and Trends in New Technologies, Software, Human-Computer Interaction, and Communicability, 502–10. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4490-8.ch045.

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In the present work, the authors use the Brain Computer Interface technology to allow the dependent persons the utilization of the basic elements of their house, such as turning on and turning off lamps, rolling up and down a roller shutter, or switching on the heating system. For doing this, it is necessary to automate these devices and to centralize its managing in a platform, which constitutes a domotics system. In order to achieve this, the authors have used the MindWave NeuroSky ® commercial device. It is affordable, portable, and wireless, and senses and delivers the computer the electroencephalographic signals produced in the frontal lobe and the levels of attention, relaxation, and blinking to the computer. In order to determine the efficiency of the obtained signals a test software was designed, which verified the operation´s device with different persons. The authors conclude that the easiest way to control the attention levels is concentrating on a certain point, and the way to control the relaxation levels is by closing the eyes. As a second step, the authors develop a software that takes the signal from the EEG (Electro Encephalo Graphy) sensor, processes it, and sends signals via USB to an Arduino board, which is associated with electronics that complies the different tasks. The user chooses the action by managing the attention levels. When they are higher than a particular threshold value, the action is executed. In order to disable this action, the user must lower the threshold level and overcome it again. This is the simplest and fastest way to handle, but it brings several problems: if the user concentrates for any other reason and this signal exceeds the threshold, it causes the activation of an involuntary action. To solve this problem, the authors use a three variables combination that can become independent of each other thru training properly. These variables are attention, meditation, and blink. When you comply with the three simultaneous previously established conditions, the action is executed, and when they return to fulfill the conditions, the action is deactivated. The software also has the feature of personalizing its conditions, so it can be best for any user, even a novice one.
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Rușanu, Oana Andreea, Luciana Cristea, and Marius Cristian Luculescu. "The development of a BCI prototype based on the integration between NeuroSky Mindwave Mobile EEG headset, Matlab software environment and Arduino Nano 33 IoT board for controlling the movement of an experimental motorcycle." In 11th International Conference on Information Science and Information Literacy, 290–97. Sciendo, 2020. http://dx.doi.org/10.2478/9788395815065-033.

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Conference papers on the topic "NeuroSky MindWave"

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Lancheros-Cuesta, Diana Janeth, Jose Luis Ramirez Arias, Yudi Yirley Forero, and Adriana Carolina Duran. "Evaluation of e-learning activities with NeuroSky MindWave EEG." In 2018 13th Iberian Conference on Information Systems and Technologies (CISTI). IEEE, 2018. http://dx.doi.org/10.23919/cisti.2018.8399316.

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Morshad, Sarwar, Md Rabiuzzaman Mazumder, and Fahad Ahmed. "Analysis of Brain Wave Data Using Neurosky Mindwave Mobile II." In ICCA 2020: International Conference on Computing Advancements. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3377049.3377053.

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Permana, K., S. K. Wijaya, and P. Prajitno. "Controlled wheelchair based on brain computer interface using Neurosky Mindwave Mobile 2." In PROCEEDINGS OF THE 4TH INTERNATIONAL SYMPOSIUM ON CURRENT PROGRESS IN MATHEMATICS AND SCIENCES (ISCPMS2018). AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5132449.

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Rusanu, Oana Andreea, Luciana Cristea, Marius Cristian Luculescu, and Sorin Constantin Zamfira. "Experimental Model of a Robotic Hand Controlled by Using NeuroSky Mindwave Mobile Headset." In 2019 E-Health and Bioengineering Conference (EHB). IEEE, 2019. http://dx.doi.org/10.1109/ehb47216.2019.8970050.

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Ramirez-Noriega, Alan, Yobani Martinez-Ramirez, Samantha Jimenez, Elizabeth Gaxiola Carrillo, and Jose Emilio Sanchez Garcia. "An Application Programming Interface for a Brain-Computer Interface using two NeuroSky MindWave devices." In 2020 8th International Conference in Software Engineering Research and Innovation (CONISOFT). IEEE, 2020. http://dx.doi.org/10.1109/conisoft50191.2020.00029.

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Katona, Jozsef, Tibor Ujbanyi, Gergely Sziladi, and Attila Kovari. "Speed control of Festo Robotino mobile robot using NeuroSky MindWave EEG headset based brain-computer interface." In 2016 7th IEEE International Conference on Cognitive Infocommunications (CogInfoCom). IEEE, 2016. http://dx.doi.org/10.1109/coginfocom.2016.7804557.

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Rusanu, Oana Andreea, Luciana Cristea, and Marius Cristian Luculescu. "LabVIEW and Android BCI Chat App Controlled By Voluntary Eye-Blinks Using NeuroSky Mindwave Mobile EEG Headset." In 2020 International Conference on e-Health and Bioengineering (EHB). IEEE, 2020. http://dx.doi.org/10.1109/ehb50910.2020.9280193.

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Alkaf, Habshi, Ahsan Khandoker, HF Jelinek, and Kinda Khalaf. "NeuroSky Mindwave Mobile Headset 2 as an Intervention for Reduction of Stress and Anxiety Measured with Pulse Rate Variability." In 2020 Computing in Cardiology Conference. Computing in Cardiology, 2020. http://dx.doi.org/10.22489/cinc.2020.350.

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Raja, P. Dinesh Anton, D. Akash, S. John Prem Kumar, Dudigam Sri Harsha, and C. Arunachalaperumal. "Feature extraction and classification of EEG signal based anomaly detection and home automation for physically challenged/impaired people using neurosky mindwave headset." In 1ST INTERNATIONAL CONFERENCE ON SUSTAINABLE MANUFACTURING, MATERIALS AND TECHNOLOGIES. AIP Publishing, 2020. http://dx.doi.org/10.1063/5.0000064.

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