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Journal articles on the topic 'Emotiv epoc'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 April 2022

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1

Zabcikova, Martina. "Measurement of Visual and Auditory Stimuli Using EEG Headset Emotiv Epoc+." MATEC Web of Conferences 292 (2019): 01023. http://dx.doi.org/10.1051/matecconf/201929201023.

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Brain-Computer Interface (BCI) has received a huge interest as a direct communication pathway between a human brain and an external device. BCI is very useful in many areas of research. This study examines and discusses the feasibility and usability of the Emotiv Epoc+ noninvasive device. The focus is on the analysis of electroencephalography (EEG) signals associated with visual and auditory senses. To measure signals the free version of software Emotiv Xavier ControlPanel is used. The results depict that the Emotiv Epoc+ device is a suitable option in BCI for scientific and entertainment purposes.

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2

Balart-Sánchez, Sebastián A., Hugo Vélez-Pérez, Sergio Rivera-Tello, Fabiola R. Gómez Velázquez, Andrés A. González-Garrido, and Rebeca Romo-Vázquez. "A step forward in the quest for a mobile EEG-designed epoch for psychophysiological studies." Biomedical Engineering / Biomedizinische Technik 64, no. 6 (December 18, 2019): 655–67. http://dx.doi.org/10.1515/bmt-2017-0189.

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Abstract The aim of this study was to compare a reconfigurable mobile electroencephalography (EEG) system (M-EMOTIV) based on the Emotiv Epoc® (which has the ability to record up to 14 electrode sites in the 10/20 International System) and a commercial, clinical-grade EEG system (Neuronic MEDICID-05®), and then validate the rationale and accuracy of recordings obtained with the prototype proposed. In this approach, an Emotiv Epoc® was modified to enable it to record in the parieto-central area. All subjects (15 healthy individuals) performed a visual oddball task while connected to both devices to obtain electrophysiological data and behavioral responses for comparative analysis. A Pearson’s correlation analysis revealed a good between-devices correlation with respect to electrophysiological measures. The present study not only corroborates previous reports on the ability of the Emotiv Epoc® to suitably record EEG data but presents an alternative device that allows the study of a wide range of psychophysiological experiments with simultaneous behavioral and mobile EEG recordings.

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Chabin, Thibault, Damien Gabriel, Emmanuel Haffen, Thierry Moulin, and Lionel Pazart. "Are the new mobile wireless EEG headsets reliable for the evaluation of musical pleasure?" PLOS ONE 15, no. 12 (December 31, 2020): e0244820. http://dx.doi.org/10.1371/journal.pone.0244820.

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Since the beginning of the 20th century, electroencephalography (EEG) has been used in a wide variety of applications, both for medical needs and for the study of various cerebral processes. With the rapid development of the technique, more and more precise and advanced tools have emerged for research purposes. However, the main constraints of these devices have often been the high price and, for some devices the low transportability and the long set-up time. Nevertheless, a broad range of wireless EEG devices have emerged on the market without these constraints, but with a lower signal quality. The development of EEG recording on multiple participants simultaneously, and new technological solutions provides further possibilities to understand the cerebral emotional dynamics of a group. A great number of studies have compared and tested many mobile devices, but have provided contradictory results. It is therefore important to test the reliability of specific wireless devices in a specific research context before developing a large-scale study. The aim of this study was to assess the reliability of two wireless devices (g.tech Nautilus SAHARA electrodes and Emotiv™ Epoc +) for the detection of musical emotions, in comparison with a gold standard EEG device. Sixteen participants reported feeling emotional pleasure (from low pleasure up to musical chills) when listening to their favorite chill-inducing musical excerpts. In terms of emotion detection, our results show statistically significant concordance between Epoc + and the gold standard device in the left prefrontal and left temporal areas in the alpha frequency band. We validated the use of the Emotiv™ Epoc + for research into musical emotion. We did not find any significant concordance between g.tech and the gold standard. This suggests that Emotiv Epoc is more appropriate for musical emotion investigations in natural settings.

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4

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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5

Strmiska, Martin, and Zuzana Koudelkova. "Analysis of Performance Metrics Using Emotiv EPOC+." MATEC Web of Conferences 210 (2018): 04046. http://dx.doi.org/10.1051/matecconf/201821004046.

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Brain-computer interface (BCI) is a device that enables the connection between the human brain and a computer, therefore, it allows us to observe the brain activity. The goal of this article is to prove that brain-computer interface is a helpful and quite precise tool. This goal will be achieved by presenting various examples from real-life situations. The results show that this device is indeed helpful, e.g. in a medical field, however, it is not commonly used in hospitals.

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6

Радовановић, Mарина. "NIGHTMARE MAGIC – ПРИМЕР УПОТРЕБЕ ЕЛЕКТРОЕНЦЕФАЛОГРАФИЈЕ И EMOTIV EPOC УРЕЂАЈА У РАЧУНАРСКИМ ИГРАМА." Zbornik radova Fakulteta tehničkih nauka u Novom Sadu 34, no. 11 (November 7, 2019): 2108–11. http://dx.doi.org/10.24867/05be48radovanovic.

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Електроенцефалографија (EEG, Electroencephalography) препознаје мерљиву електричну активност унутар мозга која се јавља при активирању неурона. За запис ових сигнала сензори се постављају преко скалпа, а један од BCI (Brain-Computer Interface, интерфејс мозак-рачунар) уређаја који користи ову неинвазивну методу је Emotiv EPOC, који омогућава управљање апликацијама путем мисли или израза лица. Циљ овог рада је истраживање практичне примене конкретног BCI уређаја у прављењу једноставне игре. Мерењем EEG таласа и детекцијом израза лица корисника управља се бацањем магије унутар игре Nightmare Magic, пројекта који је замишљен као демо за упознавање са радом Emotiv EPOC уређаја и почетна тачка за будућа истраживања.

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7

Lau, Jorge Gudiño, Luis Cordova-Alvarez, Daniel Vélez-Díaz, Janeth Alcalá-Rodríguez, Saida Charre-Ibarra, and Dayanna Guzmán-Moya. "La Diadema EMOTIV EPOC+ y los gestos faciales." XIKUA Boletín Científico de la Escuela Superior de Tlahuelilpan 7, no. 14 (July 5, 2019): 1–10. http://dx.doi.org/10.29057/xikua.v7i14.4353.

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Este artículo describe el estado del arte donde se muestra los avances de las investigaciones de los distintos dispositivos inalámbricos de emisión de electroencefalograma (EEG). También se muestra un software prototipo que interpreta las señales cerebrales que provienen de la diadema Emotiv Epoc, este proceso es llamado Interface Cerebro Computadora (ICC) o BCI (por sus siglas en inglés Brain Computer Interface) que resuelve el problema de identificación de señales EEG. El software es diseñado en Matlab y Simulink que interpreta las señales cerebrales, estas señales se pueden guardar o manipularlas en tiempo real. El software convierte las señales cerebrales a voltaje para manipular dispositivos manipuladores externos. Actualmente este trabajo está en la fase de pruebas experimentales en seres humanos y se emplea el método de adquisición de la señal no invasivo. Algunos resultados experimentales de la diadema Emotiv Epoc. Este artículo se muestra las señales que emite la diadema de los gestos faciales tales parpadeos, apretar la mandíbula, fruncir la nariz y giñar un ojo. Se espera que este trabajo ayude a personas que no tengan movimiento de su cuerpo y no puedan hablar, a manipular objetos e interpretar a través de sus gestos faciales con la diadema.

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Williams, Nikolas S., Genevieve M. McArthur, and Nicholas A. Badcock. "It’s all about time: precision and accuracy of Emotiv event-marking for ERP research." PeerJ 9 (February 9, 2021): e10700. http://dx.doi.org/10.7717/peerj.10700.

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Background The use of consumer-grade electroencephalography (EEG) systems for research purposes has become more prevalent. In event-related potential (ERP) research, it is critical that these systems have precise and accurate timing. The aim of the current study was to investigate the timing reliability of event-marking solutions used with Emotiv commercial EEG systems. Method We conducted three experiments. In Experiment 1 we established a jitter threshold (i.e. the point at which jitter made an event-marking method unreliable). To do this, we introduced statistical noise to the temporal position of event-marks of a pre-existing ERP dataset (recorded with a research-grade system, Neuroscan SynAmps2 at 1,000 Hz using parallel-port event-marking) and calculated the level at which the waveform peaks differed statistically from the original waveform. In Experiment 2 we established a method to identify ‘true’ events (i.e. when an event should appear in the EEG data). We did this by inserting 1,000 events into Neuroscan data using a custom-built event-marking system, the ‘Airmarker’, which marks events by triggering voltage spikes in two EEG channels. We used the lag between Airmarker events and events generated by Neuroscan as a reference for comparisons in Experiment 3. In Experiment 3 we measured the precision and accuracy of three types of Emotiv event-marking by generating 1,000 events, 1 s apart. We measured precision as the variability (standard deviation in ms) of Emotiv events and accuracy as the mean difference between Emotiv events and true events. The three triggering methods we tested were: (1) Parallel-port-generated TTL triggers; (2) Arduino-generated TTL triggers; and (3) Serial-port triggers. In Methods 1 and 2 we used an auxiliary device, Emotiv Extender, to incorporate triggers into the EEG data. We tested these event-marking methods across three configurations of Emotiv EEG systems: (1) Emotiv EPOC+ sampling at 128 Hz; (2) Emotiv EPOC+ sampling at 256 Hz; and (3) Emotiv EPOC Flex sampling at 128 Hz. Results In Experiment 1 we found that the smaller P1 and N1 peaks were attenuated at lower levels of jitter relative to the larger P2 peak (21 ms, 16 ms, and 45 ms for P1, N1, and P2, respectively). In Experiment 2, we found an average lag of 30.96 ms for Airmarker events relative to Neuroscan events. In Experiment 3, we found some lag in all configurations. However, all configurations exhibited precision of less than a single sample, with serial-port-marking the most precise when paired with EPOC+ sampling at 256 Hz. Conclusion All Emotiv event-marking methods and configurations that we tested were precise enough for ERP research as the precision of each method would provide ERP waveforms statistically equivalent to a research-standard system. Though all systems exhibited some level of inaccuracy, researchers could easily account for these during data processing.

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Melek, Mesut, Negin Manshouri, and Temel Kayikcioglu. "Low-Cost Brain-Computer Interface Using the Emotiv Epoc Headset Based on Rotating Vanes." Traitement du Signal 37, no. 5 (November 25, 2020): 831–37. http://dx.doi.org/10.18280/ts.370516.

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Detailed In the brain-computer interface system (BCI), electroencephalography (EEG) signals are converted into digital signals and analyzed, allowing direct communication between humans and the electronic devices around them. The convenience of the user and the speed of communication with the surrounding devices are the most important challenges of BCI systems. The Emotiv Epoc headset minimizes the discomfort of the user thanks to its wet electrodes and easy handling. In the continuation of our previous works, in this paper, we developed our BCI system based on the gaze at the rotating vanes using the inexpensive Emotiv Epoc headset. In addition to user comfort, our design has an acceptable mean accuracy rate (ACC) and mean information transfer rate (ITR) compared to similar systems.

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10

Salgado Patrón, José, and Cristian Raúl Barrera Monje. "Emotiv EPOC BCI with Python on a Raspberry pi." Sistemas y Telemática 14, no. 36 (March 30, 2016): 27–38. http://dx.doi.org/10.18046/syt.v14i36.2217.

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The hybrid Brain-Computer Interface [BCI] system gives an insight on the development of useful interfaces for users with different backgrounds, from medical applications to video games, where standalone and wearable means accessibility for the user. Systems such as EPOC offers a simple solution for acquiring electroencephalography and electromyography signals with low price and fast setup, compared to high tech medical equipment. From the processing point of view, a computer always offers the main foundation for solving any issue, as the Raspberry Pi [RPi] does, which provides the sufficient computational power for a BCI to be implemented and an open source operating system such as Raspbian. Certainly a wireless communication is a must between the robot and the RPi, where an Xbee module gives a simple bidirectional connection. Python is the principal tool used in the project with multiple libraries for the processing of brain and muscular signals not only for the preparation of them but classification as well, from multithreading functions, feature extraction such as power spectral density and Hjorth parameters, and a support vector machine classifiera.

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11

Dewangga, Sandy Akbar, Handayani Tjandrasa, and Darlis Herumurti. "Robot Motion Control Using the Emotiv EPOC EEG System." Bulletin of Electrical Engineering and Informatics 7, no. 2 (June 1, 2018): 279–85. http://dx.doi.org/10.11591/eei.v7i2.678.

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Brain-computer interfaces have been explored for years with the intent of using human thoughts to control mechanical system. By capturing the transmission of signals directly from the human brain or electroencephalogram (EEG), human thoughts can be made as motion commands to the robot. This paper presents a prototype for an electroencephalogram (EEG) based brain-actuated robot control system using mental commands. In this study, Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) method were combined to establish the best model. Dataset containing features of EEG signals were obtained from the subject non-invasively using Emotiv EPOC headset. The best model was then used by Brain-Computer Interface (BCI) to classify the EEG signals into robot motion commands to control the robot directly. The result of the classification gave the average accuracy of 69.06%.

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Ancău, Dorina, Nicolae-Marius Roman, and Mircea Ancău. "The Emotiv EPOC interface paradigm in Human-Computer Interaction." MATEC Web of Conferences 137 (2017): 04001. http://dx.doi.org/10.1051/matecconf/201713704001.

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13

Yu, Je-Hun, and Kwee-Bo Sim. "Robot Control based on Steady-State Visual Evoked Potential using Arduino and Emotiv Epoc." Journal of Korean Institute of Intelligent Systems 25, no. 3 (June 25, 2015): 254–59. http://dx.doi.org/10.5391/jkiis.2015.25.3.254.

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Tao, Wang, Wu Linyan, Li Yanping, Gao Nuo, and Zhang Weiran. "Learning Advanced Brain Computer Interface Technology." International Journal of Technology and Human Interaction 15, no. 3 (July 2019): 14–27. http://dx.doi.org/10.4018/ijthi.2019070102.

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Feature extraction is an important step in electroencephalogram (EEG) processing of motor imagery, and the feature extraction of EEG directly affects the final classification results. Through the analysis of various feature extraction methods, this article finally selects Common Spatial Patterns (CSP) and wavelet packet analysis (WPA) to extract the feature and uses Support Vector Machine (SVM) to classify and compare these extracted features. For the EEG data provided by GRAZ University, the accuracy rate of feature extraction using CSP algorithm is 85.5%, and the accuracy rate of feature extraction using wavelet packet analysis is 92%. Then this paper analyzes the EEG data collected by Emotiv epoc+ system. The classification accuracy of wavelet packet extracted features can still be maintained at more than 80%, while the classification accuracy of CSP extracted feature is decreased obviously. Experimental results show that the method of wavelet packet analysis towards competition data and Emotiv epoc+ system data can both get a desirable outcome.

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Sarno, Riyanarto, Brilian T. Nugraha, and M. Nadzeri Munawar. "Real Time Fatigue-Driver Detection from Electroencephalography Using Emotiv EPOC+." International Review on Computers and Software (IRECOS) 11, no. 3 (March 31, 2016): 214. http://dx.doi.org/10.15866/irecos.v11i3.8562.

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Duvinage, Matthieu, Thierry Castermans, Mathieu Petieau, Thomas Hoellinger, Guy Cheron, and Thierry Dutoit. "Performance of the Emotiv Epoc headset for P300-based applications." BioMedical Engineering OnLine 12, no. 1 (2013): 56. http://dx.doi.org/10.1186/1475-925x-12-56.

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Feroz, Farah Shahnaz, Ahmad Rifhan Salman, Muhammad Hairulnizam Mat Ali, Afiq Idzudden Ismail, S. Indra Devi, and S. K. Subramaniam. "Attentional bias during public speaking anxiety revealed using event-related potentials." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 1 (October 1, 2021): 253. http://dx.doi.org/10.11591/ijeecs.v24.i1.pp253-259.

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<p>Analysis of brain signals and their properties provides valuable information regarding the underlying neural deficiencies and enables the diagnosis of attention bias related to public speaking anxiety (PSA). Although 25% people around the world suffer from PSA, currently, there exists a lack of standard assessment in diagnosing the severity of attention bias in individuals with PSA. This study aims to distinguish behavioral and neural abnormalities related to attentional bias during PSA by comparing reaction time (RT) and event-related potential (ERP) correlates of high (H) PSA and low (L) PSA individuals. 12 individuals suffering from HPSA and 12 individuals with LPSA participated in the modified emotional Stroop experiment. Electroencephalography (EEG) was recorded with the low cost, 14-channel Emotiv Epoc+. RT showed slower responses, linked to attentional deficits in HPSA individuals. ERP results revealed the P200 emotional Stroop biomarker, found to be linked to attentional bias in HPSA, but not in LPSA individuals. These results revealed significant RT and P200 ERP abnormalities related to attentional bias in HPSA individuals using the low-cost Emotiv Epoc+.</p>

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Williams, Nikolas S., Genevieve M. McArthur, Bianca de Wit, George Ibrahim, and Nicholas A. Badcock. "A validation of Emotiv EPOC Flex saline for EEG and ERP research." PeerJ 8 (August 11, 2020): e9713. http://dx.doi.org/10.7717/peerj.9713.

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Background Previous work has validated consumer-grade electroencephalography (EEG) systems for use in research. Systems in this class are cost-effective and easy to set up and can facilitate neuroscience outside of the laboratory. The aim of the current study was to determine if a new consumer-grade system, the Emotiv EPOC Saline Flex, was capable of capturing research-quality data. Method The Emotiv system was used simultaneously with a research-grade EEG system, Neuroscan Synamps2, to collect EEG data across 16 channels during five well-established paradigms: (1) a mismatch negativity (MMN) paradigm that involved a passive listening task in which rare deviant (1,500 Hz) tones were interspersed amongst frequent standard tones (1,000 Hz), with instructions to ignore the tones while watching a silent movie; (2) a P300 paradigm that involved an active listening task in which participants were asked to count rare deviant tones presented amongst frequent standard tones; (3) an N170 paradigm in which participants were shown images of faces and watches and asked to indicate whether the images were upright or inverted; (4) a steady-state visual evoked potential (SSVEP) paradigm in which participants passively viewed a flickering screen (15 Hz) for 2 min; and (5) a resting state paradigm in which participants sat quietly with their eyes open and then closed for 3 min each. Results The MMN components and P300 peaks were equivalent between the two systems (BF10 = 0.25 and BF10 = 0.26, respectively), with high intraclass correlations (ICCs) between the ERP waveforms (>0.81). Although the N170 peak values recorded by the two systems were different (BF10 = 35.88), ICCs demonstrated that the N170 ERP waveforms were strongly correlated over the right hemisphere (P8; 0.87–0.97), and moderately-to-strongly correlated over the left hemisphere (P7; 0.52–0.84). For the SSVEP, the signal-to-noise ratio (SNR) was larger for Neuroscan than Emotiv EPOC Flex (19.94 vs. 8.98, BF10 = 51,764), but SNR z-scores indicated a significant brain response at the stimulus frequency for both Neuroscan (z = 12.47) and Flex (z = 11.22). In the resting state task, both systems measured similar alpha power (BF10 = 0.28) and higher alpha power when the eyes were closed than open (BF10 = 32.27). Conclusions The saline version of the Emotiv EPOC Flex captures data similar to that of a research-grade EEG system. It can be used to measure reliable auditory and visual research-quality ERPs. In addition, it can index SSVEP signatures and is sensitive to changes in alpha oscillations.

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Moreno, Luz Ángela, César Augusto Peña, and Oscar Eduardo Gualdron. "DESARROLLO DE UN SISTEMA DE NEURO-MERCADOTECNIA USANDO EL DISPOSITIVO EMOTIV-EPOC." Redes de Ingeniería 5, no. 2 (November 7, 2014): 6. http://dx.doi.org/10.14483/2248762x.8042.

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Este artículo presenta los avances concebidos en la realización de un sistema de neuro-mercadotecnia. El objetivo del sistema es permitir evaluar las emociones que presentan los televidentes al observar comerciales publicitarios. Se hace uso de la interfaz cerebro-computador Emotiv-EPOC para la adquisición de las neuro-señales. Se describen los algoritmos empleados para el análisis de las pautas publicitarias. Se presentan unas pruebas experimentales donde se comparan los resultados obtenidos por métodos tradicionales con las propuestas.

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Faruk, Ahmad Muhammad, A. Bakry Hussein, Marwan A. Rashed, Sohair F. Rezeka, and Mohamed El-Habrouk. "Solar-Rechargeable Brain-Controlled Wheel-Chair for Paralytic Patients Using Emotiv Epoc+." International Journal of Robotics and Mechatronics 5, no. 1 (May 1, 2018): 20–33. http://dx.doi.org/10.21535/ijrm.v5i1.984.

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Fakhruzzaman, Muhammad N., Edwin Riksakomara, and Hatma Suryotrisongko. "EEG Wave Identification in Human Brain with Emotiv EPOC for Motor Imagery." Procedia Computer Science 72 (2015): 269–76. http://dx.doi.org/10.1016/j.procs.2015.12.140.

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Kotowski, Krzysztof, Katarzyna Stapor, Jacek Leski, and Marian Kotas. "Validation of Emotiv EPOC+ for extracting ERP correlates of emotional face processing." Biocybernetics and Biomedical Engineering 38, no. 4 (2018): 773–81. http://dx.doi.org/10.1016/j.bbe.2018.06.006.

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Asanza, Víctor, Karla Avilés-Mendoza, Hector Trivino-Gonzalez, Félix Rosales-Uribe, Jamil Torres-Brunes, Francis R. Loayza, Enrique Peláez, Ricardo Cajo, and Raquel Tinoco-Egas. "SSVEP-EEG Signal Classification based on Emotiv EPOC BCI and Raspberry Pi." IFAC-PapersOnLine 54, no. 15 (2021): 388–93. http://dx.doi.org/10.1016/j.ifacol.2021.10.287.

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Koudelková, Zuzana, and Martin Strmiska. "Introduction to the identification of brain waves based on their frequency." MATEC Web of Conferences 210 (2018): 05012. http://dx.doi.org/10.1051/matecconf/201821005012.

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A Brain Computer Interface (BCI) enables to get electrical signals from the brain. In this paper, the research type of BCI was non-invasive, which capture the brain signals using electroencephalogram (EEG). EEG senses the signals from the surface of the head, where one of the important criteria is the brain wave frequency. This paper provides the measurement of EEG using the Emotiv EPOC headset and applications developed by Emotiv System. Two types of the measurements were taken to describe brain waves by their frequency. The first type of the measurements was based on logical and analytical reasoning, which was captured during solving mathematical exercise. The second type was based on relax mind during listening three types of relaxing music. The results of the measurements were displayed as a visualization of a brain activity.

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Díaz, Andrés, Claudio Castillo, Fabián Sáenz, and Carlos Gabriel Romero. "Sistema embebido de un audífono inteligente para personas con discapacidad auditiva a través de sensores neurológicos." MASKAY 5, no. 1 (December 1, 2015): 36. http://dx.doi.org/10.24133/maskay.v5i1.124.

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El presente artículo describe el diseño e implementación de un sistema embebido para personas con discapacidad auditiva leve mediante el uso de un casco sensorial EPOC EMOTIV para el control de un arreglo de micrófonos, mejorando la calidad auditiva y comprensión al estar presente a varias fuente de sonido o personas. El principal objetivo es determinar un dispositivo que mejore la calidad de vida de personas con niveles bajos de sordera haciendo uso de tecnologías de última generación.

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Zając, Błażej, and Szczepan Paszkiel. "USING BRAIN-COMPUTER INTERFACE TECHNOLOGY AS A CONTROLLER IN VIDEO GAMES." Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska 10, no. 3 (September 30, 2020): 26–31. http://dx.doi.org/10.35784/iapgos.1543.

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Nowadays, control in video games is based on the use of a mouse, keyboard and other controllers. A Brain Computer Interface (BCI) is a special interface that allows direct communication between the brain and the appropriate external device. Brain Computer Interface technology can be used for commercial purposes, for example as a replacement for a keyboard, mouse or other controller. This article presents a method of controlling video games using the EMOTIV EPOC + Neuro Headset as a controller.

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Matsuzawa, Kaira, Ryoichi KONISHI, and Chiharu ISHII. "2A1-I01 Control of an electric wheelchair with a biosignal acquisition headset Emotiv EPOC." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2015 (2015): _2A1—I01_1—_2A1—I01_4. http://dx.doi.org/10.1299/jsmermd.2015._2a1-i01_1.

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Sánchez-Reolid, Roberto, Arturo García, Miguel Vicente-Querol, Luz Fernández-Aguilar, María López, and Antonio González. "Artificial Neural Networks to Assess Emotional States from Brain-Computer Interface." Electronics 7, no. 12 (December 3, 2018): 384. http://dx.doi.org/10.3390/electronics7120384.

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Estimation of human emotions plays an important role in the development of modern brain-computer interface devices like the Emotiv EPOC+ headset. In this paper, we present an experiment to assess the classification accuracy of the emotional states provided by the headset’s application programming interface (API). In this experiment, several sets of images selected from the International Affective Picture System (IAPS) dataset are shown to sixteen participants wearing the headset. Firstly, the participants’ responses in form of a self-assessment manikin questionnaire to the emotions elicited are compared with the validated IAPS predefined valence, arousal and dominance values. After statistically demonstrating that the responses are highly correlated with the IAPS values, several artificial neural networks (ANNs) based on the multilayer perceptron architecture are tested to calculate the classification accuracy of the Emotiv EPOC+ API emotional outcomes. The best result is obtained for an ANN configuration with three hidden layers, and 30, 8 and 3 neurons for layers 1, 2 and 3, respectively. This configuration offers 85% classification accuracy, which means that the emotional estimation provided by the headset can be used with high confidence in real-time applications that are based on users’ emotional states. Thus the emotional states given by the headset’s API may be used with no further processing of the electroencephalogram signals acquired from the scalp, which would add a level of difficulty.

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Akimoto, Yoritaka. "Reliability of Event-Related (De) synchronization during face processing measured by Emotiv EPOC+." Proceedings of the Annual Convention of the Japanese Psychological Association 82 (September 25, 2018): 1AM—062–1AM—062. http://dx.doi.org/10.4992/pacjpa.82.0_1am-062.

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SÖZER, Abdullah Talha, and Can Bülent FİDAN. "Emotiv Epoc ile Durağan Hal Görsel Uyarılmış Potansiyel Temelli Beyin Bilgisayar Arayüzü Uygulaması." Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 8, no. 1 (March 12, 2019): 158–66. http://dx.doi.org/10.17798/bitlisfen.445574.

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Yu, Je-Hun, and Kwee-Bo Sim. "Classification of color imagination using Emotiv EPOC and event-related potential in electroencephalogram." Optik 127, no. 20 (October 2016): 9711–18. http://dx.doi.org/10.1016/j.ijleo.2016.07.074.

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Fouad, Islam A. "A robust and reliable online P300-based BCI system using Emotiv EPOC + headset." Journal of Medical Engineering & Technology 45, no. 2 (January 18, 2021): 94–114. http://dx.doi.org/10.1080/03091902.2020.1853840.

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Ferrin Bolaños, Carlos, Humberto Loaiza-Correa, Jean Pierre-Díaz, and Paulo Vélez-Ángel. "Evaluación del aporte de la covarianza de las señales electroencefalográficas a las interfaces cerebro-computador de imaginación motora para pacientes con lesiones de médula espinal." TecnoLógicas 22, no. 46 (September 20, 2019): 213–31. http://dx.doi.org/10.22430/22565337.1392.

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Las interfaces cerebro-computadora no invasivas basadas en EEG de imaginación motora (miBCI) prometen restaurar efectivamente el control motor a pacientes con discapacidades motoras, por ejemplo, aquellos con lesión de la médula espinal (LME). Sin embargo, todavía es necesario investigar las miBCI, con fines de rehabilitación, para este tipo de pacientes que utilizan dispositivos de adquisición de señales EEG de bajo costo, tales como Emotiv EPOC. En este trabajo, se describe en detalle y se comparan diez arquitecturas miBCI basadas en información de covarianza de señales EEG, adquiridas con Emotiv EPOC, para la decodificación de intención de mano abierta y cerrada en tres sujetos control y dos pacientes con LME cervical. Cuatro de estas diez miBCI usan información de covarianza para construir filtros espaciales y el resto usa la información covarianza como una representación directa de las señales EEG, permitiendo la manipulación directa mediante geometría de Riemann. Como resultado, se encontró que, a pesar de que todas las arquitecturas miBCI tienen una precisión general por encima del nivel de azar, las que utilizan la covarianza como representación directa de las señales EEG junto con clasificadores lineales, superan las miBCI que usan la covarianza para el filtrado espacial, tanto en sujetos de control como en pacientes con LME. Estos resultados sugieren un alto potencial de las miBCI basadas en la geometría de Riemann para la rehabilitación de pacientes con LME, utilizando dispositivos de adquisición de EEG de bajo costo.

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Shahin, Mohamed K., Alaa Tharwat, Tarek Gaber, and Aboul Ella Hassanien. "A Wheelchair Control System Using Human-Machine Interaction: Single-Modal and Multimodal Approaches." Journal of Intelligent Systems 28, no. 1 (January 28, 2019): 115–32. http://dx.doi.org/10.1515/jisys-2017-0085.

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Abstract Recent research studies showed that brain-controlled systems/devices are breakthrough technology. Such devices can provide disabled people with the power to control the movement of the wheelchair using different signals (e.g. EEG signals, head movements, and facial expressions). With this technology, disabled people can remotely steer a wheelchair, a computer, or a tablet. This paper introduces a simple, low-cost human-machine interface system to help chaired people to control their wheelchair using several control sources. To achieve this paper’s aim, a laptop was installed on a wheelchair in front of the sitting person, and the 14-electrode Emotiv EPOC headset was used to collect the person’s head impressions from the skull surface. The superficially picked-up signals, containing the brain thoughts, head gestures, and facial emotions, were electrically encoded and then wirelessly sent to a personal computer to be interpreted and then translated into useful control instructions. Using these signals, two wheelchair control modes were proposed: automatic (using single-modal and multimodal approaches) and manual control. The automatic mode controller was accomplished using a software controller (Arduino), whereas a simple hardware controller was used for the manual mode. The proposed solution was designed using wheelchair, Emotiv EPOC EEG headset, Arduino microcontroller, and Processing language. It was then tested by totally chaired volunteers under different levels of trajectories. The results showed that the person’s thoughts can be used to seamlessly control his/her wheelchair and the proposed system can be configured to suit many levels and degrees of disability.

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Kissné Zsámboki, Réka, and Viktória Farnady-Landerl. "Neuropedagógiai innovációs lehetőségek a neveléstudományi kutatások-ban az EMOTIV EPOC+ mobil EEG készülék alkalmazásával." Képzés és gyakorlat 16, no. 3 (2018): 21–36. http://dx.doi.org/10.17165/tp.2018.3.3.

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Taylor, Grant S., and Christina Schmidt. "Empirical Evaluation of the Emotiv EPOC BCI Headset for the Detection of Mental Actions." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 56, no. 1 (September 2012): 193–97. http://dx.doi.org/10.1177/1071181312561017.

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Leape, Charlotte, Allan Fong, and Raj M. Ratwani. "Heuristic Usability Evaluation of Wearable Mental State Monitoring Sensors for Healthcare Environments." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 60, no. 1 (September 2016): 583–87. http://dx.doi.org/10.1177/1541931213601134.

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In recent years, there has been tremendous growth in both the number and diversity of wearable sensors, including sensors for monitoring mental state. Understanding physiological metrics such as fatigue and stress is an important aspect of human factors research, yet collecting and analyzing these measures can be resource intensive. This paper explores the usability and applicability of four off-the-shelf wearable sensors (Emotiv Epoc, Melon Headband, Spire Stone, and Muse™ Headband) for applied healthcare research. We perform a heuristic usability evaluation of the four sensors and discuss the extent to which each device can be applied to human factors healthcare research in clinical settings.

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Bianconi, F., M. Filippucci, M. Seccaroni, and C. M. Aquinardi. "URBAN PARAMETRIC PERCEPTION. THE CASE STUDY OF THE HISTORIC CENTRE OF PERUGIA." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2021 (June 28, 2021): 839–46. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2021-839-2021.

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Abstract. The study describes a new approach methodology to detect and represent emotions from signals measured by an Emotiv EPOC cascade. The developed algorithm aims to obtain an analysis of emotions based on EEG data with interpolation of these using the concepts of the circumplex model. This tool will help in the analysis of emotions produced by the atmosphere of an environment in relation to the position, detected by GPS. In addition, the same algorithm will be used to search for a graphic representation of immediate reading, by means of colour association with the output values obtained, to integrate those of gaze analysis obtained by Pupil Player software and position data.

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Davies, Simon R. H., Urmila Mistry, Laura Millen, Lee Skrypchuk, and Jim Braithwaite. "Evaluation of an EEG/Electro-dermal Hybrid Device to Ascertain a User’s Attentional State." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 60, no. 1 (September 2016): 26–30. http://dx.doi.org/10.1177/1541931213601006.

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The purpose of this study was to evaluate an alleged EEG amplifier’s, the Freer Logic BodyWave, ability to record EEG by comparing its signals to those recorded at the same time by a proven EEG amplifier, the Emotiv EPOC+. The BodyWave’s circuitry is similar to that of an Electro-dermal Activity meter and alleges to record surface-EEG from a limb of the user. The EPOC+ has been proven in previous studies to record signals of high-enough quality to be used in research, whilst the BodyWave is unproven. In order to compare their recordings, both EEGs were worn simultaneously whilst different stimuli induced various brain states. 15 participants were recruited and each subjected to two different experimental paradigms meant to induce increased attention and increased relaxation. Both headsets achieved a classification accuracy well above chance when differentiating between the attention and relaxation tasks. Further testing provided evidence that data from the alpha and beta frequency bands was contributing the most to the classifier’s accuracy. However the total lack of correlation between the signals of the two devices strongly implies that the BodyWave is not measuring EEG. Despite this these results show that in some cases it can be used in place of an EEG to perform a similar function. It also proved to be much more robust against muscle artefacts compared to the EPOC+.

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Victorio Yasin, Timothius, Felix Pasila, and Resmana Lim. "A Study of Mobile Robot Control using EEG Emotiv Epoch Sensor." MATEC Web of Conferences 164 (2018): 01044. http://dx.doi.org/10.1051/matecconf/201816401044.

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The study was using an EEG Emotiv Epoc+ sensor to recognize brain activity for controlling a mobile robot's movement. The study used Emotiv Control Panel software for EEG command identification. The commands will be interfaced inside Mind Your OSCs software and processing software which processed inside an Arduino Controller. The output of the Arduino is a movement command (ie. forward, backward, turn left, and turn right). The training methods of the system composed of three sets of thinking mode. First, thinking with doing facial expressions. Second, thinking with visual help. Third, thinking mentally without any help. In the first set, there are two configurations thinking with facial expression help as command of the mobile robot. Final results of the system are the second facial expressions configuration as the best facial expressions method with success rate 88.33 %. The second facial expression configuration has overall response time 1.60175 s faster than the first facial expressions configuration. In these two methods have dominant signals on the frontal lobe. The second facial expressions method have overall respond time 6.12 and 9.53 s faster than thinking with visual, and thinking without help respectively.

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Badcock, Nicholas A., Petroula Mousikou, Yatin Mahajan, Peter de Lissa, Johnson Thie, and Genevieve McArthur. "Validation of the Emotiv EPOC®EEG gaming system for measuring research quality auditory ERPs." PeerJ 1 (February 19, 2013): e38. http://dx.doi.org/10.7717/peerj.38.

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Galis, Meiran, Milan Milosavljević, Aleksandar Jevremović, Zoran Banjac, Aleksej Makarov, and Jelica Radomirović. "Secret-Key Agreement by Asynchronous EEG over Authenticated Public Channels." Entropy 23, no. 10 (October 11, 2021): 1327. http://dx.doi.org/10.3390/e23101327.

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In this paper, we propose a new system for a sequential secret key agreement based on 6 performance metrics derived from asynchronously recorded EEG signals using an EMOTIV EPOC+ wireless EEG headset. Based on an extensive experiment in which 76 participants were engaged in one chosen mental task, the system was optimized and rigorously evaluated. The system was shown to reach a key agreement rate of 100%, a key extraction rate of 9%, with a leakage rate of 0.0003, and a mean block entropy per key bit of 0.9994. All generated keys passed the NIST randomness test. The system performance was almost independent of the EEG signals available to the eavesdropper who had full access to the public channel.

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Panayotova, Galina S., and Dimitar A. Dimitrov. "Modeling from Time Series of Complex Brain Signals." International Journal of Signal Processing Systems 9, no. 1 (March 2021): 1–6. http://dx.doi.org/10.18178/ijsps.9.1.1-6.

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Signals obtained from most of real-world systems, especially from living organisms, are irregular, often chaotic, non-stationary, and noise-corrupted. Since modern measuring devices usually realize digital processing of information, recordings of the signals take the form of a discrete sequence of samples (a time series). In the paper given a brief overview of the possibilities of such experimental data processing based on reconstruction and usage of a predictive empirical model of a time series. Brain signals can be recorded by brainwave controlled applications, such as EMotiv Epoc +14. The paper investigates the models of the observed brain signals using time series, analyzes their applicability and develops new statistical models for their study.

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Pawuś, Dawid, and Szczepan Paszkiel. "The Application of Integration of EEG Signals for Authorial Classification Algorithms in Implementation for a Mobile Robot Control Using Movement Imagery—Pilot Study." Applied Sciences 12, no. 4 (February 18, 2022): 2161. http://dx.doi.org/10.3390/app12042161.

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This paper presents a new approach to the issue of recognition and classification of electroencephalographic signals (EEG). A small number of investigations using the Emotiv Epoc Flex sensor set was the reason for searching for original solutions including control of elements of robotics with mental orders given by a user. The signal, measured and archived with a 32-electrode device, was prepared for classification using a new solution consisting of EEG signal integration. The new waveforms modified in this way could be subjected to recognition both by a classic authorial software and an artificial neural network. The properly classified signals made it possible to use them as the signals controlling the LEGO EV3 Mindstorms robot.

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Badcock, Nicholas A., Kathryn A. Preece, Bianca de Wit, Katharine Glenn, Nora Fieder, Johnson Thie, and Genevieve McArthur. "Validation of the Emotiv EPOC EEG system for research quality auditory event-related potentials in children." PeerJ 3 (April 21, 2015): e907. http://dx.doi.org/10.7717/peerj.907.

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Lievesley, Robert, Martin Wozencroft, and David Ewins. "The Emotiv EPOC neuroheadset: an inexpensive method of controlling assistive technologies using facial expressions and thoughts?" Journal of Assistive Technologies 5, no. 2 (June 17, 2011): 67–82. http://dx.doi.org/10.1108/17549451111149278.

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Stach, Tomasz, Natalia Browarska, and Aleksandra Kawala-Janik. "Initial Study on Using Emotiv EPOC+ Neuroheadset as a Control Device for Picture Script-Based Communicators." IFAC-PapersOnLine 51, no. 6 (2018): 180–84. http://dx.doi.org/10.1016/j.ifacol.2018.07.150.

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Su, Xiaomeng, Fengshengwang, and Pingtan. "E-book Retrieval and Reading System Based on Brain-Computer Interface." Journal of Physics: Conference Series 2171, no. 1 (January 1, 2022): 012009. http://dx.doi.org/10.1088/1742-6596/2171/1/012009.

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Abstract In this paper, we propose a real-time Brain-computer interface(BCI) system that ena bles paralyzed patients to read e-books on the website autonomously according to their intentio ns. The system uses EMOTIV EPOC+ to acquire the user’s electroencephalography(EEG) sign als and decodes them to analyze the user’s real intention when reading e-books on the website, i ncluding selecting and reading operations. The system uses the VRPN communication protocol to transmit the EEG data processing results to the PC to realize the corresponding operations o n the web page. To verify its performance, we have carried out experiment on 5 subjects. And t he result shows that after short-term training, the accuracy of the system can reach more than 9 0%.

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Martišius, Ignas, and Robertas Damaševičius. "A Prototype SSVEP Based Real Time BCI Gaming System." Computational Intelligence and Neuroscience 2016 (2016): 1–15. http://dx.doi.org/10.1155/2016/3861425.

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Although brain-computer interface technology is mainly designed with disabled people in mind, it can also be beneficial to healthy subjects, for example, in gaming or virtual reality systems. In this paper we discuss the typical architecture, paradigms, requirements, and limitations of electroencephalogram-based gaming systems. We have developed a prototype three-class brain-computer interface system, based on the steady state visually evoked potentials paradigm and the Emotiv EPOC headset. An online target shooting game, implemented in the OpenViBE environment, has been used for user feedback. The system utilizes wave atom transform for feature extraction, achieving an average accuracy of 78.2% using linear discriminant analysis classifier, 79.3% using support vector machine classifier with a linear kernel, and 80.5% using a support vector machine classifier with a radial basis function kernel.

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Rosenbaum, Mark Scott, and German Contreras Ramirez. "A neuroscientific perspective of a mixed-use lifestyle center." International Journal of Contemporary Hospitality Management 32, no. 4 (October 10, 2019): 1487–502. http://dx.doi.org/10.1108/ijchm-03-2019-0277.

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Purpose This paper aims to explore consumers’ cognitive responses to the presence of other people in a planned lifestyle center. The featured lifestyle center contains shopping, lodging, dining and retail options in an open-air setting full of natural elements. This work helps explain the affinity of consumers to lifestyle centers and shows marketing researchers and practitioners how to use neuroscience hardware and software in service design research. Design/methodology/approach The study draws on social impact theory to show how the social presence of others in a lifestyle center influences six different cognitive responses. The authors evaluate consumers’ cognitive responses by using the Emotiv EPOC+ headset to obtain electroencephalogram recordings. To interpret these recordings, they use EmotivPro software, which provides readings on six emotional states, including excitement, interest, stress, engagement, attention and relaxation. Findings The data obtained from mall shoppers reveal that the presence of other people in a lifestyle center evokes high levels of interest and excitement and encourages relaxation. Research limitations/implications The paper shows marketers how to use neural data to obtain insights into consumers’ cognitive responses to stimuli by using Emotiv headsets and software. Practical implications The results show the importance of social elements in encouraging customers to approach and spend time in lifestyle centers. Originality/value The paper is one of the first to explore consumers’ responses to strangers in shared settings using neuroscience.

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