Validation of Emotiv EPOC+ for extracting ERP correlates of emotional face processing

Kotowski, Krzysztof; Stąpor, Katarzyna; Łęski, Jacek M.; Kotas, Marian

doi:10.1016/j.bbe.2018.06.006

Cited by 39 publications

(33 citation statements)

References 30 publications

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“…The basic emotions processing is frequently analyzed by means of ERP correlates of facial expression perception. Here, the early posterior negativity (EPN, in the range of 240-340 ms) component is known to be emotion sensitive [35,36], which we confirmed in another study [37] (Fig. 2.3).…”

Section: Event-related Potentialssupporting

confidence: 82%

“…It should provide a sampling rate of at least 256 Hz (to record a whole effective spectrum of the brain activity), the resolution at the level of nanovolts and additional channels for electrooculography or accelerometers. Because of the high cost of these systems, there is an increasing interest in using low-cost commercial EEG systems like Emotiv EPOC+ [37].…”

Section: Sources Of the Datamentioning

confidence: 99%

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2 Machine learning approach to automatic recognition of emotions based on bioelectrical brain activity

Kotowski¹,

Fabian²,

Stąpor³

2020

Simulations in Medicine

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The feeling and the expression of emotions are the basic skills of social interaction. People with disorders such as autism, attention deficit/hyperactivity disorder, or depression may experience social marginalization because of problems with these skills. Automatic emotion recognition systems may help in diagnosis, monitoring, and rehabilitation. Specific patterns of bioelectrical brain activity in response to affective stimuli (facial expressions, music, and videos) are biomarkers of perceived emotions. The analysis of these patterns in the time domain (event-related potentials) or frequency (brain waves) is complicated because of the scale and complexity and our incomplete knowledge about the processes taking place in the brain. Nowadays machine learning methods, including deep learning, come with help along with the growing amount of available data. In this study, the state-of-the-art methods for the recognition of emotions based on electroencephalography data will be presented.

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Section: Event-related Potentialssupporting

confidence: 82%

Section: Sources Of the Datamentioning

confidence: 99%

2 Machine learning approach to automatic recognition of emotions based on bioelectrical brain activity

Kotowski¹,

Fabian²,

Stąpor³

2020

Simulations in Medicine

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“…This is functional to the analysis of the behaviours (Aspinall et al, 2015;Berka et al, 2004, n.d.;Boutani et al, n.d.;Chynal et al, 2016;Gevins et al, n.d.;Hunter et al, n.d.;Itil et al, n.d.;Mavros et al, 2012;Sharma et al, 2017;Yadava et al, 2017). In fact, the raw data collected concerning brain impulses are interpreted by algorithms related to the EEG helmets and transformed into six cognitive states: valence, arousal, stress, meditation, focus, engagement (Badcock et al, 2015(Badcock et al, , 2013Kotowski et al, 2018).…”

Section: Means and Methodsmentioning

confidence: 99%

Survey and Co-Design the Urban Landscape. Innovative Digital Path for Perception Analysis and Data-Driven Project

Bianconi

Filippucci

Seccaroni

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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The research presents the participatory processes implemented using Eye-track and EEG to analyse and "survey" the landscape, the immaterial relation that determines the quality of places. If it is always feasible to measure quantities, it is more complex to bring out data and interpretations on the values of places. This path based on the centrality of vision, it focuses on the possibility of detecting through biosensors what attracts the eye most, but also on the effect of vision of this on man. By combining these instruments and their data, it is possible to associate the observer's staring with the sensations he feels, thus explicitly explaining the concept of imageability expressed by Kevin Lynch, it is possible to individuate precisely which elements of the urban space attract the observer and what kind of status arouse in him.The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-2/W15, 2019 27th CIPA International Symposium "Documenting the past for a better future", 1-5 September 2019, Ávila, SpainThis contribution has been peer-reviewed. https://doi.org/10.5194/isprs-archives-XLII-2-W15-165-2019 | © Authors 2019. CC BY 4.0 License.The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-2/W15, 2019 27th CIPA International Symposium "Documenting the past for a better future", 1-5 September 2019, Ávila, Spain This contribution has been peer-reviewed. https://doi.org/10.5194/isprs-archives-XLII-2-W15-165-2019 | © Authors 2019. CC BY 4.0 License. Ancona, L., 1970. Dinamica della percezione. Mondadori, Milano.Anderson, D.M., 2002. Build-to-order and mass customization : the ultimate supply chain management and lean manufacturing strategy for low-cost on-demand production without forecasts or inventory.

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“…Polymer battery which offers twelve hours of work. The EEG is filtered by the EPOC+ hardware with a fifth order digital Sinc filter using bandpass of 0.16-43 Hz and a notch at 50 Hz and 60 Hz [11]. The sampling rate of the headset is 128 Hz [26].…”

Section: The Communication Between the Neuroheadset And A Computer Ismentioning

confidence: 99%

Using Brain-Computer Interface Technology as a Controller in Video Games

Zając

Paszkiel

2020

IAPGOS

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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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Validation of Emotiv EPOC+ for extracting ERP correlates of emotional face processing

Cited by 39 publications

References 30 publications

2 Machine learning approach to automatic recognition of emotions based on bioelectrical brain activity

2 Machine learning approach to automatic recognition of emotions based on bioelectrical brain activity

Survey and Co-Design the Urban Landscape. Innovative Digital Path for Perception Analysis and Data-Driven Project

Using Brain-Computer Interface Technology as a Controller in Video Games

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