Detecting Emotion from EEG Signals Using the Emotive Epoc Device

Ramírez, Rafael; Vamvakousis, Zacharias

doi:10.1007/978-3-642-35139-6_17

Cited by 165 publications

(135 citation statements)

References 7 publications

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“…They built a combined EEG system that takes as input raw EEG data and attempts to output a piano composition and performance, which expresses the estimated emotional content of the EEG data. The subject's emotion was estimated based on EEG Frontal Asymmetry where they used (3) and (4) Hayfa et al [15] and Ramirez et al [16] used frontal EEG asymmetry to specify the valence and arousal of emotions by using (5) and (6) below. A fuzzy logic classification method was implemented that was fed with the valence and arousal features.…”

Section: ) Frontal Eeg Asymmetrymentioning

confidence: 99%

“…Besides EEG applications, it has been widely used for numerous applications in engineering, science, and mathematics. In this study, each EEG signal is decomposed using PSD approach into four distinct frequency ranges: theta (4-8 Hz), alpha (8-13 Hz), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (30-40 Hz). The PSDs were computed using Python Signal Processing Toolbox (mne), and the average of power over a specific frequency range was calculated to construct a feature using the avgpower function in the toolbox.…”

Section: ) Power Spectral Density (Psd)mentioning

confidence: 99%

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Classification of Human Emotions from Electroencephalogram (EEG) Signal using Deep Neural Network

Al-Nafjan¹,

Hosny²,

Al-Wabil³

et al. 2017

ijacsa

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Abstract-Estimationof human emotions from Electroencephalogram (EEG) signals plays a vital role in developing robust Brain-Computer Interface (BCI) systems. In our research, we used Deep Neural Network (DNN) to address EEG-based emotion recognition. This was motivated by the recent advances in accuracy and efficiency from applying deep learning techniques in pattern recognition and classification applications. We adapted DNN to identify human emotions of a given EEG signal (DEAP dataset) from power spectral density (PSD) and frontal asymmetry features. The proposed approach is compared to state-of-the-art emotion detection systems on the same dataset. Results show how EEG based emotion recognition can greatly benefit from using DNNs, especially when a large amount of training data is available.

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Section: ) Frontal Eeg Asymmetrymentioning

confidence: 99%

Section: ) Power Spectral Density (Psd)mentioning

confidence: 99%

Classification of Human Emotions from Electroencephalogram (EEG) Signal using Deep Neural Network

Al-Nafjan¹,

Hosny²,

Al-Wabil³

et al. 2017

ijacsa

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“…There have been some applications that successfully utilize this technology in several fields [8][9][10][11]. In 2013, Duvinage et al [8] proposal a BCI system to discussed the performance of the Emotiv Epoc headset for P300-based applications.…”

Section: Introductionmentioning

confidence: 99%

“…In 2013, Duvinage et al [8] proposal a BCI system to discussed the performance of the Emotiv Epoc headset for P300-based applications. Ramirez and Vamvakousis [9] used Emotive Epoc device to detect emotion from EEG signals. They extracted features from the EEG signals in order to characterize states of mind in the arousal-valence 2D emotion model.…”

Section: Introductionmentioning

confidence: 99%

Implementing Remote Presence Using Quadcopter Control by a Non-Invasive BCI Device

Lin¹,

Jiang²

2015

csit

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Extracting neural signals to control a quadcopter using wireless manner is proposed in this paper for hands-free, silence and effortless human-mobile interaction with remote presence. The brain activity is recorded in real-time and discovered patterns to relate it to facial-expression states with a cheap off-the-shelf electroencephalogram (EEG) headset-Emotic Epoc device. A tablet based mobile framework with Android system is developed to convert these discovered patterns into commands to drive the quadcopter-AR Drone 2.0 through wireless interface. First, neural signals are sequentially extracted from headset and transmitted to the tablet mobile system. In the tablet mobile system, large number of feature vector of EEG can be reduced by using Principle Component Analysis (PCA) to recognize the facial expression to generate suitable commands and driving the quadcopter through wireless interface. Finally, the quadcopter can fly smoothly in accordance with the commands converted by the EEG signals. The experimental results show that the proposed system can easily control quadcopters.

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“…Further research oriented towards dynamic emotional user experience design of web-based medical services, using user brain wave recording and analysis, is recommended, as proposed previously (Ramirez & Vamvakousis, 2012).…”

mentioning

confidence: 99%

A Tool for Emotional User Experience Assessment of Web-Based Medical Services

Nikov

Gumaia

2016

CBUP

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Emotional User Experience Design (eUXD) has become increasingly important for web-based services. The primary objective of this study is to enable users to use websites that are easy to understand and operate and pleasing to use. A checklist tool for an emotional user experience (eUX) assessment that supports web-based medical services is proposed. This tool measures user moods while using medical services' websites. The tool allocates emotive design-oriented problems and thus defines relevant website design recommendations. A case study was performed illustrating the proposed tool on a website offering medical services. Five variants of this website with different design element categories were created. These variants were assessed, analyzed, and proposed for further redesign in applying the tool. The results reveal the variant and website design elements that best influenced eUX. Significant improvement in eUX is expected with implementation of design recommendations of this study. The advantages of the tool are: (1) measuring emotional responses of medical website users, (2) allocating emotive problems, (3) defining of recommendations for eUX design of web-based medical services, and (4) creation of suitable practices for eUX design of web-based medical services. UDC Classification: 617.7 DOI: http://dx

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Detecting Emotion from EEG Signals Using the Emotive Epoc Device

Cited by 165 publications

References 7 publications

Classification of Human Emotions from Electroencephalogram (EEG) Signal using Deep Neural Network

Classification of Human Emotions from Electroencephalogram (EEG) Signal using Deep Neural Network

Implementing Remote Presence Using Quadcopter Control by a Non-Invasive BCI Device

A Tool for Emotional User Experience Assessment of Web-Based Medical Services

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