Feature extraction of EEG for emotion recognition using Hjorth features and higher order crossings

Patil, Anita; Deshmukh, C. N.; Panat, Ashish

doi:10.1109/casp.2016.7746209

Cited by 37 publications

(12 citation statements)

References 8 publications

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“…1) Relative Power Spectral Density (RPSD) (Antons et al, 2014) 2) Frontal asymmetry (Huster et al, 2009) 3) Spectral envelope (Kraljevic et al, 2017) 4) Number of zero-crossings (Patil et al, 2016) 5) Katz fractal dimension (Akar et al, 2015) 6) Hjorth parameters (Mehmood and Lee, 2015) 7) Petrosian fractal dimension (Balan et al, 2020) Each feature was extracted at each electrode site available for each participant. Given that some electrodes were automatically removed during the preprocessing step (see bad channel removal in Section 3.1), not all electrodes were available for all participants.…”

Section: Feature Extractionmentioning

confidence: 99%

Real-time affect detection in virtual reality: a technique based on a three-dimensional model of affect and EEG signals

Pinilla

Voigt-Antons

García

et al. 2023

Front. Virtual Real.

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This manuscript explores the development of a technique for detecting the affective states of Virtual Reality (VR) users in real-time. The technique was tested with data from an experiment where 18 participants observed 16 videos with emotional content inside a VR home theater, while their electroencephalography (EEG) signals were recorded. Participants evaluated their affective response toward the videos in terms of a three-dimensional model of affect. Two variants of the technique were analyzed. The difference between both variants was the method used for feature selection. In the first variant, features extracted from the EEG signals were selected using Linear Mixed-Effects (LME) models. In the second variant, features were selected using Recursive Feature Elimination with Cross Validation (RFECV). Random forest was used in both variants to build the classification models. Accuracy, precision, recall and F1 scores were obtained by cross-validation. An ANOVA was conducted to compare the accuracy of the models built in each variant. The results indicate that the feature selection method does not have a significant effect on the accuracy of the classification models. Therefore, both variations (LME and RFECV) seem equally reliable for detecting affective states of VR users. The mean accuracy of the classification models was between 87% and 93%.

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Section: Feature Extractionmentioning

confidence: 99%

Real-time affect detection in virtual reality: a technique based on a three-dimensional model of affect and EEG signals

Pinilla

Voigt-Antons

García

et al. 2023

Front. Virtual Real.

View full text Add to dashboard Cite

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“…They consist of three typesactivity (Act), mobility (Mob), and complexity (Com). Activity describes the signal power, mobility determines average frequency, and complexity shows variation in frequency [20]. These are computed as shown in Eq.…”

Section: Methods 4: Hjorth Parametersmentioning

confidence: 99%

“…Motor imagery (MI) signals are produced when the user imagines moving specific parts of their bodies like their hands or feet. They usually lie in the amplitude range 0.5-100 µV, and frequency ranges Mu (8)(9)(10)(11)(12) and Beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). Mu and Beta rhythms of EEG signals can be used to distinguish between various MI tasks and are therefore analysed in MI based BCI systems.…”

Section: Introductionmentioning

confidence: 99%

EEG-Controlled Robot Navigation using Hjorth Parameters and Welch-PSD

Francis¹,

Keran²,

Chetan³

et al. 2021

IJIES

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This study aimed to find an optimal feature-extraction method for real-time electroencephalography-based navigation. A comparative study of promising methods was done on two-class motor-imagery data. A novel combination of Hjorth parameters and Welch's-power-spectral-density produced the highest two-class accuracy of 0.84 across five subjects. The method is computationally inexpensive and provides short training and execution times that best suit real-time applications. The method yielded an average accuracy of 0.66 on three-class data. The real-time feasibility of the method was tested by implementing an object-retrieving robot and running a navigation experiment. The robot successfully navigated a three-circuit maze using three commandsright, left, and forwardand automatically retrieved the desired object. Along with being ideal for real-time applications, this method utilized only eight electrodes, making it compatible with cheap, portable BCI devices. The method and the application-framework can find commercial utility in the field of healthcare for the elderly and physically-handicapped.

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“…This is called the HOC sequence for the set of filters. Different types of HOC sequences can be calculated by appropriate filter design, and this HOC is used to generate a feature vector [37], [121], [125], [127], [128].…”

Section: ) Higher-order Crossing (Hoc)mentioning

confidence: 99%

Emotion Recognition From EEG Signal Focusing on Deep Learning and Shallow Learning Techniques

et al. 2021

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Recently, electroencephalogram-based emotion recognition has become crucial in enabling the Human-Computer Interaction (HCI) system to become more intelligent. Due to the outstanding applications of emotion recognition, e.g., person-based decision making, mind-machine interfacing, cognitive interaction, affect detection, feeling detection, etc., emotion recognition has become successful in attracting the recent hype of AI-empowered research. Therefore, numerous studies have been conducted driven by a range of approaches, which demand a systematic review of methodologies used for this task with their feature sets and techniques. It will facilitate the beginners as guidance towards composing an effective emotion recognition system. In this article, we have conducted a rigorous review on the state-ofthe-art emotion recognition systems, published in recent literature, and summarized some of the common emotion recognition steps with relevant definitions, theories, and analyses to provide key knowledge to develop a proper framework. Moreover, studies included here were dichotomized based on two categories: i) deep learning-based, and ii) shallow machine learning-based emotion recognition systems. The reviewed systems were compared based on methods, classifier, the number of classified emotions, accuracy, and dataset used. An informative comparison, recent research trends, and some recommendations are also provided for future research directions.

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Feature extraction of EEG for emotion recognition using Hjorth features and higher order crossings

Cited by 37 publications

References 8 publications

Real-time affect detection in virtual reality: a technique based on a three-dimensional model of affect and EEG signals

Real-time affect detection in virtual reality: a technique based on a three-dimensional model of affect and EEG signals

EEG-Controlled Robot Navigation using Hjorth Parameters and Welch-PSD

Emotion Recognition From EEG Signal Focusing on Deep Learning and Shallow Learning Techniques

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