Spectral Analysis of physiological parameters for emotion detection

Valderrama, Camilo E.; Ulloa, Gonzalo

doi:10.1109/stsiva.2012.6340595

Cited by 9 publications

(2 citation statements)

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“…Brain activity reflects the magnetic and electrical activity experienced by neurons during emotional perception and regulation (Gunes and Pantic, 2010 ; Valderrama and Ulloa, 2012 ). However, research has demonstrated that this electrical activation is not uniform throughout the brain, and each hemisphere is associated with different behavior.…”

Section: Introductionmentioning

confidence: 99%

Identifying relevant asymmetry features of EEG for emotion processing

Mouri,

Valderrama,

Camorlinga

2023

Front. Psychol.

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The left and right hemispheres of the brain process emotion differently. Neuroscientists have proposed two models to explain this difference. The first model states that the right hemisphere is dominant over the left to process all emotions. In contrast, the second model states that the left hemisphere processes positive emotions, whereas the right hemisphere processes negative emotions. Previous studies have used these asymmetry models to enhance the classification of emotions in machine learning models. However, little research has been conducted to explore how machine learning models can help identify associations between hemisphere asymmetries and emotion processing. To address this gap, we conducted two experiments using a subject-independent approach to explore how the asymmetry of the brain hemispheres is involved in processing happiness, sadness, fear, and neutral emotions. We analyzed electroencephalogram (EEG) signals from 15 subjects collected while they watched video clips evoking these four emotions. We derived asymmetry features from the recorded EEG signals by calculating the log ratio between the relative energy of symmetrical left and right nodes. Using the asymmetry features, we trained four binary logistic regressions, one for each emotion, to identify which features were more relevant to the predictions. The average AUC-ROC across the 15 subjects was 56.2, 54.6, 51.6, and 58.4% for neutral, sad, fear, and happy, respectively. We validated these results with an independent dataset, achieving comparable AUC-ROC values. Our results showed that brain lateralization was observed primarily in the alpha frequency bands, whereas for the other frequency bands, both hemispheres were involved in emotion processing. Furthermore, the logistic regression analysis indicated that the gamma and alpha bands were the most relevant for predicting emotional states, particularly for the lateral frontal, parietal, and temporal EEG pairs, such as FT7-FT8, T7-T8, and TP7-TP8. These findings provide valuable insights into which brain areas and frequency bands need to be considered when developing predictive models for emotion recognition.

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Section: Introductionmentioning

confidence: 99%

Identifying relevant asymmetry features of EEG for emotion processing

Mouri,

Valderrama,

Camorlinga

2023

Front. Psychol.

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“…This phase can make the test longer and can contribute to participant fatigue. On the other hand, it is important to note that EEG signals have been used in «Neuromarketing» [52] and «Clinical studies» [16] where are they used to assess cognitive states of patients. Therefore, the EEG signals have been linked to workload, concentration, emotions, etc.…”

Section: Introductionmentioning

confidence: 99%

Usability Evaluation of Brain Computer Interfaces: Analysis of State of Art

Ortega-Gijón¹,

Mezura-Godoy²

2022

Proceedings of ISP RAS

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Brain Computer Interfaces – BCI allow users to communicate with the software system through cognitive functions measurable by brain signals, identified as Electroencephalography – EEG. User tests have been the most used method for usability evaluation of BCI software applications. In user tests, the data collected comes from the opinions of users through questionnaires, these tests require a lot of time, since they include not only performing interaction task and the application of the questionnaires, but also include placing and calibrating the EEG device. All this makes the evaluation process a very heavy task for the participants of the test and can mean that the data collected is not entirely reliable. That is why we are interested in including EEG signals in the usability evaluation process of applications with BCI software applications. Therefore, we present in this paper the result of the analysis of state of art in order to identify the relevant works in the area and future lines of research.

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