Multi-lag analysis of symbolic entropies on EEG recordings for distress recognition

Distress is a critical problem in developed societies given its long-term negative effects on physical and mental health. The interest in studying this emotion has notably increased during last years, being electroencephalography (EEG) signals preferred over other physiological variables in this research field. In addition, the non-stationary nature of brain dynamics has impulsed the use of non-linear metrics, such as symbolic entropies in brain signal analysis. Thus, the influence of time-lag on brain patterns assessment has not been tested. Hence, in the present study two permutation entropies denominated Delayed Permutation Entropy and Permutation Min-Entropy have been computed for the first time at different time-lags to discern between emotional states of calmness and distress from EEG signals. Moreover, a number of curve-related features were also calculated to assess brain dynamics across different temporal intervals. Complementary information among these variables was studied through sequential forward selection and 10-fold cross-validation approaches. According to the results obtained, the multi-lag entropy analysis has been able to reveal new significant insights so far undiscovered, thus notably improving the process of distress recognition from EEG recordings.Fil: Martínez Rodrigo, Arturo. Universidad de Castilla-La Mancha; EspañaFil: García Martínez, Beatriz. Universidad de Castilla-La Mancha; EspañaFil: Zunino, Luciano José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigaciones Ópticas. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones Ópticas. Universidad Nacional de La Plata. Centro de Investigaciones Ópticas; Argentina. Universidad Nacional de La Plata. Facultad de Ingeniería; ArgentinaFil: Alcaraz, Raúl. Universidad de Castilla-La Mancha; EspañaFil: Fernández Caballero, Antonio. Biomedical Research Networking Centre in Mental Health; España. Universidad de Castilla-La Mancha; Españ

Recognition of Emotional States from EEG Signals with Nonlinear Regularity- and Predictability-Based Entropy Metrics

Recently, the recognition of emotions with electroencephalographic (EEG) signals has received increasing attention. Furthermore, the nonstationarity of brain has intensified the application of nonlinear methods. Nonetheless, metrics like quadratic sample entropy (QSE), amplitude-aware permutation entropy (AAPE) and permutation min-entropy (PME) have never been applied to discern between more than two emotions. Therefore, this study computes for the first time QSE, AAPE and PME for recognition of four groups of emotions. After preprocessing the EEG recordings, the three entropy metrics were computed. Then, a tenfold classification approach based on a sequential forward selection scheme and a support vector machine classifier was implemented. This procedure was applied in a multi-class scheme including the four groups of study simultaneously, and in a binary-class approach for discerning emotions two by two, regarding their levels of arousal and valence. For both schemes, QSE+AAPE and QSE+PME were combined. In both multi-class and binary-class schemes, the best results were obtained in frontal and parietal brain areas. Furthermore, in most of the cases channels from QSE and AAPE/PME were selected in the classification models, thus highlighting the complementarity between those different types of entropy indices and achieving global accuracy results higher than 90% in multi-class and binary-class schemes. The combination of regularity- and predictability-based entropy indices denoted a high degree of complementarity between those nonlinear methods. Finally, the relevance of frontal and parietal areas for recognition of emotions has revealed the essential role of those brain regions in emotional processes.Facultad de IngenieríaCentro de Investigaciones Óptica

Evaluation of brain functional connectivity from electroencephalographic signals under different emotional states

The identification of the emotional states corresponding to the four quadrants of the valence/arousal space has been widely analyzed in the scientific literature by means of multiple techniques. Nevertheless, most of these methods were based on the assessment of each brain region separately, without considering the possible interactions among different areas. In order to study these interconnections, this study computes for the first time the functional connectivity metric called cross-sample entropy for the analysis of the brain synchronization in four groups of emotions from electroencephalographic signals. Outcomes reported a strong synchronization in the interconnections among central, parietal and occipital areas, while the interactions between left frontal and temporal structures with the rest of brain regions presented the lowest coordination. These differences were statistically significant for the four groups of emotions. All emotions were simultaneously classified with a 95.43% of accuracy, overcoming the results reported in previous studies. Moreover, the differences between high and low levels of valence and arousal, taking into account the state of the counterpart dimension, also provided notable findings about the degree of synchronization in the brain within different emotional conditions and the possible implications of these outcomes from a psychophysiological point of view.- This publication is part of the R&D Projects Nos. PID2020-115220RB-C21, EQC2019-006063P, funded by MCIN/AEI/10.13039/501100011033/, and 2018/11744, funded by "ERDF A way to make Europe". This work was partially supported by Biomedical Research Networking Centre in Mental Health (CIBERSAM) of the Instituto de Salud Carlos III. Beatriz Garcia-Martinez holds FPU16/03740 scholarship from Spanish Ministerio de Educacion y Formacion Profesional

EEG and ECG nonlinear and spectral multiband analysis to explore the effect of videogames against anxiety

Currently, the use of video games has purposes that go beyond entertainment and has been gaining prominence in the health area. In this sense, it was hypothesized that it is possible to discriminate biological signals, namely electrocardiographic and electroencephalographic signals, collected from different participants stimulated through three different commercial video games, Tetris, Bejeweled and Energy. To test this hypothesis, a protocol was developed with the Trier Social Stress Test to induce and dose stress in the subjects to similar levels before each game session, in order to observe the effects of the three test games (3 study groups) at the physiological level. Initially collected at 2000 Hz, the signals were resampled to 500 Hz and filtered using a Butterworth low-pass filter. After filtering the signals, several representative features of the study signals were collected. These features consisted of a series of nonlinear metrics such as the Lyapunov exponent and Correlation Dimension, self-similarity metrics such as the Hurst exponent, and detrended fluctuation analysis, fractal dimensions - such as the Katz and Higuchi fractal dimensions - and metrics of signal chaos and activity, such as signal energy, Logarithmic entropy and Shannon entropy, and a number of spectral metrics for the EEG signal, which should be able to help identify any differences in the stress response. As a final result, a discrimination accuracy of 100% was obtained to discriminate the three study groups, using the top 20% of features selected by the F-score technique, using the coarse K Nearest Neighbor classifier.Atualmente, o uso de videojogos tem propósitos que vão além do entretenimento e tem vindo a ganhar destaque na área da saúde. Nesse sentido, foi formulada a hipótese de que é possível discriminar sinais biológicos, nomeadamente os sinais eletrocardiográficos e eletroencefalográficos, recolhidos de diferentes participantes estimulados através de três videojogos comerciais diferentes, Tetris, Bejeweled e Energy. Para testar esta hipótese foi desenvolvido um protocolo com o Trier Social Stress Test para induzir e dosear o stress nos sujeitos para níveis semelhantes antes de cada sessão de jogo, de forma a observar os efeitos dos três jogos de teste (3 grupos de estudo) a nível fisiológico. Recolhidos inicialmente a 2000 Hz, os sinais foram reamostrados a 500 Hz e filtrados utilizando um filtro passa-baixo de Butterworth. Após filtragem dos sinais, recolheram-se várias características representativas dos sinais de estudo. Estas características consistiram numa série de métricas não lineares, como o expoente de Lyapunov e a Dimensão de Correlação, métricas de auto similaridade como o exponente de Hurst e a análise de flutuação com trends removidas, dimensões fractais - como as dimensões fractais de Katz e Higuchi - e métricas de caos e atividade dos sinais, como a energia dos sinais, a entropia Logarítmica e a entropia de Shannon, e uma série de métricas espectrais para o sinal EEG, que devem ser capazes de ajudar a identificar qualquer diferença na resposta ao stress. Como resultado final obteve-se uma precisão de discriminação de 100% para discriminar os três grupos de estudo, utilizando as 20% das melhores características selecionadas pela técnica de F-score, recorrendo ao classificador coarse K Nearest Neighbor

Applications of non-invasive brain-computer interfaces for communication and affect recognition

Doctor of PhilosophyDepartment of Electrical and Computer EngineeringDavid E. ThompsonVarious assistive technologies are available for people with communication disorders. While these technologies are quite useful for moderate to severe movement impairments, certain progressive diseases can cause a total locked-in state (TLIS). These conditions include amyotrophic lateral sclerosis (ALS), neuromuscular disease (NMD), and several other disorders that can cause impairment between the neural pathways and the muscles. For people in a locked-in state (LIS), brain-computer interfaces (BCIs) may be the only possible solution. BCIs could help to restore communication to these people, with the help of external devices and neural recordings. The present dissertation investigates the role of latency jitter on BCIs system performance and, at the same time, the possibility of affect recognition using BCIs. BCIs that can recognize human affect are referred to as affective brain-computer interfaces (aBCIs). These aBCIs are a relatively new area of research in affective computing. Estimation of affective states can improve human-computer interaction as well as improve the care of people with severe disabilities. The present work used a publicly available dataset as well as a dataset collected at the Brain and Body Sensing Lab at K-State to assess the effectiveness of EEG recordings in recognizing affective states. This work proposed an extended classifier-based latency estimation (CBLE) method using sparse autoencoders (SAE) to investigate the role of latency jitter on BCI system performance. The recent emergence of autoencoders motivated the present work to develop an SAE based CBLE method. Here, the newly-developed SAE-based CBLE method is applied to a newly-collected dataset. Results from our data showed a significant (p < 0.001) negative correlation between BCI accuracy and estimated latency jitter. Furthermore, the SAE-based CBLE method is also able to predict BCI accuracy. In the aBCI-related investigation, this work explored the effectiveness of different features extracted from EEG to identify the affect of a user who was experiencing affective stimuli. Furthermore, this dissertation reviewed articles that used the Database for Emotion Analysis Using Physiological Signals (DEAP) (i.e., a publicly available affective database) and found that a significant number of studies did not consider the presence of the class imbalance in the dataset. Failing to consider class imbalance creates misleading results. Furthermore, ignoring class imbalance makes comparing results between studies impossible, since different datasets will have different class imbalances. Class imbalance also shifts the chance level. Hence, it is vital to consider class bias while determining if the results are above chance. This dissertation suggests the use of balanced accuracy as a performance metric and its posterior distribution for computing confidence intervals to account for the effect of class imbalance

Interpretable emotion recognition using EEG signals

Electroencephalogram (EEG) signal-based emotion recognition has attracted wide interests in recent years and has been broadly adopted in medical, affective computing, and other relevant fields. However, the majority of the research reported in this field tends to focus on the accuracy of classification whilst neglecting the interpretability of emotion progression. In this paper, we propose a new interpretable emotion recognition approach with the activation mechanism by using machine learning and EEG signals. This paper innovatively proposes the emotional activation curve to demonstrate the activation process of emotions. The algorithm first extracts features from EEG signals and classifies emotions using machine learning techniques, in which different parts of a trial are used to train the proposed model and assess its impact on emotion recognition results. Second, novel activation curves of emotions are constructed based on the classification results, and two emotion coefficients, i.e., the correlation coefficients and entropy coefficients. The activation curve can not only classify emotions but also reveals to a certain extent the emotional activation mechanism. Finally, a weight coefficient is obtained from the two coefficients to improve the accuracy of emotion recognition. To validate the proposed method, experiments have been carried out on the DEAP and SEED dataset. The results support the point that emotions are progressively activated throughout the experiment, and the weighting coefficients based on the correlation coefficient and the entropy coefficient can effectively improve the EEG-based emotion recognition accuracy

Discriminative power of EEG-based biomarkers in major depressive disorder: A systematic review

Currently, the diagnosis of major depressive disorder (MDD) and its subtypes is mainly based on subjective assessments and self-reported measures. However, objective criteria as Electroencephalography (EEG) features would be helpful in detecting depressive states at early stages to prevent the worsening of the symptoms. Scientific community has widely investigated the effectiveness of EEG-based measures to discriminate between depressed and healthy subjects, with the aim to better understand the mechanisms behind the disorder and find biomarkers useful for diagnosis. This work offers a comprehensive review of the extant literature concerning the EEG-based biomarkers for MDD and its subtypes, and identify possible future directions for this line of research. Scopus, PubMed and Web of Science databases were researched following PRISMA’s guidelines. The initial papers’ screening was based on titles and abstracts; then full texts of the identified articles were examined, and a synthesis of findings was developed using tables and thematic analysis. After screening 1871 articles, 76 studies were identified as relevant and included in the systematic review. Reviewed markers include EEG frequency bands power, EEG asymmetry, ERP components, non-linear and functional connectivity measures. Results were discussed in relations to the different EEG measures assessed in the studies. Findings confirmed the effectiveness of those measures in discriminating between healthy and depressed subjects. However, the review highlights that the causal link between EEG measures and depressive subtypes needs to be further investigated and points out that some methodological issues need to be solved to enhance future research in this field

Entropy Analysis of Univariate Biomedical Signals:Review and Comparison of Methods

International audienc

Entropy and fractal analysis of brain-related neurophysiological signals in Alzheimer's and Parkinson's disease.

Brain-related neuronal recordings, such as local field potential, electroencephalogram and magnetoencephalogram, offer the opportunity to study the complexity of the human brain at different spatial and temporal scales. The complex properties of neuronal signals are intrinsically related to the concept of 'scale-free' behavior and irregular dynamic, which cannot be fully described through standard linear methods, but can be measured by nonlinear indexes. A remarkable application of these analysis methods on electrophysiological recordings is the deep comprehension of the pathophysiology of neurodegenerative diseases, that has been shown to be associated to changes in brain activity complexity. In particular, a decrease of global complexity has been associated to Alzheimer's disease, while a local increase of brain signals complexity characterizes Parkinson's disease. Despite the recent proliferation of studies using fractal and entropy-based analysis, the application of these techniques is still far from clinical practice, due to the lack of an agreement about their correct estimation and a conclusive and shared interpretation. Along with the aim of helping towards the realization of a multidisciplinary audience to approach nonlinear methods based on the concepts of fractality and irregularity, this survey describes the implementation and proper employment of the mostly known and applied indexes in the context of Alzheimer's and Parkinson's diseases