Symbolic Analysis of Brain Dynamics Detects Negative Stress

Abstract

Abstract:The electroencephalogram (EEG) is the most common tool used to study mental disorders. In the last years, the use of this recording for recognition of negative stress has been receiving growing attention. However, precise identification of this emotional state is still an interesting unsolved challenge. Nowadays, stress presents a high prevalence in developed countries and, moreover, its chronic condition often leads to concomitant physical and mental health problems. Recently, a measure of time series irregularity, such as quadratic sample entropy (QSEn), has been suggested as a promising single index for discerning between emotions of calm and stress. Unfortunately, this index only considers repetitiveness of similar patterns and, hence, it is unable to quantify successfully dynamics associated with the data temporal structure. With the aim of extending QSEn ability for identification of stress from the EEG signal, permutation entropy (PEn) and its modification to be amplitude-aware (AAPEn) have been analyzed in the present work. These metrics assess repetitiveness of ordinal patterns, thus considering causal information within each one of them and obtaining improved estimates of predictability. Results have shown that PEn and AAPEn present a discriminant power between emotional states of calm and stress similar to QSEn, i.e., around 65%. Additionally, they have also revealed complementary dynamics to those quantified by QSEn, thus suggesting a synchronized behavior between frontal and parietal counterparts from both hemispheres of the brain. More precisely, increased stress levels have resulted in activation of the left frontal and right parietal regions and, simultaneously, in relaxing of the right frontal and left parietal areas. Taking advantage of this brain behavior, a discriminant model only based on AAPEn and QSEn computed from the EEG channels P3 and P4 has reached a diagnostic accuracy greater than 80%, which improves slightly the current state of the art. Moreover, because this classification system is notably easier than others previously proposed, it could be used for continuous monitoring of negative stress, as well as for its regulation towards more positive moods in controlled environments.

Multi-Lag Analysis of Symbolic Entropies on EEG Recordings for Distress Recognition

“…In another work, a decrease of relative power in subjects facing a series of distressful stimuli was also described (Bastos Filho et al, 2012). Finally, the fact that this trend is maintained throughout the different time-lags reinforces a previous study where no lag was applied (Garćıa-Mart́ınez et al, 2017).…”

“…However, only a few of them have analyzed this phenomena from a non-linear point of view (García-Martínez et al, 2016). Recently, another approach reported that symbolic analysis of brain dynamics was able to detect distress (Garćıa-Mart́ınez et al, 2017). In that study, PE and its extension called AAPE were used to assess brain dynamics for each EEG channel.…”

“…Although the trials contained within the dataset cover the whole valence-arousal space, only two subsets corresponding to distress and calmness emotional states were studied in the present study, as shown in Figure 1. Indeed, calmness and distress groups were selected according to previous works dealing with the same problem (Bastos Filho et al, 2012; Pomer-Escher et al, 2014; García-Martínez et al, 2016; Garćıa-Mart́ınez et al, 2017). Hence, distress trials were selected from arousal and valence levels higher than 5 and lower than 3, respectively.…”

“…But, few studies have applied non-linearity to automatic detection of negative stress through EEG recordings (García-Martínez et al, 2019a). This is the case of symbolic entropies, such as Permutation Entropy (PE) (Bandt and Pompe, 2002) and Amplitude-Aware Permutation Entropy (AAPE) (Azami and Escudero, 2016), having demonstrated their efficiency in discriminating between calmness and distress (Hosseini and Naghibi-Sistani, 2011; Garćıa-Mart́ınez et al, 2017; Martínez-Rodrigo et al, 2019). Here, the quantification of similar patterns is typically obtained through consecutive samples, or their averaging, within a complete time series.…”

Application of Permutation Entropy and Permutation Min-Entropy in Multiple Emotional States Analysis of RRI Time Series

“…However, little attention has been paid so far to PE of the RRI time series for emotion recognition. To the best of our knowledge, the research developed by García-Martínez et al [23] is the only one in which this symbolic entropic measure has been considered for automatic recognition of emotions. More precisely, EEG records have been analyzed for discerning between emotions of calm and stress.…”