<h4>Background/objectives</h4>This systematic review presents how neural and emotional networks are integrated into EEG-based emotion recognition, bridging the gap between cognitive neuroscience and practical applications.<h4>Methods</h4>Following PRISMA, 64 studies were reviewed that outlined the latest feature extraction and classification developments using deep learning models such as CNNs and RNNs.<h4>Results</h4>Indeed, the findings showed that the multimodal approaches were practical, especially the combinations involving EEG with physiological signals, thus improving the accuracy of classification, even surpassing 90% in some studies. Key signal processing techniques used during this process include spectral features, connectivity analysis, and frontal asymmetry detection, which helped enhance the performance of recognition. Despite these advances, challenges remain more significant in real-time EEG processing, where a trade-off between accuracy and computational efficiency limits practical implementation. High computational cost is prohibitive to the use of deep learning models in real-world applications, therefore indicating a need for the development and application of optimization techniques. Aside from this, the significant obstacles are inconsistency in labeling emotions, variation in experimental protocols, and the use of non-standardized datasets regarding the generalizability of EEG-based emotion recognition systems.<h4>Discussion</h4>These challenges include developing adaptive, real-time processing algorithms, integrating EEG with other inputs like facial expressions and physiological sensors, and a need for standardized protocols for emotion elicitation and classification. Further, related ethical issues with respect to privacy, data security, and machine learning model biases need to be much more proclaimed to responsibly apply research on emotions to areas such as healthcare, human-computer interaction, and marketing.<h4>Conclusions</h4>This re The study and modeling of cognitive processes through a neurobiological approach are as old and diverse as the field of cognitive neuroscience itself. Cognitive neuroscience has grown around integrating a pattern of specialized but segregated neural networks that process different categories of sensory input and an abstract model of higher cognitive activity [ 32 , 33 , 34 ]. The Affective Posner Task has been used to study emotion regulation patterns through attentional shifts. This indicates that attentional biases toward emotionally salient cues modulate early- and late-stage EEG components [ 134 ]. Cognitive reappraisal tasks have provided key insights into how individuals actively regulate emotional reactivity, with findings indicating distinct neural activation patterns associated with successful versus unsuccessful emotion regulation [ 120 ]. The Go/NoGo Emotion Recognition Task has been employed to examine the rapid categorization of emotional stimuli. ERP trials show distinct activity patterns in response to positive and negative facial expressions at approximately 170 ms at occipitotemporal electrodes [ 132 ]. These findings suggest emotion processing may involve early perceptual mechanisms and higher-order cognitive appraisals. Resting-state EEG has become a meaningful way to establish a baseline of neural activity associated with various emotional states. Many such studies, using pre- and post-stimulus resting-state EEG recordings, have investigated functional connectivity changes following an affective stimulus, which are differentially affected in various populations [ 109 ]. Sampling rates as high as 5000 Hz have enabled the capture of accurate temporal patterns of neural activity associated with emotional processing. 4.2.4. Population-Specific and Contextual Considerations EEG investigations have diversified to include an increasingly broad range of populations, allowing the complex emotion and cognition interaction effects to be investigated across demographic and clinical groups. There are EEG studies investigating responses in terminal cancer patients receiving palliative care, which indicate altered affective processing due to chronic illness [ 122 ]. Real-time EEG monitoring allows therapists to track neural correlations of emotional regulation and adapt treatment strategies accordingly. Mindfulness and Meditation—EEG-guided mindfulness practices help individuals develop greater self-awareness and relaxation. Increased alpha and theta activity, commonly observed during meditation, improves emotional regulation and reduces stress. Real-Time EEG Monitoring—This approach provides adaptive feedback during therapy sessions, helping clinicians personalize interventions based on a patient’s real-time emotional and cognitive state. Furthermore, Table 6 outlines how EEG patterns reflect individual emotional and cognitive processing differences. On one - High emotional intelligence (EI) individuals tend to process advertising messages focused on social interactions and community issues, while low EI individuals prefer messages focused on physical objects and products. - High EI individuals demonstrate a “people perception” style of information processing, while low EI individuals show an “object perception” style. - The neural correlations of these differences in processing styles are reflected in distinct brain activity and connectivity patterns, as measured by EEG coherence and sLORETA. - EEG recording: Brain activity was measured using EEG to examine the neural correlates of the transferable placebo effect. - The study found a significant transferable placebo effect, where the placebo expectation established by analgesic treatment was able to alleviate negative emotions evoked by viewing unpleasant pictures. - The transferable placebo effect was associated with decreased P2 amplitude and increased N2 amplitude in EEG recordings, with the source located near the posterior cingulate region of the brain.

BACKGROUND While neuroimaging has provided insights into the formation of episodic memories in relation to voluntary memory recall, less is known about neural mechanisms that cause memories to occur involuntarily, for example as intrusive memories of trauma. Here we investigated brain activity shortly after viewing distressing events as a function of whether memories for those events later intruded involuntarily. The post-encoding period is particularly important because it is a period when clinical interventions could be applied. METHODS Thirty-two healthy volunteers underwent functional Magnetic Resonance Imaging (fMRI) while viewing distressing film clips, interspersed with five minutes of awake (post-encoding) rest. Voluntary memories of the films were assessed using free recall and verbal and visual recognition tests after a week, while intrusive (involuntary) memories were recorded in a diary throughout that week. RESULTS When analysing fMRI responses related to watching the films, we replicated findings that those "hotspots" (salient moments within the films) that would later become intrusive memories elicited higher activation in parts of the brain's salience network. Surprisingly, while the post-encoding persistence of multi-voxel correlation structures associated with entire film clips predicted subsequent voluntary recall, there was no evidence that they predicted subsequent intrusions. CONCLUSIONS Results replicate findings regarding the formation of intrusive memories during encoding, and extend findings regarding the consolidation of information in post-encoding rest in relation to voluntary memory. While we provided a first step using a naturalistic paradigm, further research is needed to elucidate the role of post-encoding neural processes in the development of intrusive memories.

<h4>Background</h4>Emotion dysregulation is a central feature of borderline personality disorder (BPD). Since impaired emotion regulation contributes to disturbed emotion functioning in BPD, it is crucial to study underlying neural activity. The current study aimed at investigating the neural correlates of two emotion regulation strategies, namely emotion acceptance and suppression, which are both important treatment targets in BPD.<h4>Methods</h4>Twenty-one women with BPD and 23 female healthy control participants performed an emotion regulation task during functional magnetic resonance imaging (fMRI). While watching fearful movie clips, participants were instructed to either accept or to suppress upcoming emotions compared to passive viewing.<h4>Results</h4>Results revealed acceptance-related insular underactivation and suppression-related caudate overactivation in subjects with BPD during the emotion regulation task.<h4>Conclusion</h4>This is a first study on the neural correlates of emotion acceptance and suppression in BPD. Altered insula functioning during emotion acceptance may reflect impairments in emotional awareness in BPD. Increased caudate activity is linked to habitual motor and cognitive processes and therefore may accord to the well-established routine in BPD patients to suppress emotional experiences. The current study aimed at investigating the neural correlates of two emotion regulation strategies, namely emotion acceptance and suppression, which are both important treatment targets in BPD. Methods Twenty-one women with BPD and 23 female healthy control participants performed an emotion regulation task during functional magnetic resonance imaging (fMRI). While watching fearful movie clips, participants were instructed to either accept or to suppress upcoming emotions compared to passive viewing. Results Results revealed acceptance-related insular underactivation and suppression-related caudate overactivation in subjects with BPD during the emotion regulation task. Conclusion This is a first study on the neural correlates of emotion acceptance and suppression in BPD. Altered insula functioning during emotion acceptance may reflect impairments in emotional awareness in BPD. Increased caudate activity is linked to habitual motor and cognitive processes and therefore may accord to the well-established routine in BPD patients to suppress emotional experiences. Functional imaging studies have tried to identify the neural correlates of emotion processing in BPD and provide growing evidence that BPD patients show an increased activation of limbic brain regions including the amygdala ( 16 ) and the insular cortex ( 17 ) during the processing of negative stimuli compared to neutral conditions. This limbic hyperactivation in BPD differed from the activation patterns reported for healthy individuals and has been repeatedly associated with abnormal prefrontal brain activation in response to emotionally challenging material ( 18 ). The current study extends these findings by investigating the effects of instructed use of emotion regulation strategies in patients with BPD using a comparable experimental design. Hence, the aim of our study was to determine the neural correlates of instructed emotion acceptance and suppression compared to a non-regulation condition (passive viewing) while watching fearful cinematic sequences. As emotion acceptance and suppression are likely to be distinct emotion regulation strategies, the focus of the current study lies on strategy-specific activity patterns and not on overlapping neural activity. Cognitive flexibility and executive functions were tested using a German verbal fluency task (Regensburg Word Fluency Test/RWT) ( 27 ) and the Trail-Making-Test part B (TMT-B) ( 28 ). 2.3. Emotion regulation task In the current study, we used a modified and previously validated version of the fearful face paradigm ( 29 , 30 ) to investigate neural activation during instructed emotion regulation, namely emotion acceptance and suppression. During fMRI scanning, participants watched 18 movie sequences with actors expressing intense fear in a pseudo-randomized order. First-level analyses involved brain activation (or deactivation) during emotional acceptance and suppression of fearful movie sequences contrasted to brain activation associated with passive viewing (contrasts reflecting activation: acceptance > passive viewing, suppression > passive viewing; contrasts reflecting deactivation: passive viewing > acceptance, passive viewing > suppression). At the second level, we conducted whole-brain analyses using within- and between-groups t -tests on these contrasts to identify instruction- and group-related differences in brain activation. There was a significant emotion regulation condition × group interaction ( F 1,42 = 9.58, p = 0.003, η 2 = 0.186), indicating a stronger acceptance-related deactivation in the BPD group compared to the HC. BPD, borderline personality disorder; HC, healthy controls. 3.2.2. Neural activation during emotion suppression In BPD patients, whole-brain analyses revealed decreased activation during emotion suppression compared to the passive viewing condition (deactivation). Analyses identified a significant cluster ( k = 551 voxels) with deactivation peaks in the right cerebellum and the left calcarine. Moreover, participants with BPD showed greater activation than HC in the right caudate during emotion suppression (suppression > passive viewing). Noteworthy, we did not However, some authors further suggested that acceptance-based strategies may require a longer training period than other regulation strategies to be effective ( 46 ). Following this argumentation, previous research indicated that insula activity during mindfulness meditation is linked to the level of prior meditation experience ( 47 ), which has been interpreted as an indicator for the learnability of mindfulness and acceptance-based skills, respectively.

Abstract The social world is dynamic and contextually embedded. Yet, most studies utilize simple stimuli that do not capture the complexity of everyday social episodes. To address this, we implemented a movie viewing paradigm and investigated how the everyday social episodes are processed in the brain. Participants watched one of two movies during an MRI scan. Neural patterns from brain regions involved in social perception, mentalization, action observation, and sensory processing were extracted. Representational similarity analysis results revealed that several labeled social features (including social interaction, mentalization, the actions of others, characters talking about themselves, talking about others, and talking about objects) were represented in superior temporal gyrus (STG) and middle temporal gyrus (MTG). The mentalization feature was also represented throughout the theory of mind network, and characters talking about others engaged the temporoparietal junction (TPJ), suggesting that listeners may spontaneously infer the mental state of those being talked about. In contrast, we did not observe the action representations in frontoparietal regions of the action observation network. The current findings indicate that STG and MTG serve as central hubs for social processing, and that listening to characters talk about others elicits spontaneous mental state inference in TPJ during natural movie viewing.

dynamics. movie viewing induces the reshaping of spontaneous brain dynamics into a reliable sequence of states whose occurrence are temporally aligned to specific features of the movie and reflect subjective engagement in the movie. Results Brain states differently expressed in movie compared to rest Functional magnetic resonance imaging (fMRI) data were analyzed for 18 healthy participants who were scanned during 8 min of resting-state followed by 20 min of movie viewing. HR and PD were also recorded. All participants completed a questionnaire immediately following the first neuroimaging session. Fourteen of these participants repeated this experimental session after 3 months. Brain states occurring at rest and during movie viewing were estimated using the HMM, a method which posits that the observed data arise from a small number of hidden states and their transitions 5 . To allow for a direct comparison between the states during the different experimental conditions (baseline rest and movie viewing, plus 3-month follow-up rest and movie viewing), we estimated brain states using concatenated time series of 14 participants who completed both experimental sessions (Methods). This allowed obtaining a group estimation of brain states for each experimental condition and session. The inversion of the HMM from these data yielded ten distinct states (Fig.  1 ). Confirmatory analyses were performed on 8 min of rest and 8 min of movie data, with HMM inversions performed on concatenated data as well as performed separately (i.e., movie and rest independently; Supplementary Figs  2 – 5 ). To understand the functional expression of these states, we coded their respective loadings onto each of 14 widely studied canonical brain networks (BNs) 23 (Supplementary Fig.  1 ). The expression of network activity was normalised so that zero corresponds to the average activity of that network across the movies and rest periods. The variability was also scaled according to that network’s