This article introduces a systematic review on arousal classification based on electrodermal activity (EDA) and machine learning (ML). From a first set of 284 articles searched for in six scientific databases, fifty-nine were finally selected according to various criteria established. The systematic review has made it possible to analyse all the steps to which the EDA signals are subjected: acquisition, pre-processing, processing and feature extraction. Finally, all ML techniques applied to the features of these signals for arousal classification have been studied. It has been found that support vector machines and artificial neural networks stand out within the supervised learning methods given their high-performance values. In contrast, it has been shown that unsupervised learning is not present in the detection of arousal through EDA. This systematic review concludes that the use of EDA for the detection of arousal is widely spread, with particularly good results in classification with the ML methods found.

Speech emotion recognition has become increasingly important in a wide range of applications, driven by the development of large transformer-based natural language processing models. However, the large size of these architectures limits their usability, which has led to a growing interest in smaller models. In this paper, we evaluate nineteen of the most popular small language models for the text and audio modalities for speech emotion recognition on the IEMOCAP dataset. Based on their cross-validation accuracy, the best architectures were selected to create ensemble models to evaluate the effect of combining audio and text, as well as the effect of incorporating contextual information on model performance. The experiments conducted showed a significant increase in accuracy with the inclusion of contextual information and the combination of modalities. The results obtained were highly competitive, outperforming numerous recent approaches. The proposed ensemble model achieved an accuracy of 82.12% on the IEMOCAP dataset, outperforming several recent approaches. These results demonstrate the effectiveness of ensemble methods for improving speech emotion recognition performance, and highlight the feasibility of training multiple small language models on consumer-grade computers.

This paper presents the identification of arousal and valence during visual stimuli exposure using electroencephalograms (EEGs) and functional near-infrared spectroscopy (fNIRS) signals. Specifically, various images were shown to several volunteers to evoke different emotions defined by their level of arousal and valence, such as happiness, sadness, fear, and anger. Brain activity was recorded using the Emotiv EPOC X and NIRSport2 devices separately. The recorded signals were then processed and analyzed to identify the primary brain regions activated during the trials. Next, machine learning methods were employed to classify the evoked emotions with highest accuracy values of 71.3% for EEG data with a Multi-Layer Perceptron (MLP) method and 64.0% for fNIRS data using a Bagging Trees (BAG) algorithm. This approach not only highlights the effectiveness of using EEG and fNIRS technologies but also provides insights into the complex interplay between different brain areas during emotional experiences. By leveraging these advanced acquisition techniques, this study aims to contribute to the broader field of affective neuroscience and improve the accuracy of emotion recognition systems. The findings could have significant implications for developing intelligent systems capable of more empathetic interactions with humans, enhancing applications in areas such as mental health, human–computer interactions, or adaptive learning environments, among others.

Deficits in social cognition may impair emotional processing and facial emotional recognition (FER) in patients with bipolar disorder (BD) and schizophrenia. FER is generally explored using photographs or images of static faces that do not fully capture the complexity of real-life facial stimuli. To overcome this limitation, we developed a set of dynamic virtual faces depicting six basic emotions (i.e. happiness, sadness, anger, fear, disgust, and surprise) and a neutral expression suitable for presentation in immersive and nonimmersive virtual realities. This study presents preliminary findings on the differences in FER accuracy from a frontal view between immersive and nonimmersive virtual realities among patients experiencing a relapse of schizophrenia (n = 10), a manic phase of BD (n = 10), and a group of healthy controls (HCs) (n = 10). As a secondary objective, we compare the FER accuracy across these three groups. Patients with schizophrenia and BD showed similar accuracy in recognizing emotions in immersive and nonimmersive virtual reality settings. However, patients with schizophrenia exhibited lower FER accuracy than HCs in both settings. Individuals with BD showed intermediate accuracy between those with schizophrenia and HCs, although these differences were not statistically significant. Notably, recognition of negative emotions was significantly impaired in both groups of patients.