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Emotion recognition using electroencephalographic (EEG) signals represents an exciting frontier where neuroscience and artificial intelligence converge. Although traditional supervised learning approaches have made progress, variability between subjects and sensor configurations remains a significant obstacle. Recently, self-supervised learning (SSL) has emerged as a promising alternative to improve generalization across datasets. In particular, the combination of generative and contrastive mechanisms allows capturing both detailed and global representations, useful for distinguishing subtle emotional states even when the electrode configuration changes. An innovative proposal in this area is masked generative-contrastive learning, which uses a spatio-temporal encoder with region-based convolution to model local functional relationships, and a joint embedding architecture that extracts features robust to noise. This approach not only improves emotional discrimination but also facilitates transfer between different datasets, a key challenge in real-world applications.
From a business perspective, integrating artificial intelligence solutions like this into commercial products requires a robust technological ecosystem. For example, at Q2BSTUDIO we develop AI for businesses that can adapt to domains such as emotional monitoring. We also offer custom applications and custom software —by consulting here— to integrate EEG models into cross-platform platforms. Additionally, to ensure the security of biomedical data, we apply advanced cybersecurity, and to scale real-time processing we use AWS and Azure cloud services. Our business intelligence services with Power BI allow visualizing emotional patterns, while AI agents automate decision-making based on those insights. This type of architecture, combined with generative-contrastive learning techniques, opens the door to applications in mental health, brain-computer interfaces, and adaptive work environments.
