Empowering machines to understand human emotions remains one of the primary challenges in developing artificial intelligence (AI). The affective brain-computer interface (BCI), which decodes emotional states based on brain signals, is an emerging field combining psychology, neuroscience, and AI. Brain signals are inherently uncontrollable, contain rich emotion-specific information, and provide a promising physiological basis for developing computing systems that support continual emotion monitoring. Since its inception, affective BCI research has required close collaboration across various disciplines: it depends on the use of information science for feature engineering and algorithm development, psychology for theoretical frameworks of emotion, and neuroscience for revealing the neural mechanisms underlying emotional processes. Such a demand for multidisciplinary co-operation forms the core focus of this review. Specifically, this paper focuses on the methods by which psychology-and neuroscience-based insights can inspire and advance affective BCI research.
We summarize the current progress at three levels: theoretical, technical, and applied. In the first one, recent advances in affective science offer new perspectives for shaping affective BCI paradigms. The traditional discrete and dimensional frameworks have laid the groundwork for emotion decoding but often overlook positive emotions and the dynamic intensity of affective experiences. Recent emotion theories emphasizing refined positive emotions, mixed emotions, and context-dependent emotions provide valuable directions for improving emotion representation. Affective computing should align with these developments, integrating them into computational models to enhance ecological validity. In turn, affective BCI research may also contribute to psychology by offering evidence to test and refine emotion theories, fostering reciprocal progress across disciplines. At the technical level, neuroscience provides crucial insights for building more robust affective BCIs. Findings on emotional valence lateralization and distributed emotion-associated brain representations can inform the design of models that better capture emotional processing complexity. Moreover, inter-subject brain synchronization research has revealed mechanisms that enhance model generalizability across users, suggesting that incorporating neuroscientific findings can substantially improve the performance and reliability of affective BCIs. At the application level, affective BCIs are expanding beyond emotion recognition toward understanding emotion-related individual differences. Variability between individuals—often treated as noise—may instead offer meaningful information about personality traits or mental health conditions. In the long term, the goal of affective BCI systems may evolve from accurately identifying emotions to comprehensively understanding each individual's psychological tendencies and dynamic affective patterns across multimodal neural and behavioral data. We advocate for stronger integration between affective BCI technologies and practical domains. Such integration allows practical demands to drive technological development, ensuring that affective BCI remains human-centered.
Finally, we discuss the technical challenges of affective BCI, including extending algorithms from controlled laboratory settings to real-world scenarios, advancing sensor technology for more convenient and reliable brain-signal acquisition, and leveraging large models to enhance performance for affective BCI. Specifically, we emphasize the vital role of ethical considerations: as affective BCIs move from passive emotion detection toward active emotional support or intervention, the responsibility of humans as rational moral agents in a future era of man-computer symbiosis must be considered, ensuring the autonomy of human emotions.
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