Electroencephalography (EEG)-based emotion recognition (ER) is a growing research area in affective computing and human–computer interaction. EEG signals capture non-invasive measurements of brain activity and provide real-time access to emotional processes at the neural level. Compared to facial expressions or speech, EEG is less susceptible to voluntary masking and therefore holds promise for robust emotion decoding. However, traditional EEG-based ER methods are often limited by low classification accuracy, small sample sizes, and reliance on discrete or bipolar affective labels. Recent advances in deep learning, multimodal fusion, and semantic-space emotion models offer promising directions. This paper reviews state-of-the-art EEG-based ER approaches, focusing on signal preprocessing, feature extraction, and classification techniques. In addition, we identify emerging trends and persistent challenges, including dataset generalizability, inter-subject variability, and the need for fine-grained emotion annotation. By synthesizing current methodologies and outlining future paths, this review aims to guide both researchers and practitioners in the development of more accurate, scalable, and cognitively grounded EEG-based emotion recognition systems.

Amid rising sustainability demands, the automotive industry must understand how its Environmental, Social, and Governance (ESG) management influences consumer purchase decisions for high-involvement products. This study investigates this relationship by examining the mediating roles of brand value, perceived quality, and corporate trust. To test the research model, we analyzed survey data from Korean automotive market consumers using partial least-squares structural equation modeling. The findings reveal that governance transparency and social responsibility significantly enhance corporate trust and brand value, which are the primary drivers of purchase intention. In contrast, environmental initiatives do not directly foster trust, and the effect of perceived quality is fully mediated by brand value and corporate trust. This study advances value-driven consumption theory by demonstrating that non-financial ESG dimensions—especially governance and social responsibility—can supersede functional quality in shaping purchase decisions in high-involvement contexts. These findings suggest that automakers should prioritize governance and social initiatives as strategic levers to build trust and strengthen long-term consumer loyalty.