2.3. Emotion Recognition

Integrating EEG and ECG signals offers promising results across several different domains, ranging from real-time emotion recognition systems, healthcare and psychological monitoring and safety applications.

One significant application of EEG and ECG integration is the development of responsive emotion recognition systems to improve real-time human-computer interaction. Systems proposed by Shin et al. [] and Hwang et al. [] focus on enhancing user experience by capturing complex emotional states in real time. Similarly, many other emotion recognition models have been emoployed extensively to differentiate subtle emotional states [], [], []. Moving beyond the tradition behavior-based inputs, such as keyboard and mouse, these systems ultilized multimodal signals to create human-centered interfaces, which can then be particularly useful in creating interactive content such as games or personalized media.

Healthcare sector also benefit greatly from EEG-ECG emotion recognition systems, particularly in non-invasive monitoring for mental health and eldercare. Zeng et al. [] explored wearable textile devices that continuously monitor EEG and ECG, aiding in dementia care by identifying emotional shifts in patients. Other studies [][] focused on analyzing emotional states to assess mental health indicators like stress, which is crucial for early intervention in mental health. By capturing the link between cortical and heart responses, EEG-ECG integration provides healthcare professionals with tools to track patient’s wellbeing more holistically.

In safety-critical settings, EEG and ECG combination has proven effective in monitoring attention levels and fatigue, contributing to workplace and transport safety. For instance, Michail et al. found that EEG and HRV markers could be used to assess sleepiness and attention lapses in drivers. They highlighted physiological patterns that correlated with driving errors, suggesting that such markers could even help prevent accidents []. The combined use of EEG and ECG data, as demonstrated in these studies, opens a future with vigilance-monitoring systems capable of providing real-time feedback in high-risk settings. Beyond workplace setting, studies by Panagiotis and Nalwaya et al. investigated how specific stimuli impact physiological response. They showed that EEG-ECG systems could detect autonomic responses tied to emotional processing under stress, which suggests potential applications in roles that require prolonged attention and emotional stability [], []. Expanding on mental state management, Patel et al. introduced a model assessing stress and fatigue levels using EEG and ECG, allowing for real-time feedback to improve performance in demanding environments, such as semi-autonomous driving [].