ARIEL: Brain-Computer Interfaces meet Large Language Models for Emotional Support Conversation

In an era characterized by unprecedented virtual connectivity, paradoxically, individuals often find themselves disconnected from genuine human interactions. The advent of remote working arrangements, compounded by the influence of digital communication platforms, has fostered a sense of isolation among people. Consequently, the prevailing socio-technological landscape has underscored the critical need for innovative solutions to address the emotional void. Conversational systems help people improve their everyday tasks with informative dialogues, and recent applications employ them to target emotional support conversation tasks. Nevertheless, their understanding of human feelings is limited, as they depend solely on information discernible from the text or the users' emotional declarations. Recently, Brain-Computer Interfaces (BCIs), devices that analyze electroencephalographic (EEG) signals, have increasingly become popular given their minimally invasive nature and low cost, besides enabling the detection of users' emotional states reliably. Hence, we propose ARIEL, an emotionAl suppoRt bcI dEvices and Llm-based conversational agent that aims at supporting users' emotional states through conversations and monitoring them via BCI. In this way, it is possible to comprehend the users' feelings reliably, thus making the conversational agent aware of users' emotional evolution during conversations. Our framework makes the LlaMA 2 chat model communicate with an emotion recognition BCI-based system to achieve the emotional support conversation goal. Also, we present a controlled running example that shows the potential of our model and its effective functioning, made possible by a wisely designed hard-prompt strategy. In the future, we will conduct an in-vivo experiment to evaluate the system and its components.