Towards Improving EEG-Based Intent Recognition in Visual Search Tasks.

Accurate estimation of intentions is a prerequisite in a non-verbal human-machine collaborative search task. Electroencephalography (EEG) based intent recognition promises a convenient approach for recognizing explicit and implicit human intentions based on neural activity. In search tasks, implicit intent recognition can be applied to differentiate if a human is looking at a specific scene, i.e., Navigational Intent, or is trying to search a target to complete a task, i.e., Informational Intent. However, previous research studies do not offer any robust mechanism to precisely differentiate between the intents mentioned above. Additionally, these techniques fail to generalize over several participants. Thus, making these methods unfit for real-world applications. This paper presents an end-to-end intent classification pipeline that can achieve the highest mean accuracy of $$97.89 \pm 0.74$$ (%) for a subject-specific scenario. We also extend our pipeline to support cross-subject conditions by addressing inter and intra-subject variability. The generalized cross-subject model achieves the highest mean accuracy of $$96.83 \pm 0.53$$ (%), allowing our cross-subject pipeline to transfer learning from seen subjects to an unknown subject, thus minimizing the time and effort required to acquire subject-specific training sessions. The experimental results show that our intent recognition model significantly improves the classification accuracy compared to the state-of-the-art.