A rule-based exploratory analysis for discovery of multimodal biomarkers of ADHD using eye movement and EEG data

Abstract

Developing biomarkers for a complex neurodevelopmental disorder such as the attention deficit hyperactivity disorder (ADHD) is a challenging task since it is a multifactorial and multi-faceted condition. Researchers have been employing different sensing modalities to acquire measurements of the condition, however, there has been a lack of approaches that can adequately combine the multimodal data and detect interactions among the modalities. To demonstrate the concept and benefit of multimodal biomarker discovery, we conducted a multimodal data collection targeting the ADHD condition and demonstrated how a rule-based exploratory analysis approach could be used to analyze the data. To the best of our knowledge, our work is the first attempt to explore and identify interesting interactions among two modalities of data, eye movement data and the EEG signal, for multimodal biomarker discovery for ADHD. The detection of these interactions would help us better understand the condition and develop better prediction models and intervention strategies.

Keywords:

• Rule learning
• ADHD prediction
• multimodal data fusion