Mood Input-Output (MoodIO): A Brain-Computer Interface Application for Personalized Emotional Regulation in Children

Emotional regulation is crucial for healthy child development, yet many children struggle with managing their emotions effectively. This pilot study aims to demonstrate the potential to use brain-computer interface (BCI) and artificial intelligence technology to detect a child’s emotions and provide personalized interventions to achieve their desired emotional balance.

The Mood Input-Output (MoodIO) application integrates BCI technology and noninvasive electroencephalography (EEG) sensors to analyze alpha, beta, and theta brain waves. For this pilot study, two neurotypical participants, ages 12 and 9, were selected for initial testing. Each participant completed two trials while wearing an EEG cap, starting with a BCI headset that recorded brainwave activity during emotion-triggering inputs to establish baseline emotional responses. After a 5-minute rest period, participants engaged with MoodIO’s interventions, including parental voice support, adaptive music therapy, and guided breathing exercises, while EEG readings were continuously monitored. Brainwave data were wirelessly transmitted via Bluetooth to a machine-learning model, which analyzed the patterns, identified user emotions, and triggered recommended personalized interventions. Data collection focused on fluctuations in alpha wave activity, which correlates with relaxation and emotional regulation.

MoodIO influenced brainwave patterns associated with emotional regulation. EEG electrodes placed across multiple brain regions captured activity related to focus, auditory processing, and sensory integration. Preintervention data showed decreased alpha waves and increased beta activity, indicating heightened anxiety. During the intervention, participants engaged in calming exercises, music, parental voice recordings, and journaling. Postintervention EEG revealed increased alpha waves (relaxation), normalized beta waves (improved focus and regulation), and decreased theta waves (reduced anxiety).

With a sample size limited to two neurotypical participants, this pilot study provides evidence that MoodIO was able to detect emotions and provided personalized interventions, demonstrating its potential to serve as a tool for emotional support.