Artificial intelligence (AI) and Machine learning (ML) based graphical user interface (GUI) system for early detection of depression symptoms using facial expression recognition and electroencephalogram

Background

Major depressive disorder (MDD) is a detrimental mental condition that inhibits everyday activities for individuals.

A leading cause of disability among psychological disorders, yet it remains widely undiagnosed and untreated due to lack of sensitive and reliable diagnostic tools and methods.

Technology

To overcome these societal limitations, we introduce a sophisticated system that integrates Artificial Intelligence (AI) and Machine Learning (ML) within a Graphical User Interface (GUI) to facilitate the early detection of depressive symptoms.

The system uniquely combines facial expression recognition with electroencephalography (EEG) to monitor emotional and cognitive states in real-time.
Can detect subtle changes indicative of depressive moods by analyzing facial movements micro-expressions, and brainwave patterns.
This continuous and non-intrusive monitoring approach provides an objective and proactive method for mental health assessment, reducing reliance on self-reported symptoms, which are often subjective and prone to bias.
Designed for integration with various digital devices, enhancing accessibility and enabling remote monitoring for healthcare providers.

Market Opportunity

Early detection and intervention of depression is crucial in managing depressive symptoms effectively, reducing the risk of severe outcomes such as suicide.

Traditional diagnostic methods rely heavily on patient self-reporting and clinical assessments, which can be subjective, inconsistent, and delayed, creating a significant gap in timely and accurate diagnosis.
Increasing demand for innovative digital health solutions that provide real-time monitoring and objective analysis.
Particularly relevant in the current landscape, where digital health and remote care have gained prominence due to the increasing acceptance of telemedicine and the necessity for non-contact health monitoring solutions.

Innovation and Meaningful Advantages

A unique combination of facial expression analysis and EEG data to deliver a holistic and accurate assessment of an individual’s emotional state.

Algorithms learn personal behavioral patterns over time, enhance detection accuracy and reduce false positives.
Personalized to each user’s unique emotional baseline, enabling early detection of deviations that may indicate depressive symptoms.
Provides insights into cognitive activity, such as decreased alpha wave activity commonly associated with depression.
User friendly GUI for varying tech-literacy levels, including adolescents and the elderly.
Continuous monitoring capability to facilitate timely interventions, empowering users to seek help at an early stage.
Both supports patients in self-monitoring their mental health but also provides healthcare professionals with valuable insights for informed clinical decisions.