THE EEG-BASED LEARNING ABILITY AND MENTAL HEALTH ASSESSMENT (MLA) FRAMEWORK: A CONCEPTUAL MODEL INTEGRATING NEUROPHYSIOLOGICAL, EMOTIONAL, AND COGNITIVE INDICATORS

Abstract

Understanding the neurophysiological basis of learning ability and mental health is becoming increasingly important in neuroscience, education, and clinical psychology. However, many current assessments mainly rely on behavioural or psychometric measures that do not account for internal neural mechanisms. The EEG-Based Learning Ability and Mental Health Assessment (MLA) Framework addresses this gap by combining twelve neuro-cognitive-emotional indicators derived from real-time EEG data. These indicators reflect how effectively a person can stay focused, maintain optimal alertness, regulate emotions, cope with stress, manage fatigue, balance left- and right-brain activity, and remain physiologically prepared to perform. This provides a comprehensive, human-centred view of brain function that illustrates how the brain operates in real-world learning and performance contexts. This conceptual paper examines the theoretical principles of each indicator and consolidates findings from cognitive neuroscience, affective neuroscience, psychophysiology, and EEG research to support the framework. The MLA Framework views brain activity as an interconnected system, offering a clearer understanding of learning readiness and mental state. It is designed for use in education, early screening, mental health assessment, and personalised neurofeedback, while also recognising the ongoing need for further empirical validation and the establishment of robust normative data.