AI-generated music often sounds technically flawless but emotionally hollow. This analysis explores a promising multimodal research approach: combining EEG brainwave and eye-tracking data with MIR audio analysis to help AI understand what actually moves us when we listen. By measuring physiological responses (brain activity, pupil dilation) alongside musical features (tempo, harmony, timbre), researchers hope to bridge the gap between technical patterns and emotional meaning. I examine the proposed system's potential and its practical limitations - individual variation, expensive data collection, and the artificiality of lab conditions. The approach could make emotion-music connections more transparent, revealing which musical elements trigger physiological responses. But transparency about correlations isn't the same as understanding why. Knowing that certain frequencies trigger reactions won't explain why a Miles Davis Bâ™ has such power - some mysteries resist quantification.
Phil Conil - Berklee College of Music