EEG-Guided Music Generation through Cross-Modal Latent Alignment: A Conceptual Framework

Key Points

• The aim is to create a pipeline that generates music directly from brain activity signals.
• Developed a framework for mapping EEG signals to audio representation spaces.
• Applied cross-modal contrastive learning to align EEG features with audio embeddings.
• Utilized generative models for music synthesis based on inferred latent codes.
• Demonstrated potential for real-time music generation from brain signals.
• Showed successful alignment between EEG-derived features and audio embeddings.
• Indicated applications in brain-computer interfaces and creative AI.