Multimodal fnirs–emg platform for consumer emotional response decoding

Overview

The multimodal fNIRS–EMG platform is designed to decode implicit consumer emotional responses as they interact with products. By integrating functional near-infrared spectroscopy (fNIRS) and electromyography (EMG) data, the platform employs advanced machine learning models to identify affective states such as interest, hesitation, and comfort. This technology translates these insights into actionable product-design and value-creation metrics, enabling a deeper understanding of consumer preferences and behaviors.

Technical specifications

• Multimodal Data Integration: Combines fNIRS for neurophysiological data and EMG for muscular response detection.
• Advanced Analytics: Utilizes machine learning for affective-state classification, detecting subtle changes in consumer responses.
• Changepoint Detection: Employs algorithms to identify time-specific shifts in responses during product interaction.
• Event Alignment: Maps emotional dynamics to specific consumer movements and product-use events for comprehensive analysis.

Technology readiness level

Currently at TRL 3, the platform is in the experimental proof-of-concept phase. Future validation involves a structured one-year plan to refine tasks, enhance preprocessing methods, and validate metrics against consumer preferences and perceived quality.