LIFE: A music Listening and Imagery Hybrid fNIRS-EEG Dataset

We present LIFE, a multimodal EEG-fNIRS dataset that captures synchronized neural activity from 30 healthy adults during matched music listening and imagery tasks using six familiar excerpts in a paired-block design. To evaluate its validity, we conducted representative analyses that show clear neural differences between listening and imagery states. Specifically, we observed that parietal and frontal alpha-band power increases progressively from baseline through listening and reaches its peak during imagery. In addition, the frontal HbR concentrations rise reliably during listening relative to baseline and imagery. Furthermore, classification results indicate that the neural signals can be decoded effectively for both song identity and task state discrimination. In addition to providing the raw signals, we also release complete open-source scripts for preprocessing, feature extraction, and baseline classification to promote reproducibility and support ongoing research in music cognition, multimodal decoding, and imagery-based BCI.