Hevolve AI: Self-Evolving Multimodal AI Agents

Turn your domain expertise into AI agents that keep learning. Hevolve AI lets experts build multimodal AI systems by talking to them and correcting them in real time, with no code to write.

Key Features

Quick Links

© 2024 Hevolve AI Pvt Ltd. All rights reserved.

← All research
Brain–Computer Interfaces
arXiv (EEG) · June 23, 2026

EEG Interpretation Across Chant Listening: A Single-Subject Pilot Investigation Using Spectral and Functional Connectivity Analysis

Prerna Singh, Aishwarya Ghosh, Neelam Sinha +1 more

This technical report records EEG from a single healthy five-year-old across five listening conditions: resting quietly, three chants (Shiv Tandav Stotra, Mahasudarshan Mantra, and Aum), and the drone of a tanpura. The analysis uses spectral power together with functional connectivity measured by the weighted Phase Lag Index.

Two things are worth being clear about. It is one participant, so this is a pilot that can suggest where to look and cannot establish anything general. And the weighted Phase Lag Index is chosen for a specific reason: it is designed to discount the spurious coupling that appears when two electrodes pick up the same source, so apparent connectivity is less likely to be an artefact of the recording.

What the report offers is condition-specific modulation of neural oscillations, meaning the brain's rhythms looked different depending on what was playing. That is a starting point rather than a conclusion, and the write-up presents it as one.

From the arXiv (EEG) abstract

This technical report presents an EEG-based investigation of neural activity across five auditory conditions: Resting State (RS), Shiv Tandav Stotra (STS), Mahasudarshan Mantra (MM), Aum Chant, and Tanpura Listening. EEG recordings acquired from a healthy 5-year-old participant were analyzed using spectral power estimation and functional connectivity measures based on the weighted Phase Lag Index (wPLI). Spectral analysis revealed condition-specific modulation of neural…


More Brain–Computer Interfaces papers