Frequency-Specific Neural Response and Cross-Correlation Analysis of Envelope Following Responses to Native Speech and Music Using Multichannel EEG Signals: A Case Study
Md. Mahbub Hasan, Md Rakibul Hasan, Md Zakir Hossain, Tom Gedeon
- Year
- 2025
- Access
- Open access
Abstract
Although native speech and music envelope following responses (EFRs) play a crucial role in auditory processing and cognition, their frequency profile, such as the dominating frequency and spectral coherence, is largely unknown. We have assumed that the auditory pathway - which transmits envelope components of speech and music to the scalp through time-varying neurophysiological processes - is a linear time-varying system, with the envelope and the multi-channel EEG responses as excitation and response, respectively. This paper investigates the transfer function of this system through two analytical techniques - time-averaged spectral responses and cross-spectral density - in the frequency domain at four different positions of the human scalp. Our findings suggest that alpha (8-11 Hz), lower gamma (53-56 Hz), and higher gamma (78-81 Hz) bands are the peak responses of the system. These frequently appearing dominant frequency responses may be the key components of familiar speech perception, maintaining attention, binding acoustic features, and memory processing. The cross-spectral density, which reflects the spatial neural coherence of the human brain, shows that 10-13 Hz, 27-29 Hz, and 62-64 Hz are common for all channel pairs. As neural coherences are frequently observed in these frequencies among native participants, we suggest that these distributed neural processes are also dominant in native speech and music perception.
Keywords
Related papers
Artificial intelligence: a modern approach
1995
Are we ready for autonomous driving? The KITTI vision benchmark suite
Andreas Geiger, P Lenz, R. Urtasun
2012
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Martı́n Abadi, Ashish Agarwal, Paul Barham +17 more
2016
Vision meets robotics: The KITTI dataset
Andreas Geiger, Philip Lenz, Christoph Stiller +1 more
2013