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Brain rhythms and natural audiovisual speech processing

Principal Supervisor: Dr. Hyojin ParkLink opens in a new window

Co-supervisor: Prof. Ole Jensen

PhD project title: Brain rhythms and natural audiovisual speech processing

University of Registration: University of Birmingham

Project outline:

The main goal of our research group (Neural Oscillations in Multisensory Communication Group at the Centre for Human Brain Health (CHBH), University of Birmingham) is to understand the brain’s information processing in human communication. We are particularly interested in how brain rhythms (also known as neural oscillations) interact with audiovisual speech rhythms when we listen to the natural speech (perception) [1-3] as well as in a conversation (interaction between speech perception and production) in our daily lives.

To study this, we use Magnetoencephalography (MEG) and Optically Pumped Magnetometers (OPM), a neuroimaging method that allows for investigating the brain with excellent temporal and good spatial resolution. We aim to study how brain rhythms track speech rhythms (reactive process) as well as predict upcoming speech inputs (proactive process) from diverse perspectives, such as:

1) Local/global brain network within- and cross-frequency in order to identify feedforward and feedback (top-down) information processing using connectivity/causality measures. In addition, its relationship to the anatomical brain network (e.g. data acquired by Diffusion-Weighted Imaging).

2) In combination with neuromodulation techniques, e.g., sensory (via rapid frequency tagging) and/or ultrasound stimulation methods to understand the causal/modulatory mechanism.

3) Analysis of brain signals in combination with Machine Learning (ML) based Natural Language Processing (NLP) algorithms in order to identify feature representations in the brain [4].

4) Also, we plan to extend the study to identify diagnostic and prognostic biomarkers for patients with (age-related) hearing loss and mild/moderate traumatic brain injury (mTBI) in order to find new approaches to effective intervention and developing rehabilitation programmes for the patients.

5) We will study the neural mechanism in audiovisual speech tracking and semantic processing across the life span (children-adolescents-young adults-older adults) in order to intervene neurodevelopmental disorders such as speech and language disorders, Autism spectrum disorder (ASD), Attention deficit hyperactivity disorder (ADHD).


The PhD project will be well suited to candidates ideally having experience in electrophysiology, cognitive neuroimaging, computational neuroscience, psychology, computer science or similar disciplines. The research projects require the acquisition of MEG data and analysis of neural oscillations and their relationship to behaviour. Therefore, experience with MEG/EEG/OPM, spectral analysis, programming skills (Matlab, Python, or R), Machine Learning/Deep Learning skills is highly desirable.


[1] Park H, Ince RAA, Schyns PG, Thut G, Gross J (2018) Representational interactions during audiovisual speech entrainment: Redundancy in left posterior superior temporal gyrus and synergy in left motor cortex. PLoS Biology.

[2] Park H, Kayser C, Thut G, Gross J (2016) Lip movements entrain the observers’ low-frequency brain oscillations to facilitate speech intelligibility. eLife.

[3] Park H, Ince RAA, Schyns PG, Thut G, Gross J (2015) Frontal top-down signals increase coupling of auditory low-frequency oscillations to continuous speech in human listeners. Current Biology.

[4] Park H, Gross J (2022) Get the gist of the story: Neural map of topic keywords in multi-speaker environment. bioRxiv.


BBSRC Strategic Research Priority: Understanding the rules of life Neuroscience and Behaviour.

Techniques that will be undertaken during the project:

  • Magnetoencephalography (MEG)
  • Optically Pumped Magnetometers (OPM)
  • Magnetic Resonance Imaging (MRI) and Diffusion-Weighted Imaging (DWI)
  • Cognitive neuroscientific paradigms
  • Signal-processing and statistical assessment using Matlab and Python
  • Machine Learning (ML), particularly Natural Language Processing (NLP) based techniques


Contact: Dr. Hyojin ParkLink opens in a new window