Education

Master Thesis: Investigating Attentional Modulation of Otoacoustic Emissions: A Simulation and Experimental Comparison

Starting date: February 2025

Otoacoustic emissions (OAEs) are subtle sounds produced by the cochlea as a result of its active amplification processes. They are not only critical for understanding cochlear function but also serve as a valuable tool for non-invasive auditory diagnostics. Recent research suggests that OAEs may be influenced by higher-order processes such as auditory attention, making them a promising area of study to explore the interplay between cognitive and peripheral auditory mechanisms.

This Master thesis will utilize an existing cochlear model capable of simulating otoacoustic emissions. The candidate will test stimuli that have been used in recent experiments to compare simulation results with experimental data. A key focus will be implementing a mechanism within the model to simulate the ability to attend to a specific speaker in a “speech-in-noise” scenario, where male and female speakers are presented simultaneously. The goal is to determine whether attentional focus introduces measurable differences in the simulated OAEs.

Additionally, the thesis will aim to quantify these potential changes in OAEs due to attentional modulation and systematically compare the findings with experimental results. This project provides an exciting opportunity to bridge computational modeling with real-world auditory experiments, contributing to the understanding of how cognitive processes interact with auditory physiology.

Skills and Qualifications:

  • Programming proficiency: Advanced skills in MATLAB, Python, or similar programming environments for implementing models and analyzing data. The cochlear model is written in Python.
  • Signal processing expertise: Understanding of time-frequency analysis, filtering, and noise handling techniques.
  • Mathematics skills: Proficiency in mathematical concepts related to differential equations, linear systems, and data modeling.
  • Understanding of electrodynamics and circuit theory: Familiarity with concepts such as resistances, admittances, and their role in modeling physical systems. The candidate should be able to apply these principles to understand and work with the cochlear model.
  • Experience with computational modeling: Knowledge of simulating physiological processes, preferably within auditory systems.

Application and Contact:

  • If you are interested, please send an email to Janna Steinebach (janna.steinebach@fau.de)
  • Include your CV and a transcript of records in your application

 

Bachelor or Master Thesis: Analysis and Extraction of Otoacoustic Emissions from Noisy Recordings Using Linear Modeling Approaches

Starting date: February 2025

Otoacoustic emissions (OAEs) are faint sounds generated by the inner ear as a byproduct of the cochlea’s active amplification process. They provide a non-invasive means of assessing cochlear function and are commonly used in hearing diagnostics to evaluate the integrity of the auditory system.

This thesis focuses on analyzing otoacoustic emissions (OAEs) generated in response to stimuli that resemble natural running speech. The recordings under investigation are inherently noisy, which complicates traditional evaluation methods such as cross-correlation. These methods often fail to reliably identify the presence of OAEs due to the high levels of background noise.

To address this challenge, the thesis proposes an alternative approach: applying a linear model to map the stimuli to the recorded signals. This method aims to isolate the components of the recording corresponding to the stimuli. The residual signal—after subtracting the modeled components—should predominantly consist of noise and the OAE waveform. By analyzing this residual, the thesis seeks to reliably identify and characterize the OAE despite the noisy recording environment.

The study aims to validate the effectiveness of this method and explore its potential as a robust alternative for analyzing otoacoustic emissions in complex auditory scenarios.

Skills and Qualifications:

  • Programming experience:Proficiency in Python for signal analysis and modeling.
  • Strong foundation in signal processing:Familiarity with techniques such as filtering, cross-correlation, and Fourier analysis.
  • Interest in auditory neuroscience:Curiosity about the physiological mechanisms of hearing and how otoacoustic emissions are generated.

Application and Contact:

  • If you are interested, please send an email to janna.steinebach@fau.de
  • Include your CV and a transcript of records in your application.

 

Winter Term 
Module: Algorithms and Data Structures (for Medical Technology) 
Lecturer: Prof. Dr. Tobias Reichenbach 
Type: Lecture  

Module: Algorithms and Data Structures (for Medical Technology) 
Lecturer: Alina Schüller, Jonas Auernheimer 
Type: Exercises 

Module: Neurotechnology Project 
Lecturer: Prof. Dr. Tobias Reichenbach, Constantin Jehn, Jasmin Riegel
Type: Project
Summer Term 
Module: Algorithms and Data Structures (for Medical Technology) 
Lecturer: Constantin Jehn, Jasmin Riegel
Type: Exercises  

Module: Neurotechnology Project 
Lecturer: Prof. Dr. Tobias Reichenbach, Constantin Jehn, Jasmin Riegel Janna Steinebach
Type: Project 

Module: Computational Neurotechnology 
Lecturer: Prof. Dr. Tobias Reichenbach, Pablo Ochoa de Eribe Delgado 
Type: Lecture and Exercises 

Module: Colloquium Sensory Neuroscience 
Lecturer: Prof. Dr. Tobias Reichenbach 
Type: Colloquium