Mikolaj Kegler

Mikolaj Kegler

PhD candidate in Neurotechnology

Imperial College London

About me

Hi, my name is Mikolaj Kegler. I am a final-year PhD student in the Department of Bioengineering & Centre for Neurotechnology at Imperial College London (ICL). I am a member of the Sensory Neuroengineering group led by Prof. Tobias Reichenbach.

My PhD research focuses on understanding neural mechanisms underlying perception and comprehension of natural speech, especially in challenging listening conditions. In my work, I am combining computational modelling with neuroimaging and non-invasive brain stimulation to understand how natural speech is processed across human auditory pathways.

Apart from my neuroscience research, I am a scientific advisor at Logitech and develop deep learning algorithms for speech signal processing. An interactive demo of our latest project, Deep speech inpainting, is available here. I am also a graduate teaching assistant in a range of computational courses at ICL.

Currently, I am working as an Applied Scientist Intern at Amazon Lab126.

For more details, please see my CV, explore this website or just send me an email!


  • Neurotechnology
  • Data Analysis & Modelling
  • Auditory Cognitive Neuroscience
  • Computational Neuroscience
  • Biomedical Signal Processing
  • Brain Stimulation
  • Speech Signal Processing


  • PhD in Neurotechnology, 2018 - 2022 (expected)

    Imperial College London, UK

  • MRes in Neurotechnology, 2018

    Imperial College London, UK

  • MSc in Biomedical Engineering, 2017

    Imperial College London, UK

  • BEng in Biomedical Engineering, 2016

    Warsaw University of Technology, Poland


*-equal contribution

Also available on my Google Scholar profile.

(2020). Deep Speech Inpainting of Time-Frequency Masks. Proc. Interspeech 2020.


(2020). Hearing aids do not alter cortical entrainment to speech at audible levels in mild-to-moderately hearing-impaired subjects. Frontiers in Human Neuroscience.

PDF Dataset DOI

(2019). Speech-VGG: A deep feature extractor for speech processing. arXiv.


(2019). Decoding of selective attention to continuous speech from the human auditory brainstem response. NeuroImage.




More code on my GitHub.


Journal club

Link to the club’s homepage.

Modelling the effects of tACS on speech processing

(Code) Computational model for the effect of non-invasive brain stimulation on speech in noise processing.


(Code) Transferable pre-trained feature extractor for speech processing.

Deep Speech Inpainting

(Demo) Algorithm for recovering missing or severely degraded parts of time-frequency representations of speech.

Complex TRF

(Code) Complex TRFs for modelling auditory brainstem responses to continuous speech from full-cap EEG

Fundamental waveform extraction

(Code) EMD-based algorithm for the extraction of F0 waveform from continuous speech. Maintained code. Original implementation by A.E. Forte.

EEG tools

(Code) Custom set of tools for EEG processing and analysis. Based on pyEEG by H. Weissbart.