I study the neural basis of language and speech processing. When humans listen to speech, the acoustic signal that enters the ears is a complex pattern of air pressure fluctuations. Yet, listeners intuitively and almost instantaneously experience meaning in these sounds. My research focuses on the transformations that happen in the brain to enable this.

To study this I mainly use MEG and EEG with reverse correlation. Reverse correlation allows us to think of brain responses as a continuous transformation of the speech signal, rather than relying on pre-defined events in the stimuli. It also allows us to disentangle responses related to different levels of processing, such as the formation of auditory and lexical representations.

I use Python to develop tools to make this research possible, and many of those tools are available in the open source libraries MNE-Python and Eelbrain.