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Speech is our most natural way of communication and the loss of the ability to speak is therefore devastating to patients. A speech neuroprostheses that directly reconstructs speech processes from neural activity could provide a new means of communications to these severely affected patients. In this presentation, I will present some approaches to reconstruct different representations of speech from intracranial recordings and highlight how they can be used to build a speech neuroprosthesis. The decoding of speech processes is particularly challenging, as not only the neural, but also the target signal has complex, nonlinear dynamics. I will stress the use of interpretable machine learning models for this task to ensure that meaningful activity is decoded and scientific insights might be generated as a side product.
Dr. Christian Herff is an assistant professor in the School for Mental Health and Neuroscience at Maastricht University where he leads the invasive BCI research line. His research interest lays in the application of machine learning technology to neurophysiological data for Brain-Computer Interfaces and neuroscience research. With a particular focus on the decoding of speech processes from intracranial data, he tries to improve the lives of severely paralyzed patients while simultaneously improving our understanding of complex higher order cognition. He emphasizes the ability to achieve interpretable results based on computational models. In particular, visualization of complex dynamic models, such as deep neural networks, is of interest to him.
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