dr. M.W. Tangermann (Michael)

My expertise is in developing machine learning algorithms, specifically in order to be applied to time series data obtained from neural recordings. These signals typically are high-dimensional, very noisy and non-stationary, i.e. they display changing signal distributions over time, which poses specific challenges to decoding algorithms.
I apply these methods by implementing closed-loop systems that intact with the nervous system. Such neurotechnological systems are brain-computer interfaces for communication, control and rehabilitation and adaptive deep brain stimulation systems for patients with neurological or psychiatric diseases. This closed-loop interaction requires machine learning solutions on how to choose stimulation parameters given different brain states.
On the signal side, I work with recordings of the electroencephalogram (EEG), electrocorticogram (ECoG), local field potentials (LFP), magnetoencephalogram (MEG) and electromyogram (EMG). In addition, behavioural data like responses upon motor and language tasks are included.
I translate the methods developed into neurotechnological applications for clinical- and non-clinical purposes, e.g., brain-computer interfaces for communication, control and monitoring, adaptive deep brain stimulation, language rehabilitation after stroke, and hand motor rehabilitation.

• Natural Computing & Neurotechnology
• Language and Communication

• Artificial cognitive systems
• Brain Computer Interfacing