NI-Talk with Dr. Michael Tangermann

On 11 December 2019 Apoorv Shukla gives a Talk on the topic "Classification of small and heterogeneous data in BCI" at the invitation of the research group Neuroinformatics. The Faculty of Computer Science cordially invites all interested parties!


Real-time interaction with the brain in closed-loop systems such as brain-computer interfaces (BCIs) would not be possible without machine learning methods capable to decode noisy brain signal recordings like the electroencephalogram (EEG). A widely-used experimental paradigm requires the user to attend to specific stimuli and ignore others, leading to so-called target and non-target event-related responses (ERP) in the EEG.

The training of e.g. a classification model is challenged specifically by two aspects: first, the training data sets can be very small. This problem arises specifically when working with patients, where the duration of a single BCI session can be extremely limited for reasons, which are beyond the influenced of the experimenter. Second, the data may violate i.i.d. assumptions made specifically by model classes of low complexity, which are specifically suitable to deal with small training data sets. An example of such a violation is the presence of subgroups in the ERP responses of both, the target and the non-target class, or non-stationary feature distributions.

In my talk, I will provide examples of such data derived from an auditory oddball paradigm and from a recent visual ERP paradigm, where the user's interacts directly with the environment instead of via an additional user interface. I will also propose two different algorithmic mitigations for the above challenges. Both are based on variations of the standard LDA classifier and make use of transfer learning and data augmentation.


After graduation in Computer Science (University of Tübingen, Germany), Michael Tangermann was a member of the Berlin BCI (BBCI) research lab at the TU Berlin before he became head of the brain state decoding laboratory at the Albert-Ludwigs-University of Freiburg, Germany in 2013. Currently he in addition is substitute professor of the Autonomous Intelligent Systems Lab. Embedded within the cluster of excellence BrainLinks BrainTools, Michael Tangermann investigates machine learning approaches to tackle neuroscientific and neurotechnological data problems. His research interests comprise adaptive unsupervised and supervised methods for the classification and regression of non stationary brain signals, regularization techniques, reinforcement learning and deep learning. He translates these methods into clinical brain-computer interface applications (e.g. for rehabilitation of stroke-induced hand motor and language deficits, closed-loop deep brain stimulation in Parkinson's disease) and to create novel human-robot interaction paradigms.


NI-Talk with Dr. Michael Tangermann
When: 11 December 2019, 11 am
Where: SR5 | Währinger Straße 29, 1090 Vienna

Research Group Neuroinformatics