  When it comes to the classification of brain signals in real-life
applications, the training and the prediction data are often described by
different distributions. Furthermore, diverse data sets, e.g., recorded from
various subjects or tasks, can even exhibit distinct feature spaces. The fact
that data that have to be classified are often only available in small amounts
reinforces the need for techniques to generalize learned information, as
performances of brain-computer interfaces (BCIs) are enhanced by increasing
quantity of available data. In this paper, we apply transfer learning to a
framework based on deep convolutional neural networks (deep ConvNets) to prove
the transferability of learned patterns in error-related brain signals across
different tasks. The experiments described in this paper demonstrate the
usefulness of transfer learning, especially improving performances when only
little data can be used to distinguish between erroneous and correct
realization of a task. This effect could be delimited from a transfer of merely
general brain signal characteristics, underlining the transfer of
error-specific information. Furthermore, we could extract similar patterns in
time-frequency analyses in identical channels, leading to selective high signal
correlations between the two different paradigms. Classification on the
intracranial data yields in median accuracies up to $(81.50 \pm 9.49)\,\%$.
Decoding on only $10\%$ of the data without pre-training reaches performances
of $(54.76 \pm 3.56)\,\%$, compared to $(64.95 \pm 0.79)\,\%$ with
pre-training.
