  Electroencephalography (EEG) classification is a versatile and portable
technique for building non-invasive Brain-computer Interfaces (BCI). However,
the classifiers that decode cognitive states from EEG brain data perform poorly
when tested on newer domains, such as tasks or individuals absent during model
training. Researchers have recently used complex strategies like Model-agnostic
meta-learning (MAML) for domain adaptation. Nevertheless, there is a need for
an evaluation strategy to evaluate the fast adaptability of the models, as this
characteristic is essential for real-life BCI applications for quick
calibration. We used motor movement and imaginary signals as input to
Convolutional Neural Networks (CNN) based classifier for the experiments.
Datasets with EEG signals typically have fewer examples and higher time
resolution. Even though batch-normalization is preferred for Convolutional
Neural Networks (CNN), we empirically show that layer-normalization can improve
the adaptability of CNN-based EEG classifiers with not more than ten
fine-tuning steps. In summary, the present work (i) proposes a simple strategy
to evaluate fast adaptability, and (ii) empirically demonstrate fast
adaptability across individuals as well as across tasks with simple transfer
learning as compared to MAML approach.
