Data analysis methods for neuroimaging data pre-processing to decode cognitive tasks using logistic regression for BCI applications

Brain-Computer Interfaces permit neural activity
to be directly interpreted and used for applications, like
therapeutic replacement of lost function (e.g. stroke) or to
supplement existing function (e.g. handsfree applications). Two
major challenges for BCI are accurate interpretation of neural
activity and signal processing speed for real-time applications
i.e. correctly decode a user’s intent and the timely execution of
that intent. Magnetoencephalography has advantages over
Electroencephalography with respect to spatial and temporal
resolution which could potentially allow better decoding of
brain activity. High spatial and temporal resolution using MEG
generates a large volume of data which must be rapidly
preprocessed and classified correctly for practical real-time
BCI. This paper presents a simple data processing technique to
clean, normalise and reduce data dimensionality, for optimal
class label decoding using a simple Logistic Regression
classifier. Good decoding performance was achieved using an
off-line MEG dataset, with or without data dimensionality
reduction, comparable to more complex data pre-processing
methods and classifiers already studied.