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

Abstract

Brain-Computer Interfaces permit neural activityto be directly interpreted and used for applications, liketherapeutic replacement of lost function (e.g. stroke) or tosupplement existing function (e.g. handsfree applications). Twomajor challenges for BCI are accurate interpretation of neuralactivity and signal processing speed for real-time applicationsi.e. correctly decode a user’s intent and the timely execution ofthat intent. Magnetoencephalography has advantages overElectroencephalography with respect to spatial and temporalresolution which could potentially allow better decoding ofbrain activity. High spatial and temporal resolution using MEGgenerates a large volume of data which must be rapidlypreprocessed and classified correctly for practical real-timeBCI. This paper presents a simple data processing technique toclean, normalise and reduce data dimensionality, for optimalclass label decoding using a simple Logistic Regressionclassifier. Good decoding performance was achieved using anoff-line MEG dataset, with or without data dimensionalityreduction, comparable to more complex data pre-processingmethods and classifiers already studied