Improving Classification Performance by Combining Feature Vectors with a Boosting Approach for Brain Computer Interface (BCI)

Abstract

Brain-computer interfaces (BCI) are an interesting emerging technology providing an efficient communication system between human brain and external devices like computers or neuroprosthesis. Among assorts of neuroimaging techniques, electroencephalogram (EEG) is among one of the non-invasive methods exploited mostly in BCI studies. Recent studies have shown that Motor Imagery (MI) based BCI can be used as a rehabilitation tool for patients with severe neuromuscular disabilities. The spatial and spectral information related to brain activities associated with BCI paradigms are usually pre-determined as default in EEG analysis without speculation, which can lead to loses effects in practical applications due to individual variability across different subjects. Recent studies have shown that feature combination of each specifically tailored for different physiological phenomena such as Readiness Potential (RP) and Event Related Desynchronization (ERD) might benefit BCI making it robust against artifacts. Hence, the objective is to design a CSSBP with combined feature vectors, where the signal is divided into several sub bands using a band pass filter, and this channel and frequency configurations are then modeled as preconditions before learning base learners and introducing a new heuristic of stochastic gradient boost for training the base learners under these preconditions. The effectiveness and robustness of this algorithm along with feature combination is evaluated on two different data sets recorded from distinct populations. Results showed that Boosting approach with feature combination clearly outperformed the state-of-the-art algorithms, and improved the classification performance and resulted in increased robustness. This method can also be used to explore the neurophysiological mechanism of underlying brain activities.