  Brain Computer Interface technologies are popular methods of communication
between the human brain and external devices. One of the most popular
approaches to BCI is Motor Imagery. In BCI applications, the
ElectroEncephaloGraphy is a very popular measurement for brain dynamics because
of its non-invasive nature. Although there is a high interest in the BCI topic,
the performance of existing systems is still far from ideal, due to the
difficulty of performing pattern recognition tasks in EEG signals. BCI systems
are composed of a wide range of components that perform signal pre-processing,
feature extraction and decision making. In this paper, we define a BCI
Framework, named Enhanced Fusion Framework, where we propose three different
ideas to improve the existing MI-based BCI frameworks. Firstly, we include aan
additional pre-processing step of the signal: a differentiation of the EEG
signal that makes it time-invariant. Secondly, we add an additional frequency
band as feature for the system and we show its effect on the performance of the
system. Finally, we make a profound study of how to make the final decision in
the system. We propose the usage of both up to six types of different
classifiers and a wide range of aggregation functions (including classical
aggregations, Choquet and Sugeno integrals and their extensions and overlap
functions) to fuse the information given by the considered classifiers. We have
tested this new system on a dataset of 20 volunteers performing motor
imagery-based brain-computer interface experiments. On this dataset, the new
system achieved a 88.80% of accuracy. We also propose an optimized version of
our system that is able to obtain up to 90,76%. Furthermore, we find that the
pair Choquet/Sugeno integrals and overlap functions are the ones providing the
best results.
