Optimizing fMRI experimental design for MVPA-based BCI control: Combining the strengths of block and event-related designs.

Functional Magnetic Resonance Imaging (fMRI) has been successfully used for Brain Computer Interfacing (BCI) to classify (imagined) movements of different limbs. However, reliable classification of more subtle signals originating from co-localized neural networks in the sensorimotor cortex, e.g. individual movements of fingers of the same hand, has proved to be more challenging, especially when taking into account the requirement for high single trial reliability in the BCI context. In recent years, Multi Voxel Pattern Analysis (MVPA) has gained momentum as a suitable method to disclose such weak, distributed activation patterns. Much attention has been devoted to developing and validating data analysis strategies, but relatively little guidance is available on the choice of experimental design, even less so in the context of BCI-MVPA. When applicable, block designs are considered the safest choice, but the expectations, strategies and adaptation induced by blocking of similar trials can make it a sub-optimal strategy. Fast event-related designs, in contrast, require a more complicated analysis and show stronger dependence on linearity assumptions but allow for randomly alternating trials. However, they lack resting intervals that enable the BCI participant to process feedback. In this proof-of-concept paper a hybrid blocked fast-event related design is introduced that is novel in the context of MVPA and BCI experiments, and that might overcome these issues by combining the rest periods of the block design with the shorter and randomly alternating trial characteristics of a rapid event-related design. A well-established button-press experiment was used to perform a within-subject comparison of the proposed design with a block and a slow event-related design. The proposed hybrid blocked fast-event related design showed a decoding accuracy that was close to that of the block design, which showed highest accuracy. It allowed for across-design decoding, i.e. reliable prediction of examples obtained with another design. Finally, it also showed the most stable incremental decoding results, obtaining good performance with relatively few blocks. Our findings suggest that the blocked fast event-related design could be a viable alternative to block designs in the context of BCI-MVPA, when expectations, strategies and adaptation make blocking of trials of the same type a sub-optimal strategy. Additionally, the blocked fast event-related design is also suitable for applications in which fast incremental decoding is desired, and enables the use of a slow or block design during the test phase.