Analytic beamformer transformation for transfer learning in motion-onset visual evoked potential decoding.

Objective. While decoders of electroencephalography-based event-related potentials (ERPs) are routinely tailored to the individual user to maximize performance, developing them on populations for individual usage has proven much more challenging. We propose the analytic beamformer transformation (ABT) to extract phase and/or magnitude information from spatiotemporal ERPs in response to motion-onset stimulation. Approach. We have tested ABT on 52 motion-onset visual evoked potential (mVEP) datasets from 26 healthy subjects and compared the classification accuracy of support vector machine (SVM), spatiotemporal beamformer (stBF) and stepwise linear discriminant analysis (SWLDA) when trained on individual subjects and on a population thereof. Main results. When using phase- and combined phase/magnitude information extracted by ABT, we show significant improvements in accuracy of population-trained classifiers applied to individual users ( p < 0.001). We also show that 450 epochs are needed for a correct functioning of ABT, which corresponds to 2 min of paradigm stimulation. Significance. We have shown that ABT can be used to create population-trained mVEP classifiers using a limited number of epochs. We expect this to pertain to other ERPs or synchronous stimulation paradigms, allowing for a more effective, population-based training of visual BCIs. Finally, as ABT renders recordings across subjects more structurally invariant, it could be used for transfer learning purposes in view of plug-and-play BCI applications.