In the signal decoding tasks, we work with multidimensional time series, which are a discretization of a continuous process. The latest works in neural ODE illustrate the possibility to work with recurrent neural networks as with differential equations.
This work addresses such applications as change of sampling rate and handling missed or irregular data. It becomes possible if we represent our signal as a continuous in time function. This approach is relevant for signals from various wearable devices: accelerometers, heart rate monitors, devices for picking up brain signals such as electroencephalograms or electrocorticograms.
The main result of this work is a comparison of convolutional and differential approaches to solving P300-paradigm signal decoding task while having regular and irregular time series.
TBD