Master thesis on continuous time representation in signal decoding tasks.
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 an algorithm which allows us to work with a signal as if it was a continuous function. We also look at different applications of this algorithm and propose to do further research on expanding the continuity of time to the continuity of space.
The full text of the thesis can be found here
For now it's all in the notebook, soon the training and visualizing would be done through CLI (work in progress)