Machine Learning in Analysing Invasively Recorded Neuronal Signals: Available Open Access Data Sources

Abstract

Neuronal signals allow us to understand how the brain operates and this process requires sophisticated processing of the acquired signals, which is facilitated by machine learning-based methods. However, these methods require large amount of data to first train them on the patterns present in the signals and then employ them to identify patterns from unknown signals. This data acquisition process involves expensive and complex experimental setups which are often not available to all – especially to the computational researchers who mainly deal with the development of the methods. Therefore, there is a basic need for the availability of open access datasets which can be used as benchmark towards novel methodological development and performance comparison across different methods. This would facilitate newcomers in the field to experiment and develop novel methods and achieve more robust results through data aggregation. In this scenario, this paper presents a curated list of available open access datasets of invasive neuronal signals containing a total of more than 25 datasets.