Interpretable Model for Artefact Detection in Local Field Potentials via Feature Extraction and Decision Trees

The process of recording local fields potentials can be influenced by many internal and external sources of electrical noise. To successfully use these recordings, noise must be removed, for which an automatic detection tool is needed to speed up the reviewing process. This research aims to develop an interpretable model, and among the many machine learning models, decision trees stand out due to the innate ability to allow insight into the classification criteria. As they require extracted features instead of the raw signal to achieve good performance, the adaptation of features was proposed originally intended for electroencephalography classification for the detection of artefacts in local fields potentials. Afterwards, they are grouped, and three different filtering feature selection algorithms are applied to obtain the most relevant ones and compare their choices. Classification accuracy of 88.1% is obtained with a single feature. Hence, it is concluded that interpretable models can be obtained with a performance similar to that of deep learning approaches.