Signal Power Affects Artefact Detection Accuracy in Chronically Recorded Local Field Potentials: Preliminary Results

Abstract

Local fields potentials (LFP) can be contaminated by different internal and external sources of noise during their recordings. In cases where artefacts are present in them, automatic detection tools are needed to speed up the high accuracy detection process, followed by their removal to successfully use these recordings. This process is facilitated by a pool of supervised machine learning based tools which require labelled data for training. These algorithms have the capacity to distinguish between normal brain patterns and artefacts from an individual or a group, which is more flexible than template matching and subtraction. In addition, their portability has seen developments from both the software and hardware perspective. For many tools, LFP signal power is used as a gauge to measure and eventually label the artefacts portion of the LFP. This work explores how signal power affects the detection and classification accuracy of artefacts. Results show that a higher threshold value impacts positively the accuracy, due to less false positives in the data, without compromising the specificity in balanced datasets.