Channel-independent recreation of artefactual signals in chronically recorded local field potentials using machine learning.

ABSTRACT: Acquisition of neuronal signals involves a wide range of devices with specific electrical properties. Combined with other physiological sources within the body, the signals sensed by the devices are often distorted. Sometimes these distortions are visually identifiable, other times, they overlay with the signal characteristics making them very difficult to detect. To remove these distortions, the recordings are visually inspected and manually processed. However, this manual annotation process is time-consuming and automatic computational methods are needed to identify and remove these artefacts. Most of the existing artefact removal approaches rely on additional information from other recorded channels and fail when global artefacts are present or the affected channels constitute the major

SUBMITTER: Fabietti M 

PROVIDER: S-EPMC8741911 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature