Generalized framework for stimulus artifact removal

Abstract

Stimulation is extensively used in neuroscience research in diverse fields ranging from cognitive to clinical. Studying the effect of electrical and magnetic stimulation on neuronal activity is complicated by large stimulation-derived artifacts on the recording electrodes, which mask the spiking activity. Multiple studies have suggested a variety of solutions for the removal of artifacts and were typically directed at specific stimulation setups. In this study we introduce a generalized framework for stimulus artifacts removal, the Stimulus Artifact Removal Graphical Environment (SARGE). The framework provides an encapsulated environment for a multi-stage removal process, starting from the stimulus pulse detection, through estimation of the artifacts and their removal, and finally to signal reconstruction and the assessment of removal quality. The framework provides the user with subjective graphical and objective quantitative tools for assessing the resulting signal, and the ability to adjust the process to optimize the results. This extendable publicly available framework supports different types of stimulation, stimulation patterns and shapes, and a variety of artifact estimation methods. We exemplify the removal of artifacts generated by electrical micro- and macro-stimulation and magnetic stimulation and different stimulation protocols. The use of different estimation methods, such as averaging and function fitting is demonstrated, and the differences between them are discussed. Finally, the quality of removal is assessed and validated using quantitative measures and combined experimental-simulation studies. The framework marks a shift from “algorithm” and “data” centric approach to a “workflow” centric approach, thus introducing an innovative concept to the artifact removal process.