Stimulation artefact removal: review and evaluation of applications in evoked responses.

This study investigated software methods for removing stimulation artefacts in recordings undertaken during deep brain stimulation (DBS). We aimed to evaluate artefact attenuation using sample recordings of evoked resonant neural activity (ERNA), as well as a synthetic ground-truth waveform that emulated observed ERNA characteristics.The synthetic waveform and eight raw DBS recordings were processed by fourteen algorithms spanning the following categories: signal modification, signal decomposition, and template subtraction. For the synthetic waveform, performance was quantified by comparing each reconstructed signal against the ground-truth waveform. For DBS recordings, performance was contrasted amongst each other. The stimulation artefact was quantified by its amplitude and subsequent decay to baseline by the time to first zero-crossing. Each reconstructed ERNA signal was characterised by peak-to-peak-amplitude, root-mean-square amplitude, latency, and number of zero-crossings.None of the methods performed overall as well as the Backward Filter. Signal decomposition techniques were able to attenuate stimulation artefact albeit with unacceptable ERNA distortion.Upon evaluation of common software methods for DBS artefact attenuation, we advocate the use of the Backward Filter for reducing such artefacts while reconstructing ERNA.