Stimulation artifact correction method for estimation of early cortico-cortical evoked potentials

Trébaul, L; Rudrauf, David; Job, Anne‐Sophie; Mălîia, Mihai Dragoş; Popa, Irina; Barborică, Andrei; Minotti, Lorella; Mı̂ndruță, Ioana; Kahane, Philippe; David, Olivier

doi:10.1016/j.jneumeth.2016.03.002

Cited by 39 publications

(33 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…First introduced as an offline method [48,53,54], template subtraction has been implemented in both online hardware [55 ] and software [37,56] (Figure 3f). Templates may be formed from averaging artifacts [37,48,53,54,55 ,56-58] or fitting artifacts to a predefined function type [9,53,59]. These subtraction techniques suffer from varying artifact morphology resulting from undersampling the artifact shape and misalignment of stimulation and sample timing [49 ,57].…”

Section: Back-end Neural Signal Recoverymentioning

confidence: 99%

See 1 more Smart Citation

Toward true closed-loop neuromodulation: artifact-free recording during stimulation

Zhou

Johnson

Muller

2018

Current Opinion in Neurobiology

View full text Add to dashboard Cite

Closed-loop and responsive neuromodulation systems improve open-loop neurostimulation by responding directly to measured neural activity and providing adaptive, on-demand therapy. To be effective, these systems must be able to simultaneously record and stimulate neural activity, a task made difficult by persistent stimulation artifacts that distort and obscure underlying biomarkers. To enable simultaneous stimulation and recording, several techniques have been proposed. These techniques involve artifact-preventing system configurations, resilient recording front-ends, and back-end signal processing for removing recorded artifacts. Co-designing and integrating these artifact cancellation techniques will be key to enabling neuromodulation systems to stimulate and record at the same time. Here, we review the state-of-the-art for these techniques and their role in achieving artifact-free neuromodulation.

show abstract

Section: Back-end Neural Signal Recoverymentioning

confidence: 99%

“…Averaged template subtraction (offine) [48,53,54] Averaged template resampling and subtraction (offline) [57,58] Function fitting template subtraction (offline) [9,53,59] Adaptive filter (online) [38 ]…”

Section: Stimulation Waveform Designmentioning

confidence: 99%

Toward true closed-loop neuromodulation: artifact-free recording during stimulation

Zhou

Johnson

Muller

2018

Current Opinion in Neurobiology

View full text Add to dashboard Cite

show abstract

“…Stimulation artifact has been shown to last between 1 and 6 msec in SEEG, with the time being slightly longer in ECoG (5‐10 msec) . Many studies choose an alternating monophasic stimulation waveform to cancel the stimulation artifact, the advantage of this being that it is simple and effective.…”

Section: Postprocessing Considerationsmentioning

confidence: 99%

“…Interpolation of the time around stimulation (first 8 msec) using a cubic spline has been implemented with success . Stimulation artifact has been modeled using an electric circuit approach with promising results; however, this assumes uniformity across all recording sites.…”

Section: Postprocessing Considerationsmentioning

confidence: 99%

Considerations in performing and analyzing the responses of cortico‐cortical evoked potentials in stereo‐EEG

2017

View full text Add to dashboard Cite

SummaryThis review aims to highlight key considerations when performing cortico-cortical evoked potentials (CCEPs) using stereo-electroencephalography (SEEG) for network mapping and show its clinical applicability to presurgical evaluations. The parameters for performing stimulation and safety aspects have been investigated in electrocorticography (ECoG) and deep brain stimulation (DBS), but not as extensively in SEEG. A review of current literature was performed, with an attempt made to emphasize practical insights from all modalities of intracranial stimulation. This paper reviews physical stimulation parameters, highlights safety limits, and considers the influence of changing common stimulation parameters.These factors are put into the context of CCEPs in SEEG. Given the paucity of direct research in this area, studies utilizing low frequency stimulation, DBS, and ECoG are incorporated along with the fundamental principles of electrical engineering. In addition, postprocessing considerations are reviewed, including electrode localization, application of digital filters, baseline selection, application of connectivity metrics, and higher order network analysis. The aim is to guide CCEP stimulation as well as to provide an understanding of the underlying principles of this technique. At present, there are few articles detailing the design of low-frequency stimulation paradigms, especially in the setting of SEEG. Providing a review of the fundamentals and postprocessing considerations when performing CCEPs in SEEG will increase the accessibility of this technique. K E Y W O R D Sbrain mapping, CCEP, connectivity, electrical stimulation, review, SEEG

show abstract

“…That is, nearly all approaches assume that the electrical stimulation currents are unknown, which in most instances is not the case since they are being delivered by the experimenter via a computer interface. These include artifact template subtraction [5][6][7][8], local curve fitting [9], sample-andinterpolate technique [10], and independent component analysis [11][12][13][14][15][16]. Such waveform centric algorithms can suppress artifacts in certain stimulation/recording paradigms, yet they often rely on artifact estimation and subtraction techniques that place assumptions on the statistics of the neural signals and artifacts and can themselves distort the target neural signals.…”

Section: Introductionmentioning

confidence: 99%

Optimal Multichannel Artifact Prediction and Removal for Brain Machine Interfaces and Neural Prosthetics

Chen

Dutta

et al. 2019

Preprint

View full text Add to dashboard Cite

Neural implants that electrically stimulate neural tissue such as deep brain stimulators, cochlear implants (CI), and vagal nerve stimulators are becoming the routine treatment options for various diseases. Optimizing electrical stimulation paradigms requires closed-loop stimulation using simultaneous recordings of evoked neural activity in real time. Stimulus-evoked artifacts at the recording site are generally orders of magnitude larger than the neural signals, which challenge the interpretation of evoked neural activity. We developed a generalized artifact removal algorithm that can be applied in a variety of neural recording modalities. The procedure leverages known electrical stimulation currents to derive optimal filters that are used to predict and remove artifacts. We validated the procedure using paired recordings and electrical stimulation from sciatic nerve axons, high-rate bilateral CI stimulation, and concurrent multichannel stimulation in auditory midbrain and recordings in auditory cortex. We demonstrate a vast enhancement in the quality of recording even for high-throughput multi-site stimulation with typical improvements in the signal-to-noise ratio between 20-40 dB. The algorithm is efficient, can be scaled to arbitrary number of sites, and is applicable in range of recording modalities. It has numerous benefits over existing approaches and thus should be valuable for emerging neural recording and stimulation technologies.

show abstract

Stimulation artifact correction method for estimation of early cortico-cortical evoked potentials

Cited by 39 publications

References 28 publications

Toward true closed-loop neuromodulation: artifact-free recording during stimulation

Toward true closed-loop neuromodulation: artifact-free recording during stimulation

Considerations in performing and analyzing the responses of cortico‐cortical evoked potentials in stereo‐EEG

Optimal Multichannel Artifact Prediction and Removal for Brain Machine Interfaces and Neural Prosthetics

Contact Info

Product

Resources

About