Video-EEG and PerceptTM PC Deep Brain Neurostimulator Fine-Grained Synchronization for Multimodal Neurodata Analysis

• Type: Article in International Conference Proceedings Book
• Date: 2021

Authors

Lopes, EM (Author) Other; Vilas Boas, MD (Author) Other; Rego, R (Author) Other; Santos, A (Author) Other; Cunha, JPS (Author) FEUP

Conference proceedings International

Title: 2021 10TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING (NER)

Pages: 963-966

10th International IEEE-EMBS Conference on Neural Engineering (NER)

Prague, ELECTR NETWORK, MAY 04-06, 2021

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Authenticus ID: P-00V-1V7

DOI: 10.1109/ner49283.2021.9441422

Abstract (EN): Adaptive Deep Brain Stimulation has recently emerged to tackle conventional DBS limitations by measuring disease fluctuations and to adapt stimulation parameter accordingly. In early 2020, Medtronic launched in the European Union the first certified DBS neurostimulator capable of simultaneously stimulate and read signals from the deep brain structures, the PerceptTMPC. In epilepsy, the most common target brain structure is the Anterior Nucleus of Thalamus and the Local Field Potentials analysis requires prior synchronization of data recorded from the Percept PC with video-Electroencephalography (vEEG) equipment. Fine-grained synchronization (sub-second resolution) is mandatory for multimodal neurodata analysis and may be achieved by aligning artefacts perceived in both systems. In this work we study two methods aiming for neurodata streams clock synchronization: one based on DBS stimulation artefacts and another on tapping maneuver artefacts. For this purpose, we studied the data collected from the first epileptic patient that underwent 1-week vEEG-PerceptTMPC monitoring at a Hospital monitoring unit. We found that tapping maneuver-based methodology allowed a more accurate synchronization in relation to the stimulation artefact-based method (0.56s vs. 2.07s absolute average uncertainty). This method was also more complete one since tapping timestamps can be determined by video timeframes and do not require a prior identification of artefacts in EEG data by clinicians.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 4