Adaptive Waveform Learning: A Framework for Modeling Variability in Neurophysiological Signals

Abstract : When analyzing brain activity such as local field potentials (LFP), it is often desired to represent neural events by stereotypic waveforms. Due to the non-deterministic nature of the neural responses, an adequate waveform estimate typically requires to record multiple repetitions of the neural events. It is common practice to segment the recorded signal into event-related epochs and calculate their average. This approach suffers from two major drawbacks: (i) epoching can be problematic, especially in the case of overlapping neural events and (ii) variability of the neural events across epochs (such as varying onset latencies) is not accounted for, which may lead to a distorted average. In this paper, we propose a novel method called adaptive waveform learning (AWL). It is designed to learn multi-component representations of neural events while explicitly capturing and compensating for waveform variability, such as changing latencies or more general shape variations. Thanks to its generality, it can be applied to both epoched (i.e., segmented) and continuous (i.e., non-epoched) signals by making the corresponding specializations to the algorithm. We evaluate AWL's performance and robustness to noise on simulated data and demonstrate its empirical utility on an electrophysiological recording containing intracranial epileptiform discharges (epileptic spikes).
Type de document :
Article dans une revue
IEEE Transactions on Signal Processing, Institute of Electrical and Electronics Engineers, 2017, 65, pp.4324 - 4338. 〈10.1109/TSP.2017.2698415〉
Liste complète des métadonnées

Littérature citée [23 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-01548428
Contributeur : Maureen Clerc <>
Soumis le : mardi 27 juin 2017 - 15:40:45
Dernière modification le : jeudi 18 janvier 2018 - 02:01:35
Document(s) archivé(s) le : mercredi 17 janvier 2018 - 22:31:58

Fichier

AdaptiveWaveformLearning_autho...
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

Citation

Sebastian Hitziger, Maureen Clerc, Sandrine Saillet, Christian Bénar, Théodore Papadopoulo. Adaptive Waveform Learning: A Framework for Modeling Variability in Neurophysiological Signals. IEEE Transactions on Signal Processing, Institute of Electrical and Electronics Engineers, 2017, 65, pp.4324 - 4338. 〈10.1109/TSP.2017.2698415〉. 〈hal-01548428〉

Partager

Métriques

Consultations de la notice

247

Téléchargements de fichiers

61