Pitfalls of high-pass filtering for detecting epileptic oscillations: a technical note on “false” ripples

CG Bénar, L Chauvière, F Bartolomei… - Clinical …, 2010 - Elsevier
OBJECTIVES: To analyze interictal High frequency oscillations (HFOs) as observed in the
medial temporal lobe of epileptic patients and animals (ripples, 80–200Hz and fast ripples …

Resonance-based signal decomposition: A new sparsity-enabled signal analysis method

IW Selesnick - Signal Processing, 2011 - Elsevier
Numerous signals arising from physiological and physical processes, in addition to being
non-stationary, are moreover a mixture of sustained oscillations and non-oscillatory …

Increased intra-participant variability in children with autistic spectrum disorders: evidence from single-trial analysis of evoked EEG

E Milne - Frontiers in psychology, 2011 - frontiersin.org
Intra-participant variability in clinical conditions such as autistic spectrum disorder (ASD) is
an important indicator of pathophysiological processing. The data reported here illustrate …

[BOOK][B] Practical biomedical signal analysis using MATLAB®

KJ Blinowska, J Żygierewicz - 2021 - taylorfrancis.com
Covering the latest cutting-edge techniques in biomedical signal processing while
presenting a coherent treatment of various signal processing methods and applications, this …

Compressive sensing scalp EEG signals: implementations and practical performance

AM Abdulghani, AJ Casson… - Medical & biological …, 2012 - Springer
Highly miniaturised, wearable computing and communication systems allow unobtrusive,
convenient and long term monitoring of a range of physiological parameters. For long term …

Algorithms for estimating time-locked neural response components in cortical processing of continuous speech

JP Kulasingham, JZ Simon - IEEE Transactions on Biomedical …, 2022 - ieeexplore.ieee.org
Objective: The Temporal Response Function (TRF) is a linear model of neural activity time-
locked to continuous stimuli, including continuous speech. TRFs based on speech …

A comparison of methods for separation of transient and oscillatory signals in EEG

N Jmail, M Gavaret, F Wendling, A Kachouri… - Journal of neuroscience …, 2011 - Elsevier
Brain oscillations constitute a prominent feature of electroencephalography (EEG), in both
physiological and pathological states. An efficient separation of oscillation from transient …

Multivariate temporal dictionary learning for EEG

Q Barthélemy, C Gouy-Pailler, Y Isaac… - Journal of neuroscience …, 2013 - Elsevier
This article addresses the issue of representing electroencephalographic (EEG) signals in
an efficient way. While classical approaches use a fixed Gabor dictionary to analyze EEG …

Application of modern tests for stationarity to single-trial MEG data: transferring powerful statistical tools from econometrics to neuroscience

L Kipiński, R König, C Sielużycki, W Kordecki - Biological cybernetics, 2011 - Springer
Stationarity is a crucial yet rarely questioned assumption in the analysis of time series of
magneto-(MEG) or electroencephalography (EEG). One key drawback of the commonly …

Consensus matching pursuit for multi-trial EEG signals

CG Bénar, T Papadopoulo, B Torrésani… - Journal of neuroscience …, 2009 - Elsevier
Time–frequency representations are commonly used to analyze the oscillatory nature of
brain signals in EEG, MEG or intracranial EEG. In the signal processing literature, there is …