Digital filter design for electrophysiological data – a practical approach

Widmann, Andreas; Schröger, Erich; Maess, Burkhard

doi:10.1016/j.jneumeth.2014.08.002

Cited by 517 publications

(461 citation statements)

References 13 publications

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“…Electrophysiological data were processed with the EEGLAB toolbox (Delorme and Makeig, 2004) for Matlab (MathWorks Inc., Natick, USA). After changing the sampling rate to 512 Hz (with Biosemi Decimator 86), the data was high-pass and low-pass filtered using Hamming-windowed sinc FIR filters with 0.3 Hz and 30 Hz cutoff frequencies, respectively (Widmann et al, 2015;Widmann and Schröger, 2012). Epochs starting at 100 ms before stimulus onset, and ending at 1300 ms after stimulus onset, were extracted, with baseline correction based on the whole epoch length.…”

Section: Methodsmentioning

confidence: 99%

The first independent study on the complex trial protocol version of the P300-based concealed information test: Corroboration of previous findings and highlights on vulnerabilities

Lukács

Weiss

Dalos

et al. 2016

International Journal of Psychophysiology

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More than a dozen studies of the Complex Trial Protocol (CTP) version of the P300-based Concealed Information Test have been published since its introduction (Rosenfeld et al., 2008), and it has been fairly consistently proven to provide high accuracy and strong resistance to countermeasures . However, no independent authors have verified these findings until now. In the present, first independent study, we corroborate the accuracy and countermeasure-resistance of the CTP, when the probe item (critical presented information, e.g., crime detail; P) vs. all irrelevant items (Iall) comparison is used for classifying participants as guilty or innocent, but we also show that the CTP is severely vulnerable to countermeasures, when the P vs. the irrelevant item with the largest P300 responses (Imax) comparison is used.This latter measure can be defeated by creating "oddball" items among the irrelevant items (through targeting them with covert responses), and thereby making their P300 responses statistically indistinguishable from those of the probe item. Practical implications are discussed.

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Section: Methodsmentioning

confidence: 99%

The first independent study on the complex trial protocol version of the P300-based concealed information test: Corroboration of previous findings and highlights on vulnerabilities

Lukács

Weiss

Dalos

et al. 2016

International Journal of Psychophysiology

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show abstract

“…Temporal bandpass filtering was also applied, set at 2-120 Hz for EEG data and 2-30 Hz for the motion sensors. The choice of a highpass cutoff of 2 Hz was motivated by preliminary tests showing that increasing the cutoff frequency improved motion artifact estimation, likely due to reduced biases from slow-drift contributions; this compromise comes at the cost of excluding EEG information from part of the delta band, but did not affect VEP morphology (Widmann et al, 2015). The choice of a lowpass cutoff of 30 Hz for the motion sensors was again based on insights from part I, pointing that this is the relevant frequency band for motion contributions.…”

Section: Part Ii: Optimization Of Motion Artifact Correctionmentioning

confidence: 99%

Towards high-quality simultaneous EEG-fMRI at 7 T: Detection and reduction of EEG artifacts due to head motion

et al. 2015

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a b s t r a c t a r t i c l e i n f oThe enhanced functional sensitivity offered by ultra-high field imaging may significantly benefit simultaneous EEG-fMRI studies, but the concurrent increases in artifact contamination can strongly compromise EEG data quality. In the present study, we focus on EEG artifacts created by head motion in the static B 0 field. A novel approach for motion artifact detection is proposed, based on a simple modification of a commercial EEG cap, in which four electrodes are non-permanently adapted to record only magnetic induction effects. Simultaneous EEG-fMRI data were acquired with this setup, at 7 T, from healthy volunteers undergoing a reversingcheckerboard visual stimulation paradigm. Data analysis assisted by the motion sensors revealed that, after gradient artifact correction, EEG signal variance was largely dominated by pulse artifacts (81-93%), but contributions from spontaneous motion (4-13%) were still comparable to or even larger than those of actual neuronal activity (3-9%). Multiple approaches were tested to determine the most effective procedure for denoising EEG data incorporating motion sensor information. Optimal results were obtained by applying an initial pulse artifact correction step (AAS-based), followed by motion artifact correction (based on the motion sensors) and ICA denoising. On average, motion artifact correction (after AAS) yielded a 61% reduction in signal power and a 62% increase in VEP trial-by-trial consistency. Combined with ICA, these improvements rose to a 74% power reduction and an 86% increase in trial consistency. Overall, the improvements achieved were well appreciable at single-subject and single-trial levels, and set an encouraging quality mark for simultaneous EEG-fMRI at ultra-high field.

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“…The frequency response of Butterworth filters has no ripple in the passband and is extremely flat. The minimum order of the filter is applied to yield a precise and significantly effective design [127,128]. The minimum order and cutoff frequencies are defined as (1) and (2) respectively.…”

Section: Pre-processingmentioning

confidence: 99%

Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation

Aboalayon

Faezipour

Almuhammadi

et al. 2016

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Sleep specialists often conduct manual sleep stage scoring by visually inspecting the patient's neurophysiological signals collected at sleep labs. This is, generally, a very difficult, tedious and time-consuming task. The limitations of manual sleep stage scoring have escalated the demand for developing Automatic Sleep Stage Classification (ASSC) systems. Sleep stage classification refers to identifying the various stages of sleep and is a critical step in an effort to assist physicians in the diagnosis and treatment of related sleep disorders. The aim of this paper is to survey the progress and challenges in various existing Electroencephalogram (EEG) signal-based methods used for sleep stage identification at each phase; including pre-processing, feature extraction and classification; in an attempt to find the research gaps and possibly introduce a reasonable solution. Many of the prior and current related studies use multiple EEG channels, and are based on 30 s or 20 s epoch lengths which affect the feasibility and speed of ASSC for real-time applications. Thus, in this paper, we also present a novel and efficient technique that can be implemented in an embedded hardware device to identify sleep stages using new statistical features applied to 10 s epochs of single-channel EEG signals. In this study, the PhysioNet Sleep European Data Format (EDF) Database was used. The proposed methodology achieves an average classification sensitivity, specificity and accuracy of 89.06%, 98.61% and 93.13%, respectively, when the decision tree classifier is applied. Finally, our new method is compared with those in recently published studies, which reiterates the high classification accuracy performance.

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Digital filter design for electrophysiological data – a practical approach

Abstract: We present strategies for recognizing common adverse filter effects and filter artifacts and demonstrate them in practical examples. Best practices and recommendations for the selection and reporting of filter parameters, limitations, and alternatives to filtering are discussed.

Cited by 517 publications

References 13 publications

The first independent study on the complex trial protocol version of the P300-based concealed information test: Corroboration of previous findings and highlights on vulnerabilities

The first independent study on the complex trial protocol version of the P300-based concealed information test: Corroboration of previous findings and highlights on vulnerabilities

Towards high-quality simultaneous EEG-fMRI at 7 T: Detection and reduction of EEG artifacts due to head motion

Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation

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