scholarly journals Review of Artifact Rejection Methods for Electroencephalographic Systems

10.5772/68023 ◽  
2017 ◽  
Author(s):  
Suguru Kanoga ◽  
Yasue Mitsukura
Keyword(s):  
Artifact Rejection

The Effect of Kalman-Weighted Averaging and Artifact Rejection on Residual Noise During Auditory Brainstem Response Testing

2019 ◽  
Vol 28 (1) ◽  
pp. 114-124
Author(s):  
Linda W. Norrix ◽  
Julie Thein ◽  
David Velenovsky
Keyword(s):  
Repeated Measures ◽  
Auditory Brainstem ◽  
Weighted Averaging ◽  
Auditory Brainstem Responses ◽  
Residual Noise ◽  
Electrophysiological Recordings ◽  
Artifact Rejection ◽  
Brainstem Response ◽  
Active Motor ◽  
Averaging Time

Purpose Low residual noise (RN) levels are critically important when obtaining electrophysiological recordings of threshold auditory brainstem responses. In this study, we examine the effectiveness and efficiency of Kalman-weighted averaging (KWA) implemented on the Vivosonic Integrity System and artifact rejection (AR) implemented on the Intelligent Hearing Systems SmartEP system for obtaining low RN levels. Method Sixteen adults participated. Electrophysiological measures were obtained using simultaneous recordings by the Vivosonic and Intelligent Hearing Systems for subjects in 2 relaxed conditions and 4 active motor conditions. Three averaging times were used for the relaxed states (1, 1.5, and 3 min) and for the active states (1.5, 3, and 6 min). Repeated-measures analyses of variance were used to examine RN levels as a function of noise reduction strategy (i.e., KWA, AR) and averaging time. Results Lower RN levels were obtained using KWA than AR in both the relaxed and active motor states. Thus, KWA was more effective than was AR under the conditions examined in this study. Using KWA, approximately 3 min of averaging was needed in the relaxed condition to obtain an average RN level of 0.025 μV. In contrast, in the active motor conditions, approximately 6 min of averaging was required using KWA. Mean RN levels of 0.025 μV were not attained using AR. Conclusions When patients are not physiologically quiet, low RN levels are more likely to be obtained and more efficiently obtained using KWA than AR. However, even when using KWA, in active motor states, 6 min of averaging or more may be required to obtain threshold responses. Averaging time needed and whether a low RN level can be attained will depend on the level of motor activity exhibited by the patient.

Intraoperative monitoring of olfactory function: a feasibility study

2020 ◽  
Vol 132 (5) ◽  
pp. 1659-1664 ◽  
Author(s):  
Shahan Momjian ◽  
Rémi Tyrand ◽  
Basile N. Landis ◽  
Colette Boëx
Keyword(s):  
Evoked Potentials ◽  
General Anesthesia ◽  
Intraoperative Neuromonitoring ◽  
Olfactory Function ◽  
Chemical Senses ◽  
Cerebral Lesions ◽  
Artifact Rejection ◽  
Rejection Criterion ◽  
Olfactory Stimuli ◽  
Anterior Fossa

OBJECTIVEIntraoperative neuromonitoring of the chemical senses (smell and taste) has never been performed. The objective of this study was to determine if olfactory-evoked potentials could be obtained intraoperatively under general anesthesia.METHODSA standard olfactometer was used in the surgical theater with hydrogen sulfide (4 ppm, 200 msec). Olfactory-evoked potentials were recorded in 8 patients who underwent neurosurgery for resection of cerebral lesions. These patients underwent routine target-controlled propofol and sufentanil general anesthesia. Frontal, temporal, and parietal scalp subdermal electrodes were recorded ipsilaterally and contralaterally at the site of the surgery. Evoked potentials were computed if at least 70 epochs (0.5–100 Hz) satisfying the artifact rejection criterion (threshold 45 μV) could be extracted from signals of electrodes.RESULTSContributive recordings were obtained for 5 of 8 patients (3 patients had fewer than 70 epochs with an amplitude < 45 μV). Olfactory-evoked potentials showed N1 responses (mean 442.8 ± 40.0 msec), most readily observed in the patient who underwent midline anterior fossa neurosurgery. No component of later latencies could be recorded consistently.CONCLUSIONSThe study confirms that olfactory-evoked potentials can be measured in response to olfactory stimuli under general anesthesia. This demonstrates the feasibility of recording olfactory function intraoperatively and opens the potential for neuromonitoring of olfactory function during neurosurgery.

Online Automatic Artifact Rejection using the Real-time EEG Source-mapping Toolbox (REST)

2018 ◽  
Author(s):  
Luca Pion-Tonachini ◽  
Sheng-Hsiou Hsu ◽  
Chi-Yuan Chang ◽  
Tzyy-Ping Jung ◽  
Scott Makeig
Keyword(s):  
Real Time ◽  
The Real ◽  
Artifact Rejection

scholarly journals Interval-coded texture features for artifact rejection in automated cervical cytology

Cytometry ◽  
1988 ◽  
Vol 9 (5) ◽  
pp. 418-425 ◽  
Author(s):  
James H. Tucker ◽  
Karsten Rodenacker ◽  
Uta Juetting ◽  
Peter Nickolls ◽  
Keith Watts ◽  
...  
Keyword(s):  
Texture Features ◽  
Cervical Cytology ◽  
Artifact Rejection

Automated artifact rejection using ICA and image processing algorithms

2017 ◽  
Author(s):  
R Christina Marion P Gilberet ◽  
Ria Susan Roy ◽  
N J Sairamya ◽  
D Narain Ponraj ◽  
S Thomas George
Keyword(s):  
Image Processing ◽  
Artifact Rejection ◽  
Image Processing Algorithms ◽  
Processing Algorithms

EEG Analysis

2017 ◽  
Author(s):  
Fabrice Wendling ◽  
Marco Congendo ◽  
Fernando H. Lopes da Silva
Keyword(s):  
Matching Pursuit ◽  
Effective Connectivity ◽  
Eeg Signals ◽  
Model Decomposition ◽  
Time Frequency ◽  
Frequency Domains ◽  
Stationary Signals ◽  
Artifact Rejection ◽  
Transient Events

This chapter addresses the analysis and quantification of electroencephalographic (EEG) and magnetoencephalographic (MEG) signals. Topics include characteristics of these signals and practical issues such as sampling, filtering, and artifact rejection. Basic concepts of analysis in time and frequency domains are presented, with attention to non-stationary signals focusing on time-frequency signal decomposition, analytic signal and Hilbert transform, wavelet transform, matching pursuit, blind source separation and independent component analysis, canonical correlation analysis, and empirical model decomposition. The behavior of these methods in denoising EEG signals is illustrated. Concepts of functional and effective connectivity are developed with emphasis on methods to estimate causality and phase and time delays using linear and nonlinear methods. Attention is given to Granger causality and methods inspired by this concept. A concrete example is provided to show how information processing methods can be combined in the detection and classification of transient events in EEG/MEG signals.

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