Physiologic artifacts originate from the patient and non-physiologic artifacts originate from the environment of the patient. This chapter defines and gives examples of physiologic artifacts such as EKG, pulse, pacemaker, eye movements, myogenic, shivering, sniffling, hiccupping, glossokinetic and sway...
ICA is a standard and widely used blind source separation method for removing artifacts from EEG signals36. For our dataset, ICA processing was performed only on the EEG channels, using the MNE implementation of the extended infomax ICA. Due to the wide frequency range of our data, we set ...
Power line noise interference at 50 Hz and harmonics was suppressed using a comb filter and eye-blink artifacts were removed using wavelet enhanced independent component analysis (wICA)78, selecting components through visual inspection. Unlike the conventional ICA algorithm, which completely zeroes out ...
Supplementary Figure 3.2. Identification of artifacts n = 32. Supplementary Figure 3.3. Identification of abnormalities n =32. Supplementary Figure 3.4. Identification of activation procedures n = 32. Additional file 5: Supplementary Figure 4. Recommendation of handbook and training program n = 32....
For instance, the large and transient artifacts can be eliminated by removing the bad trials that contain them. Furthermore, additional electrodes can be used to record ocular and movement artifacts. The information recorded by these electrodes can be used to separate the artifacts from the brain ...
Clean EEG data and artifacts Before you jump into data collection and analysis, there’s one thing you should make your mantra: There is no substitute for clean data (you might remember this sentence from the beginning of this chapter). Always make sure your data is as clean as possible, ...
The book focuses on digital recording and analyses based on digital data with an emphasis on pattern recognition, artifacts recognition, technical pitfalls and the clinical correlates of EEG. The first part of the book explains the technical aspects of electroneurodiagnosis, including basic electronics...
Also, artifacts were successfully removed from the EEG without impairing classification performance. Finally, reuse of the classifier causes only a small loss in classification performance. Conclusions: Our data provide first evidence that EEG can be automatically classified on single-trial basis in CI ...
After the exclusion of the artifacts that were included in the data due to eye movements or other factors, the raw EEG data were filtered applying a 30 Hz low pass filter and divided into epochs. Each epoch started 100 ms before the stimulus onset and extended for 600 ms after the ...
New feature for removing heart artifacts from EEG or ESG data using a… Jan 20, 2025 .github [pre-commit.ci] pre-commit autoupdate (#13073) Jan 22, 2025 doc Fix dev installation guide (#13163) Mar 23, 2025 examples BUG: Fix bug with movecomp and t=0 (#13132) Feb 27, 2025 ...