Rating by detection: an artifact detection protocol for rating EEG quality with average event duration.

Objective. Quantitative evaluation protocols are critical for the development of algorithms that remove artifacts from real electroencephalography (EEG) optimally. However, visually inspecting the real EEG to select the top-performing artifact removal pipeline is infeasible while hand-crafted EEG data allow assessing artifact removal configurations only in a simulated environment. This study proposes a novel, principled approach for quantitatively evaluating algorithmically corrected EEG without access to ground truth in real-world conditions.Approach. Our offline evaluation protocol uses a detector to score the presence of artifacts. It computes their average duration, which measures the recovered EEG's deviation from the modeled background activity with a single score. As we expect the detector to make generalization errors, we employ a generic and configurable Wiener-based artifact removal method to validate the reliability of our detection protocol.Main results. Quantitative experiments extensively compare many Wiener filters and show their consistent rankings agree with their theoretical assumptions and expectations.Significance. The rating-by-detection protocol with the average event duration measure should be of value for EEG practitioners and developers. After removing artifacts from real EEG, the protocol experimentally shows that reliable comparisons between many artifact filtering configurations are possible despite the missing ground-truth neural signals.

Decoding working memory-related information from repeated psychophysiological EEG experiments using convolutional and contrastive neural networks.

Objective.Extracting reliable information from electroencephalogram (EEG) is difficult because the low signal-to-noise ratio and significant intersubject variability seriously hinder statistical analyses. However, recent advances in explainable machine learning open a new strategy to address this problem.Approach.The current study evaluates this approach using results from the classification and decoding of electrical brain activity associated with information retention. We designed four neural network models differing in architecture, training strategies, and input representation to classify single experimental trials of a working memory task.Main results.Our best models achieved an accuracy (ACC) of 65.29 ± 0.76 and Matthews correlation coefficient of 0.288 ± 0.018, outperforming the reference model trained on the same data. The highest correlation between classification score and behavioral performance was 0.36 (p= 0.0007). Using analysis of input perturbation, we estimated the importance of EEG channels and frequency bands in the task at hand. The set of essential features identified for each network varies. We identified a subset of features common to all models that identified brain regions and frequency bands consistent with current neurophysiological knowledge of the processes critical to attention and working memory. Finally, we proposed sanity checks to examine further the robustness of each model's set of features.Significance.Our results indicate that explainable deep learning is a powerful tool for decoding information from EEG signals. It is crucial to train and analyze a range of models to identify stable and reliable features. Our results highlight the need for explainable modeling as the model with the highest ACC appeared to use residual artifactual activity.

Recording of amplitude-integrated electroencephalography, oxygen saturation, pulse rate, and cerebral blood flow during massage of premature infants.

OBJECTIVE: Stimulation of the nervous system plays an important role in brain function and psychomotor development of children. Massage can benefit premature infants, but has limitations. STUDY DESIGN: The authors conducted a study to verify the direct effects of massage on amplitude-integrated electroencephalography (aEEG), oxygen saturation (SaO(2)), and pulse analyzed by color cerebral function monitor (CCFM) and cerebral blood flow assessed by the Doppler technique. RESULTS: The amplitude of the aEEG trend during massage significantly increased. Massage also impacted the dominant frequency δ waves. Frequency significantly increased during the massage and return to baseline after treatment. SaO(2) significantly decreased during massage. In four premature infants, massage was discontinued due to desaturation below 85%. Pulse frequency during the massage decreased but remained within physiological limits of greater than 100 beats per minute in all infants. Doppler flow values in the anterior cerebral artery measured before and after massage did not show statistically significant changes. Resistance index after massage decreased, which might provide greater perfusion of the brain, but this difference was not statistically significant. CONCLUSION: Use of the CCFM device allows for monitoring of three basic physiologic functions, namely aEEG, SaO(2), and pulse, and increases the safety of massage in preterm infants.