Direction and viewing area-sensitive influence of EOG artifacts revealed in the EEG topographic pattern analysis

Technical challenges in REM sleep microstructure classification: A study of patients with REM sleep behaviour disorder

While commonly treated as a uniform state in practice, rapid eye movement sleep contains two distinct microstructures-phasic (presence of rapid eye movement) and tonic (no rapid eye movement). This study aims to identify technical challenges during rapid eye movement sleep microstructure visual classification in patients with rapid eye movement sleep behaviour disorder, and to propose solutions to enhance reliability between scorers. Fifty-seven sleep recordings were randomly allocated into three subsequent batches (n = 10, 13 and 34) for scoring. To reduce single-centre bias, we recruited three raters/scorers, with each trained from a different institution. Two raters independently scored each 30-s rapid eye movement sleep into 10 × fSEM3-s phasic/tonic microstructures based on the AASM guidelines. The third rater acted as an "arbitrator" to resolve opposite opinions persisting during the revision between batches. Besides interrater differences in artefact rejection rate, interrater variance frequently occurred due to transitioning between microstructures and moderate-to-severe muscular/electrode artefact interference. To enhance interrater agreement, a rapid eye movement scoring schematic graph was developed, incorporating proxy electrode use, filters and cut-offs for microstructure transitioning. To assess potential effectiveness of the schematic graph proposed, raters were instructed to systematically apply it in scoring for the third batch. Of the 34 recordings, 27 reached a Cohen's kappa score above 0.8 (i.e. almost perfect agreement between raters), significantly improved from the prior batches (p = 0.0003, Kruskal-Wallis test). Our study illustrated potential solutions and guidance for challenges that may be encountered during rapid eye movement sleep microstructure classification.

A neural network predicting the amplitude of the N2pc in individual EEG datasets

Objective.The N2pc is a small amplitude transient interhemispheric voltage asymmetry used in cognitive neuroscience to investigate subject's allocation of selective visuo-spatial attention. N2pc is typically estimated by averaging the sweeps of the electroencephalographic (EEG) signal but, in absence of explicit normative indications, the number of sweeps is often based on arbitrariness or personal experience. With the final aim of reducing duration and cost of experimental protocols, here we developed a new approach to reliably predict N2pc amplitude from a minimal EEG dataset.Approach.First, features predictive of N2pc amplitude were identified in the time-frequency domain. Then, an artificial neural network (NN) was trained to predict N2pc mean amplitude at the individual level. By resorting to simulated data, accuracy of the NN was assessed by computing the mean squared error (MSE) and the amplitude discretization error (ADE) and compared to the standard time averaging (TA) technique. The NN was then tested against two real datasets consisting of 14 and 12 subjects, respectively.Main result.In simulated scenarios entailing different number of sweeps (between 10 and 100), the MSE obtained with the proposed method resulted, on average, 1/5 of that obtained with the TA technique. Implementation on real EEG datasets showed that N2pc amplitude could be reliably predicted with as few as 40 EEG sweeps per cell of the experimental design.Significance.The developed approach allows to reduce duration and cost of experiments involving the N2pc, for instance in studies investigating attention deficits in pathological subjects.

Computer data simulator to assess the accuracy of estimates of visual N2/N2pc event-related potential components

OBJECTIVE

Event-related potentials (ERPs) evoked by visual stimulations comprise several components, with different amplitudes and latencies. Among them, the N2 and N2pc components have been demonstrated to be a measure of subjects' allocation of visual attention to possible targets and to be involved in the suppression of irrelevant items. Unfortunately, the N2 and N2pc components have smaller amplitudes compared with those of the background electroencephalogram (EEG), and their measurement requires employing techniques such as conventional averaging, which in turn necessitates several sweeps to provide acceptable estimates. In visual search studies, the number of sweeps (Nswp) used to extrapolate reliable estimates of N2/N2pc components has always been somehow arbitrary, with studies using 50-500 sweeps. In-silico studies relying on synthetic data providing a close-to-realistic fit to the variability of the visual N2 component and background EEG signals are therefore needed to go beyond arbitrary choices in this context.

APPROACH

In the present work, we sought to take a step in this direction by developing a simulator of ERP variations in the N2 time range based on real experimental data while monitoring variations in the estimation accuracy of N2/N2pc components as a function of two factors, i.e. signal-to-noise ratio (SNR) and number of averaged sweeps.

MAIN RESULTS

The results revealed that both Nswp and SNR had a strong impact on the accuracy of N2/N2pc estimates. Critically, the present simulation showed that, for a given level of SNR, a non-arbitrary Nswp could be parametrically determined, after which no additional significant improvements in noise suppression and N2/N2pc accuracy estimation were observed.

SIGNIFICANCE

The present simulator is thought to provide investigators with quantitative guidelines for designing experimental protocols aimed at improving the detection accuracy of N2/N2pc components. The parameters of the simulator can be tuned, adapted, or integrated to fit other ERP modulations.

A TrAdaBoost Method for Detecting Multiple Subjects' N200 and P300 Potentials Based on Cross-Validation and an Adaptive Threshold

Traditional training methods need to collect a large amount of data for every subject to train a subject-specific classifier, which causes subjects fatigue and training burden. This study proposes a novel training method, TrAdaBoost based on cross-validation and an adaptive threshold (CV-T-TAB), to reduce the amount of data required for training by selecting and combining multiple subjects' classifiers that perform well on a new subject to train a classifier. This method adopts cross-validation to extend the amount of the new subject's training data and sets an adaptive threshold to select the optimal combination of the classifiers. Twenty-five subjects participated in the N200- and P300-based brain-computer interface. The study compares CV-T-TAB to five traditional training methods by testing them on the training of a support vector machine. The accuracy, information transfer rate, area under the curve, recall and precision are used to evaluate the performances under nine conditions with different amounts of data. CV-T-TAB outperforms the other methods and retains a high accuracy even when the amount of data is reduced to one-third of the original amount. The results imply that CV-T-TAB is effective in improving the performance of a subject-specific classifier with a small amount of data by adopting multiple subjects' classifiers, which reduces the training cost.

Development of an Improved LMD Method for the Low-Frequency Elements Extraction from Turbine Noise Background

Given the prejudicial environmental effects of fossil-fuel based energy production, renewable energy sources can contribute significantly to the sustainability of human society. As a clean, cost effective and inexhaustible renewable energy source, wind energy harvesting has found a wide application to replace conventional energy productions. However, concerns have been raised over the noise generated by turbine operating, which is helpful in fault diagnose but primarily identified for its adverse effects on the local ecosystems. Therefore, noise monitoring and separation is essential in wind turbine deployment. Recent developments in condition monitoring provide a solution for turbine noise and vibration analysis. However, the major component, aerodynamic noise is often distorted in modulation, which consequently affects the condition monitoring. This study is conducted to explore a novel approach to extract low-frequency elements from the aerodynamic noise background, and to improve the efficiency of online monitoring. A framework built on the spline envelope method and improved local mean decomposition has been developed for low-frequency noise extraction, and a case study with real near-field noises generated by a mountain-located wind turbine was employed to validate the proposed approach. Results indicate successful extractions with high resolution and efficiency. Findings of this research are also expected to further support the fault diagnosis and the improvement in condition monitoring of turbine systems.

Optimizing the ICA-based removal of ocular EEG artifacts from free viewing experiments

Combining EEG with eye-tracking is a promising approach to study neural correlates of natural vision, but the resulting recordings are also heavily contaminated by activity of the eye balls, eye lids, and extraocular muscles. While Independent Component Analysis (ICA) is commonly used to suppress these ocular artifacts, its performance under free viewing conditions has not been systematically evaluated and many published reports contain residual artifacts. Here I evaluated and optimized ICA-based correction for two tasks with unconstrained eye movements: visual search in images and sentence reading. In a first step, four parameters of the ICA pipeline were varied orthogonally: the (1) high-pass and (2) low-pass filter applied to the training data, (3) the proportion of training data containing myogenic saccadic spike potentials (SP), and (4) the threshold for eye tracker-based component rejection. In a second step, the eye-tracker was used to objectively quantify the correction quality of each ICA solution, both in terms of undercorrection (residual artifacts) and overcorrection (removal of neurogenic activity). As a benchmark, results were compared to those obtained with an alternative spatial filter, Multiple Source Eye Correction (MSEC). With commonly used settings, Infomax ICA not only left artifacts in the data, but also distorted neurogenic activity during eye movement-free intervals. However, correction results could be strongly improved by training the ICA on optimally filtered data in which SPs were massively overweighted. With optimized procedures, ICA removed virtually all artifacts, including the SP and its associated spectral broadband artifact from both viewing paradigms, with little distortion of neural activity. It also outperformed MSEC in terms of SP correction. Matlab code is provided.

A Removal of Eye Movement and Blink Artifacts from EEG Data Using Morphological Component Analysis

EEG signals contain a large amount of ocular artifacts with different time-frequency properties mixing together in EEGs of interest. The artifact removal has been substantially dealt with by existing decomposition methods known as PCA and ICA based on the orthogonality of signal vectors or statistical independence of signal components. We focused on the signal morphology and proposed a systematic decomposition method to identify the type of signal components on the basis of sparsity in the time-frequency domain based on Morphological Component Analysis (MCA), which provides a way of reconstruction that guarantees accuracy in reconstruction by using multiple bases in accordance with the concept of “dictionary.” MCA was applied to decompose the real EEG signal and clarified the best combination of dictionaries for this purpose. In our proposed semirealistic biological signal analysis with iEEGs recorded from the brain intracranially, those signals were successfully decomposed into original types by a linear expansion of waveforms, such as redundant transforms: UDWT, DCT, LDCT, DST, and DIRAC. Our result demonstrated that the most suitable combination for EEG data analysis was UDWT, DST, and DIRAC to represent the baseline envelope, multifrequency wave-forms, and spiking activities individually as representative types of EEG morphologies.