EEG Signal Quality Research Articles

Use of spontaneous blinking for application in human authentication

Contamination of electroencephalogram (EEG) signals due to natural blinking electrooculogram (EOG) signals is often removed to enhance the quality of EEG signals. This paper discusses the possibility of using solely involuntary blinking signals for human authentication. The EEG data of 46 subjects were recorded while the subject was looking at a sequence of different pictures. During the experiment, the subject was not focused on any kind of blinking task. Having the blink EOG signals separated from EEG, 25 features were extracted and the data were preprocessed in order to handle the corrupt or missing values. Since spontaneous and voluntary blinks have different characteristics in terms of kinematic variables and because the previous studies’ control setup may have altered the type of blink from spontaneous to voluntary, a series of statistical analysis was carried out in order to inspect the changes in the multivariate probability distribution of data compared to the previous studies. Statistical significance shows that it is very likely that the blink features of both voluntary and involuntary blink signal are generated by Gaussian probability density function, although different than voluntary blink, spontaneous blink is not well discriminated with Gaussian. Despite testing several models, none managed to classify the data using only the information of a single spontaneous blink. Thereby, we examined the possibility of learning the patterns of a series of blinks using Gated Recurrent Unit (GRU). Our results show that individuals can be distinguished with up to 98.7% accuracy using only a reasonably short sequence of involuntary blinking signals.

Matching pursuit algorithm for enhancing EEG signal quality and increasing the accuracy and efficiency of emotion recognition.

In this paper, we suggest an efficient, accurate and user-friendly brain-computer interface (BCI) system for recognizing and distinguishing different emotion states. For this, we used a multimodal dataset entitled "MAHOB-HCI" which can be freely reached through an email request. This research is based on electroencephalogram (EEG) signals carrying emotions and excludes other physiological features, as it finds EEG signals more reliable to extract deep and true emotions compared to other physiological features. EEG signals comprise low information and signal-to-noise ratios (SNRs); so it is a huge challenge for proposing a robust and dependable emotion recognition algorithm. For this, we utilized a new method, based on the matching pursuit (MP) algorithm, to resolve this imperfection. We applied the MP algorithm for increasing the quality and SNRs of the original signals. In order to have a signal of high quality, we created a new dictionary including 5-scale Gabor atoms with 5000 atoms. For feature extraction, we used a 9-scale wavelet algorithm. A 32-electrode configuration was used for signal collection, but we used just eight electrodes out of that; therefore, our method is highly user-friendly and convenient for users. In order to evaluate the results, we compared our algorithm with other similar works. In average accuracy, the suggested algorithm is superior to the same algorithm without applying MP by 2.8% and in terms of f-score by 0.03. In comparison with corresponding works, the accuracy and f-score of the proposed algorithm are better by 10.15% and 0.1, respectively. So as it is seen, our method has improved past works in terms of accuracy, f-score and user-friendliness despite using just eight electrodes.

Effect of Sweating on Electrode-Skin Contact Impedances and Artifacts in EEG Recordings With Various Screen-Printed Ag/Agcl Electrodes

In response to the growing clinico-economic need for comprehensive home-based sleep testing, we recently developed a self-applicable facial electrode set with screen-printed Ag/AgCl electrodes. Our previous studies revealed that nocturnal sweating is a common problem, causing low-frequency artifacts in the measured electroencephalography (EEG) signals. As the electrode set is designed to be used without skin abrasion, not surprisingly this leads to relatively high electrode-skin impedances, significant impedance changes due to sweating and an increased risk of sweat artifacts. However, our recent electrochemical in vitro investigations revealed that the sweat artifact tolerance of an EEG electrode can be improved by utilizing an appropriate Ag/AgCl ink. Here we have investigated in vivo electrode-skin impedances and the quality of EEG signals and interference due to sweating in the population of 11 healthy volunteers. Commercial Ag and Ag/AgCl inks (Engineered Conductive Materials ECM LLC and PPG Industries Inc.) were used to test electrode sets with differently constructed ink layers. Electrode-skin impedances and EEG signals were recorded before and after exercise-induced sweating. There was extensive variation in the electrode-skin impedances between the volunteers and the electrode positions: 14.6–200 $\text{k}\Omega $ (PPG electrodes) and 7.7–200 $\text{k}\Omega $ (ECM electrodes). Sweating significantly decreased ( $p ) the impedances in most cases. The EEG signal quality was assessed by comparing average band powers from 0.5 to 2 Hz before and after sweating. Only slight differences existed between the ECM and PPG electrodes; however, the lowest band power ratio ( i.e . the smallest increase in the band power due to sweating) was achieved with ECM electrodes.

Improved sweat artifact tolerance of screen-printed electrodes by material selection - in vivo comparison of EEG signal quality

Improved sweat artifact tolerance of screen-printed electrodes by material selection - in vivo comparison of EEG signal quality

Tripolar concentric EEG electrodes reduce noise

ObjectiveTo assay EEG signal quality recorded with tripolar concentric ring electrodes (TCREs) compared to regular EEG electrodes. MethodsEEG segments were recorded simultaneously by TCREs and regular electrodes, low-pass filtered at 35 Hz (REG35) and 70 Hz (REG70). Clips were rated blindly by nine electroencephalographers for presence or absence of key EEG features, relative to the “gold-standard” of the clinical report. ResultsTCRE showed less EMG artifact (F = 15.4, p < 0.0001). Overall quality rankings were not significantly different. Focal slowing was better detected by TCRE and spikes were better detected by regular electrodes. Seizures (n = 85) were detected by TCRE in 64 cases (75.3%), by REG70 in 75 (88.2%) and REG35 in 69 (81.2%) electrodes. TCRE detected 9 (10.6%) seizures not detected by one of the other 2 methods. In contrast, 14 seizures (16.5%) were not detected by TCRE, but were by REG35 electrodes. Each electrode detected interictal spikes when the other did not. ConclusionsTCRE produced similar overall quality and confidence ratings versus regular electrodes, but less muscle artifact. TCRE recordings detected seizures in 7% of instances where regular electrodes did not. SignificanceThe combination of the two types increased detection of epileptiform events compared to either alone.

Design of Wearable Headset with Steady State Visually Evoked Potential-Based Brain Computer Interface

Brain–computer interface (BCI) is a system that allows people to communicate directly with external machines via recognizing brain activities without manual operation. However, for most current BCI systems, conventional electroencephalography (EEG) machines and computers are usually required to acquire EEG signal and translate them into control commands, respectively. The sizes of the above machines are usually large, and this increases the limitation for daily applications. Moreover, conventional EEG electrodes also require conductive gels to improve the EEG signal quality. This causes discomfort and inconvenience of use, while the conductive gels may also encounter the problem of drying out during prolonged measurements. In order to improve the above issues, a wearable headset with steady-state visually evoked potential (SSVEP)-based BCI is proposed in this study. Active dry electrodes were designed and implemented to acquire a good EEG signal quality without conductive gels from the hairy site. The SSVEP BCI algorithm was also implemented into the designed field-programmable gate array (FPGA)-based BCI module to translate SSVEP signals into control commands in real time. Moreover, a commercial tablet was used as the visual stimulus device to provide graphic control icons. The whole system was designed as a wearable device to improve convenience of use in daily life, and it could acquire and translate EEG signal directly in the front-end headset. Finally, the performance of the proposed system was validated, and the results showed that it had excellent performance (information transfer rate = 36.08 bits/min).

Multiclass Fuzzy Time-Delay Common Spatio-Spectral Patterns With Fuzzy Information Theoretic Optimization for EEG-Based Regression Problems in Brain–Computer Interface (BCI)

Electroencephalogram (EEG) signals are one of the most widely used noninvasive signals in brain–computer interfaces. Large dimensional EEG recordings suffer from poor signal-to-noise ratio. These signals are very much prone to artifacts and noise, so sufficient preprocessing is done on raw EEG signals before using them for classification or regression. Properly selected spatial filters enhance the signal quality and subsequently improve the rate and accuracy of classifiers, but their applicability to solve regression problems is quite an unexplored objective. This paper extends common spatial patterns (CSP) to EEG state space using fuzzy time delay and thereby proposes a novel approach for spatial filtering. The approach also employs a novel fuzzy information theoretic framework for filter selection. Experimental performance on EEG-based reaction time (RT) prediction from a lane-keeping task data from 12 subjects demonstrated that the proposed spatial filters can significantly increase the EEG signal quality. A comparison based on root-mean-squared error (RMSE), mean absolute percentage error (MAPE), and correlation to true responses is made for all the subjects. In comparison to the baseline fuzzy CSP regression one versus rest, the proposed Fuzzy Time-delay Common Spatio-Spectral filters reduced the RMSE on an average by $\text{9.94}\%$ , increased the correlation to true RT on an average by $\text{7.38}\%$ , and reduced the MAPE by $\text{7.09}\%$ .

AN OPTIMAL APPROACH FOR EEG/ERP NOISE CANCELLATION USING ADAPTIVE FILTER WITH OPPOSITIONAL WHALE OPTIMIZATION ALGORITHM

In this paper, the Oppositional Whale Optimization Algorithm (OWOA) is applied to Adaptive Noise Canceller (ANC) for the filtering of Electroencephalography/Event-Related Potentials (EEG/ERP) signals. Performance of ANC will be improved by calculating the optimal weight value and proposed OWOA technique is used to update weight value. Adaptive filter’s noise reduction capability has been tested through consideration of White Gaussian Noise (WGN) over contaminated EEG signals at various SNR levels ([Formula: see text]10[Formula: see text]dB, [Formula: see text]15[Formula: see text]dB and [Formula: see text]20[Formula: see text]dB). The performance of the proposed OWOA algorithm is assessed in terms of Signal to Noise Ratio (SNR) in dB, mean value, and the correlation between resultant and input ERP. In this work, ANCs are also implemented by utilizing conventional gradient-based techniques like Recursive Least Square (RLS), Least Mean Square (LMS) and other optimization algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and WOA techniques. In average cases of noisy environment, comparative analysis shows that the proposed OWOA technique provides higher SNR value and significantly lower mean, and correlation as compared to gradient-based and swarm-based techniques. The comparative results show that extracting the desired EEG component is more effective in the proposed OWOA method. So, it has seen that OWOA-based noise reduction technique removing the artifacts and improving the quality of EEG signals significantly for biomedical analysis.

Comparison of a Pragmatic and Regression Approach for Wearable EEG Signal Quality Assessment.

Wearable electroencephalogram (EEG) solutions allow portability and real-time measurements in uncontrolled conditions. For reliable and reproducible interpretation of the EEG data, it is essential to accurately identify EEG segments contaminated by artefacts. Two data quality indicator approaches are proposed: pragmatic and regression based. The former extracts statistical features and applies data-driven thresholding, while the latter uses a regression model on the same set of statistical features to predict data quality. The performance of the approaches is validated against EEG data recorded during uncontrolled laboratory and free-living conditions, and compared to a validated approach. The proposed approaches achieve average accuracy of over [Formula: see text] in detecting artefactual data, which is higher than the FORCe signal quality estimation method ([Formula: see text]). The main strength of the proposed algorithms is in the significant increase of specificity over the state-of-the-art. The two models perform equally across different databases. Training of the two approaches on free-living conditions data showed better generalization when tested on different types of databases, i.e., uncontrolled laboratory and free-living. Although the accuracy in determining artefact-contaminated data is highest when using a window size of 8s, the accuracy drop is minor when using shorter window size, demonstrating another advantage over existing methods. Given low complexity of both pragmatic and regression approach, it facilitates a real-time implementation, which is demonstrated using a wearable EEG headset system available at IMEC.

Spherical image QoE approximations for vision augmentation scenarios

Augmented Reality (AR) devices are commonly head-worn to overlay context-dependent information into the field of view of the device operators. One particular scenario is the overlay of still images, for which we evaluate the interplay of user ratings as Quality of Experience (QoE) with (i) the non-referential BRISQUE objective image quality metric as Quality of Service (QoS) and (ii) human subject dry electrode EEG signals gathered with a commercial off-the-shelf device. We employ basic machine learning approaches to perform QoE and QoS predictions based on this data. We find strong correlations for QoS inputs with aggregated user ratings as Mean Opinion Scores with spherical images. For subject-specific EEG portfolios, overall predictability of the QoE for both media types can be attained. Our overall results can be employed in practical scenarios by content and network service providers to optimize the user experience in augmented reality scenarios with a passive human in-the-loop in the future.

A study on quality assessment of the surface EEG signal based on fuzzy comprehensive evaluation method

Surface EEG (Electroencephalography) signal is vulnerable to interference due to its characteristics and sampling methods. So it is of great importance to evaluate the collected EEG signal prior to use. Traditional methods usually use the impedance between skin and electrode to estimate the quality of the EEG signal, which has shortcomings such as monotonous features, high false positive rates, and poor real-time capability. Aiming at addressing these issues, this paper presents a novel model of EEG quality assessment based on Fuzzy Comprehensive Evaluation method. The developed model employs amplitude, power frequency ratio, and alpha band PSD (Power Spectral Density) ratio of resting EEG signal as evaluation factors, and performs a quantitative assessment of the signal quality. Experiments show that the proposed model can significantly determine the EEG signal quality. In addition, the model is simple in implementation with low computational complexity, and is able to present the EEG quality evaluation results in real time. Before the formal measurement, collecting short-term resting EEG data, and evaluating the EEG signal quality and current signal acquisition environment using the model, the collection efficiency of qualified EEG signals can be greatly improved.

Scalp sensor for simultaneous acoustic emission detection and electroencephalography during transcranial ultrasound

Focused ultrasound is now capable of noninvasively penetrating the intact human skull and delivering energy to specific areas of the brain with millimeter accuracy. The ultrasound energy is supplied in high-intensities to create brain lesions or at low-intensities to produce reversible physiological interventions. Conducting acoustic emission detection (AED) and electroencephalography (EEG) during transcranial focused ultrasound may lead to several new brain treatment and research applications. This study investigates the feasibility of using a novel scalp senor for acquiring concurrent AED and EEG during clinical transcranial ultrasound. A piezoelectric disk is embedded in a plastic cup EEG electrode to form the sensor. The sensor is coupled to the head via an adhesive/conductive gel-dot. Components of the sensor prototype are tested for AED and EEG signal quality in a bench top investigation with a functional ex vivo skull phantom.

The Next Paradigm Change in EEG Recording: What Will it Mean for EEG Technologists?

ABSTRACTSeveral companies are developing an EEG recording system that uses dry, wireless electrodes, and one has recently been approved by the Food and Drug Administration (FDA) for medical use. Today, the quality of EEG signals recorded by these dry systems may not always match those of conventionally applied electrodes, but the gap is closing fast. As dry, wireless EEG systems find their way into hospitals and clinics over the next few years, the job of the EEG technologist will change. Dry systems can be placed on like a cap and, after a few minutes of adjustment, begin broadcasting EEG. No longer will it be as necessary to spend time meticulously measuring, cleaning, affixing, and adjusting electrodes on the scalp. This upcoming time of rapid change can be an opportunity to redefine the role of the EEG technologist.

Effects of Cable Sway, Electrode Surface Area, and Electrode Mass on Electroencephalography Signal Quality during Motion.

More neuroscience researchers are using scalp electroencephalography (EEG) to measure electrocortical dynamics during human locomotion and other types of movement. Motion artifacts corrupt the EEG and mask underlying neural signals of interest. The cause of motion artifacts in EEG is often attributed to electrode motion relative to the skin, but few studies have examined EEG signals under head motion. In the current study, we tested how motion artifacts are affected by the overall mass and surface area of commercially available electrodes, as well as how cable sway contributes to motion artifacts. To provide a ground-truth signal, we used a gelatin head phantom with embedded antennas broadcasting electrical signals, and recorded EEG with a commercially available electrode system. A robotic platform moved the phantom head through sinusoidal displacements at different frequencies (0–2 Hz). Results showed that a larger electrode surface area can have a small but significant effect on improving EEG signal quality during motion and that cable sway is a major contributor to motion artifacts. These results have implications in the development of future hardware for mobile brain imaging with EEG.

Spatial Filtering for EEG-Based Regression Problems in Brain–Computer Interface (BCI)

Electroencephalogram (EEG) signals are frequently used in brain–computer interfaces (BCIs), but they are easily contaminated by artifacts and noise, so preprocessing must be done before they are fed into a machine learning algorithm for classification or regression. Spatial filters have been widely used to increase the signal-to-noise ratio of EEG for BCI classification problems, but their applications in BCI regression problems have been very limited. This paper proposes two common spatial pattern (CSP) filters for EEG-based regression problems in BCI, which are extended from the CSP filter for classification, by using fuzzy sets. Experimental results on EEG-based response speed estimation from a large-scale study, which collected 143 sessions of sustained-attention psychomotor vigilance task data from 17 subjects during a 5-month period, demonstrate that the two proposed spatial filters can significantly increase the EEG signal quality. When used in LASSO and $k$ -nearest neighbors regression for user response speed estimation, the spatial filters can reduce the root-mean-square estimation error by $10.02-19.77\%$ , and at the same time increase the correlation to the true response speed by $19.39-86.47\%$ .

Steady state visual evoked potential (SSVEP) based brain-computer interface (BCI) performance under different perturbations.

Brain-computer interface (BCI) paradigms are usually tested when environmental and biological artifacts are intentionally avoided. In this study, we deliberately introduced different perturbations in order to test the robustness of a steady state visual evoked potential (SSVEP) based BCI. Specifically we investigated to what extent a drop in performance is related to the degraded quality of EEG signals or rather due to increased cognitive load. In the online tasks, subjects focused on one of the four circles and gave feedback on the correctness of the classification under four conditions randomized across subjects: Control (no perturbation), Speaking (counting loudly and repeatedly from one to ten), Thinking (mentally counting repeatedly from one to ten), and Listening (listening to verbal counting from one to ten). Decision tree, Naïve Bayes and K-Nearest Neighbor classifiers were used to evaluate the classification performance using features generated by canonical correlation analysis. During the online condition, Speaking and Thinking decreased moderately the mean classification accuracy compared to Control condition whereas there was no significant difference between Listening and Control conditions across subjects. The performances were sensitive to the classification method and to the perturbation conditions. We have not observed significant artifacts in EEG during perturbations in the frequency range of interest except in theta band. Therefore we concluded that the drop in the performance is likely to have a cognitive origin. During the Listening condition relative alpha power in a broad area including central and temporal regions primarily over the left hemisphere correlated negatively with the performance thus most likely indicating active suppression of the distracting presentation of the playback. This is the first study that systematically evaluates the effects of natural artifacts (i.e. mental, verbal and audio perturbations) on SSVEP-based BCIs. The results can be used to improve individual classification performance taking into account effects of perturbations.

Real-Time EEG Signal Enhancement Using Canonical Correlation Analysis and Gaussian Mixture Clustering.

Electroencephalogram (EEG) signals are usually contaminated with various artifacts, such as signal associated with muscle activity, eye movement, and body motion, which have a noncerebral origin. The amplitude of such artifacts is larger than that of the electrical activity of the brain, so they mask the cortical signals of interest, resulting in biased analysis and interpretation. Several blind source separation methods have been developed to remove artifacts from the EEG recordings. However, the iterative process for measuring separation within multichannel recordings is computationally intractable. Moreover, manually excluding the artifact components requires a time-consuming offline process. This work proposes a real-time artifact removal algorithm that is based on canonical correlation analysis (CCA), feature extraction, and the Gaussian mixture model (GMM) to improve the quality of EEG signals. The CCA was used to decompose EEG signals into components followed by feature extraction to extract representative features and GMM to cluster these features into groups to recognize and remove artifacts. The feasibility of the proposed algorithm was demonstrated by effectively removing artifacts caused by blinks, head/body movement, and chewing from EEG recordings while preserving the temporal and spectral characteristics of the signals that are important to cognitive research.

Electro-deposited Nanoporous Platinum Electrode for EEG Monitoring.

BackgroundOne of the key issues in electroencephalogram (EEG) monitoring is accurate signal acquisition with less cumbersome electrodes. In this study, the L2 phase electro-deposited nanoporous platinum (L2-ePt) electrode is introduced, which is a new type of electrode that utilizes a stable nanoporous platinum surface to reduce the skin-electrode impedance.MethodsL2-ePt electrodes were fabricated using electro-deposition technique. Then, the effect of the nanoporous surface on the surface roughness and the electrode impedance were observed from the L2-ePt electrodes and the flat platinum (FlatPt) electrode. The skin-electrode impedances of the L2-ePt electrodes, a gold cup electrode, and the FlatPt electrode were evaluated when placed on the hairy occipital area of the head in ten subjects. For the validation of using the L2-ePt electrode, a correlational analysis of the alpha rhythms was performed in the same subjects for simultaneous EEG recordings using the L2-ePt and clinically-used EEG electrodes.ResultsThe results indicated that the L2-ePt electrode with a roughness factor of 200 had the lowest mean impedance performance. Moreover, the proposed L2-ePt electrode showed a significantly lower mean skin-electrode impedance than the FlatPt electrode. Finally, the EEG signal quality recorded by the L2-ePt electrode (r = 0.94) was comparable to that of the clinically-used gold cup electrode.ConclusionBased on these results, the proposed L2-ePt electrode is suitable for use in various high-quality EEG applications.

Assessing a novel micro-seepage electrode with flexible and elastic tips for wearable EEG acquisition

ObjectElectroencephalogram (EEG) electrodes play an important role in the applications of wearable brain-computer interfaces (BCI). Although traditional wet gel-based electrodes can obtain low contact impedance and high signal-to-noise ratio (SNR), cumbersome procedures including skin preparation, gelling, and hair cleaning make them inconvenient to use. Dry electrodes can avoid these shortcomings; however, they can hardly achieve impedance and SNR as good as wet electrodes. This study aims to develop a novel micro-seepage electrode, which possesses both high convenience of dry electrode and low contact impedance of wet electrode, and assess contact impedance, comfort level, and EEG signal quality of the proposed electrode. MethodThe proposed micro-seepage electrode can continuously provide a small amount of electrolyte from flexible brush-pen like tips to scalp, which keeps the scalp-electrode interface moist like that of wet electrodes. The electrode can be put on and taken off easily like dry electrode, and there is also no need to wash hair after use. This study investigated the contact impedance of the electrode with regard to electrolyte type, recording duration, and contact pressure on the occipital area. In addition, this study compared SNR and classification accuracy of steady-state visual evoked potentials (SSVEPs) between the proposed electrode and the wet electrode. ResultThe proposed electrode can be set up very quickly. The flexible and elastic tips make it comfortable to wear. Using the contact lens cleaning solution as electrolyte, the average impedance of the electrode was 12.1 kΩ@10 Hz. The impedance kept below 20 kΩ for more than 8 h on volunteers’ scalp. The SNR and classification accuracy of SSVEPs obtained by the proposed electrodes were comparable to those of wet electrodes. SignificanceThe proposed micro-seepage electrode can provide a comfortable and convenient solution for high-quality EEG acquisition.

Efficient Removal of Artifacts from EEG SIGNAL Using Enhanced Hybrid Learning Method

In this paper, the analysis and removal of artifacts is done by the proposed technique. Normally, ECG is one of the components of artifacts source and EEG is mixed by various artifacts and affects the electroencephalographic data. For further clinical analysis the data preparation is important to minimize the artifacts. In proposed method, Improved Adaptive Neuro-Fuzzy Inference System (IANFIS) and Improved ANFISParticle Swarm Optimization (IANFIS-PSO) algorithms are used to separate the signals of ECG and EEG for eliminating artifacts and to intensify the estimation of EEG signal quality. The pre-processing is done by ennobled quantum based genetic algorithm for fast process of optimization and removal of noise interference. The simulation result shows the improvement in Signal-to-Noise Ratio (SNR), minimum Mean-Square Error (MSE) along with the Power Spectrum Density (PSD) plot, which are used to measure the performance comparison of proposed with existing algorithm. The prospective method perfo...