Artifact Removal Methods in EEG Recordings: A Review

To obtain the correct analysis of electroencephalogram (EEG) signals, non-physiological and physiological artifacts should be removed from EEG signals. This study aims to give an overview on the existing methodology for removing physiological artifacts, e.g., ocular, cardiac, and muscle artifacts. The datasets, simulation platforms, and performance measures of artifact removal methods in previous related research are summarized. The advantages and disadvantages of each technique are discussed, including regression method, filtering method, blind source separation (BSS), wavelet transform (WT), empirical mode decomposition (EMD), singular spectrum analysis (SSA), and independent vector analysis (IVA). Also, the applications of hybrid approaches are presented, including discrete wavelet transform - adaptive filtering method (DWT-AFM), DWT-BSS, EMD-BSS, singular spectrum analysis - adaptive noise canceler (SSA-ANC), SSA-BSS, and EMD-IVA. Finally, a comparative analysis for these existing methods is provided based on their performance and merits. The result shows that hybrid methods can remove the artifacts more effectively than individual methods

Emotion Classification through Nonlinear EEG Analysis Using Machine Learning Methods

Background: Emotion recognition, as a subset of affective computing, has received considerable attention in recent years. Emotions are key to human-computer interactions. Electroencephalogram (EEG) is considered a valuable physiological source of information for classifying emotions. However, it has complex and chaotic behavior.Methods: In this study, an attempt is made to extract important nonlinear features from EEGs with the aim of emotion recognition. We also take advantage of machine learning methods such as evolutionary feature selection methods and committee machines to enhance the classification performance. Classification performed concerning both arousal and valence factors.Results: Results suggest that the proposed method is successful and comparable to the previous works. A recognition rate equal to 90% achieved, and the most significant features reported. We apply the final classification scheme to 2 different databases including our recorded EEGs and a benchmark dataset to evaluate the suggested approach.Conclusion: Our findings approve of the effectiveness of using nonlinear features and a combination of classifiers. Results are also discussed from different points of view to understand brain dynamics better while emotion changes. This study reveals useful insights about emotion classification and brain-behavior related to emotion elicitation

Validating and improving the correction of ocular artifacts in electro-encephalography

For modern applications of electro-encephalography, including brain computer interfaces and single-trial Event Related Potential detection, it is becoming increasingly important that artifacts are accurately removed from a recorded electro-encephalogram (EEG) without affecting the part of the EEG that reflects cerebral activity. Ocular artifacts are caused by movement of the eyes and the eyelids. They occur frequently in the raw EEG and are often the most prominent artifacts in EEG recordings. Their accurate removal is therefore an important procedure in nearly all electro-encephalographic research. As a result of this, a considerable number of ocular artifact correction methods have been introduced over the past decades. A selection of these methods, which contains some of the most frequently used correction methods, is given in Section 1.5. When two different correction methods are applied to the same raw EEG, this usually results in two different corrected EEGs. A measure for the accuracy of correction should indicate how well each of these corrected EEGs recovers the part of the raw EEG that truly reflects cerebral activity. The fact that this accuracy cannot be determined directly from a raw EEG is intrinsic to the need for artifact removal. If, based on a raw EEG, it would be possible to derive an exact reference on what the corrected EEG should be, then there would not be any need for adequate artifact correction methods. Estimating the accuracy of correction methods is mostly done either by using models to simulate EEGs and artifacts, or by manipulating the experimental data in such a way that the effects of artifacts to the raw EEG can be isolated. In this thesis, modeling of EEG and artifact is used to validate correction methods based on simulated data. A new correction method is introduced which, unlike all existing methods, uses a camera to monitor eye(lid) movements as a basis for ocular artifact correction. The simulated data is used to estimate the accuracy of this new correction method and to compare it against the estimated accuracy of existing correction methods. The results of this comparison suggest that the new method significantly increases correction accuracy compared to the other methods. Next, an experiment is performed, based on which the accuracy of correction can be estimated on raw EEGs. Results on this experimental data comply very well with the results on the simulated data. It is therefore concluded that using a camera during EEG recordings provides valuable extra information that can be used in the process of ocular artifact correction. In Chapter 2, a model is introduced that assists in estimating the accuracy of eye movement artifacts for simulated EEG recordings. This model simulates EEG and eye movement artifacts simultaneously. For this, the model uses a realistic representation of the head, multiple dipoles to model cerebral and ocular electrical activity, and the boundary element method to calculate changes in electrical potential at different positions on the scalp. With the model, it is possible to simulate different data sets as if they are recorded using different electrode configurations. Signal to noise ratios are used to assess the accuracy of these six correction methods for various electrode configurations before and after applying six different correction methods. Results show that out of the six methods, second order blind identification, SOBI, and multiple linear regression, MLR, correct most accurately overall as they achieve the highest rise in signal to noise ratio. The occurrence of ocular artifacts is linked to changes in eyeball orientation. In Chapter 2 an eye tracker is used to record pupil position, which is closely linked to eyeball orientation. The pupil position information is used in the model to simulate eye movements. Recognizing the potential benefit of using an eye tracker not only for simulations, but also for correction, Chapter 3 introduces an eye movement artifact correction method that exploits the pupil position information that is provided by an eye tracker. Other correction methods use the electrooculogram (EOG) and/or the EEG to estimate ocular artifacts. Because both the EEG and the EOG recordings are susceptive to cerebral activity as well as to ocular activity, these other methods are at risk of overcorrecting the raw EEG. Pupil position information provides a reference that is linked to the ocular artifact in the EEG but that cannot be affected by cerebral activity, and as a result the new correction method avoids having to solve traditionally problematic issues like forward/backward propagation and evaluating the accuracy of component extraction. By using both simulated and experimental data, it is determined how pupil position influences the raw EEG and it is found that this relation is linear or quadratic. A Kalman filter is used for tuning of the parameters that specify the relation. On simulated data, the new method performs very well, resulting in an SNR after correction of over 10 dB for various patterns of eye movements. When compared to the three methods that performed best in the evaluation of Chapter 2, only the SOBI method which performed best in that evaluation shows similar results for some of the eye movement patterns. However, a serious limitation of the correction method is its inability to correct blink artifacts. In order to increase the variety of applications for which the new method can be used, the new correction should be improved in a way that enables it to correct the raw EEG for blinking artifacts. Chapter 4 deals with implementing such improvements based on the idea that a more advanced eye-tracker should be able to detect both the pupil position and the eyelid position. The improved eye tracker-based ocular artifact correction method is named EYE. Driven by some practical limitations regarding the eye tracking device currently available to us, an alternative way to estimate eyelid position is suggested, based on an EOG recorded above one eye. The EYE method can be used with both the eye tracker information or with the EOG substitute. On simulated data, accuracy of the EYE method is estimated using the EOGbased eyelid reference. This accuracy is again compared against the six other correction methods. Two different SNR-based measures of accuracy are proposed. One of these quantifies the correction of the entire simulated data set and the other focuses on those segments containing simulated blinking artifacts. After applying EYE, an average SNR of at least 9 dB for both these measures is achieved. This implies that the power of the corrected signal is at least eight times the power of the remaining noise. The simulated data sets contain a wide range of eye movements and blink frequencies. For almost all of these data sets, 16 out of 20, the correction results for EYE are better than for any of the other evaluated correction method. On experimental data, the EYE method appears to adequately correct for ocular artifacts as well. As the detection of eyelid position from the EOG is in principle inferior to the detection of eyelid position with the use of an eye tracker, these results should also be considered as an indicator of even higher accuracies that could be obtained with a more advanced eye tracker. Considering the simplicity of the MLR method, this method also performs remarkably well, which may explain why EOG-based regression is still often used for correction. In Chapter 5, the simulation model of Chapter 2 is put aside and, alternatively, experimentally recorded data is manipulated in a way that correction inaccuracies can be highlighted. Correction accuracies of eight correction methods, including EYE, are estimated based on data that are recorded during stop-signal tasks. In the analysis of these tasks it is essential that ocular artifacts are adequately removed because the task-related ERPs, are located mostly at frontal electrode positions and are low-amplitude. These data are corrected and subsequently evaluated. For the eight methods, the overall ranking of estimated accuracy in Figure 5.3, corresponds very well with the correction accuracy of these methods on simulated data as was found in Chapter 4. In a single-trial correction comparison, results suggest that the EYE corrected EEG, is not susceptible to overcorrection, whereas the other corrected EEGs are

An investigation into the mechanisms of inter-brain synchrony during early social interactions

Over the last 20 years there has been a growing increase in the amount of research investigating how and why two or more individual’s brain activity can synchronise during social interaction. What we know so far from this research is that inter-brain synchrony (defined through temporally coordinated patterns of brain activity between two interacting individuals, Holroyd 2022) tends to associate with moments of behavioural coordination (i.e., when two individuals are doing or attending to the same thing at the same time) and task cooperation (i.e., the action or process of two individuals working together to the same end). These observations have led many researchers to theorise over whether and how behavioural coordination mechanistically drives inter-brain synchrony (Wass et al., 2020; Hamilton, 2021). There is also some very recent evidence to suggest that increased inter-brain synchrony actually facilitates/ supports aspects of social interaction. For example, inter-brain synchrony has been shown to predict team performance (Reinero et al., 2021), although this research is primarily based on correlational study designs. Taken together however the field of inter-brain synchrony shares one fundamental limitation; that is that it does not account (although see recent animal research e.g., Kingsbury et al., 2019; Zhang et al., 2019), empirically for the mechanisms that give rise to inter-brain synchrony, which would help to falsify claims that inter-brain synchrony is a core mechanism facilitating social interaction. This is because of two main reasons; Firstly, the study of inter-brain synchrony has primarily been investigated as a time-invariant property, almost no studies have explored how inter-brain synchrony varies over time relative to individual moments of behavioural coordination. Secondly, little attention has been paid to the changes in the underlying signal properties (i.e., increases in power, changes in frequency) that must take place for two unsynchronised signals to become synchronised (e.g., Haresign et al., 2022). Using two-person naturalistic biobehavioural recording techniques, coupled with state of the art, EEG pre-processing and analyses procedures (see chapters 5 and 6), the present thesis examines the mechanisms that give rise to inter-brain synchrony during parent-infant social interactions. Evidence is presented showing how inter-brain synchrony does not arise around individual moments of gaze coordination. This is despite previous investigations suggesting that increased inter-brain synchrony (averaged over all moments of eye contact) associates with gaze synchrony. Evidence also shows the contribution of behavioural coordination across multiple modalities to inter-brain synchrony during parent-infant social interaction. Discussion is focused on the contribution of these findings to our understanding of the mechanisms that give rise to inter-brain synchrony

Finding the discriminative frequencies of motor electroencephalography signal using genetic algorithm

A crucial part of the brain-computer interface is a classification of electroencephalography (EEG) motor tasks. Artifacts such as eye and muscle movements corrupt EEG signal and reduce the classification performance. Many studies try to extract not redundant and discriminative features from EEG signals. Therefore, this study proposed a signal preprocessing and feature extraction method for EEG classification. It consists of removing the artifacts by using discrete fourier transform (DFT) as an ideal filter for specific frequencies. It also cross-correlates the EEG channels with the effective channels to emphases the EEG motor signals. Then the resultant from cross correlation are statistical calculated to extract feature for classifying a left and right finger movements using support vector machine (SVM). The genetic algorithm was applied to find the discriminative frequencies of DFT for the two EEG classes signal. The performance of the proposed method was determined by finger movement classification of 13 subjects and the experiments show that the average accuracy is above 93 percent

Determination and evaluation of clinically efficient stopping criteria for the multiple auditory steady-state response technique

Background: Although the auditory steady-state response (ASSR) technique utilizes objective statistical detection algorithms to estimate behavioural hearing thresholds, the audiologist still has to decide when to terminate ASSR recordings introducing once more a certain degree of subjectivity. Aims: The present study aimed at establishing clinically efficient stopping criteria for a multiple 80-Hz ASSR system. Methods: In Experiment 1, data of 31 normal hearing subjects were analyzed off-line to propose stopping rules. Consequently, ASSR recordings will be stopped when (1) all 8 responses reach significance and significance can be maintained for 8 consecutive sweeps; (2) the mean noise levels were ≤ 4 nV (if at this “≤ 4-nV” criterion, p-values were between 0.05 and 0.1, measurements were extended only once by 8 sweeps); and (3) a maximum amount of 48 sweeps was attained. In Experiment 2, these stopping criteria were applied on 10 normal hearing and 10 hearing-impaired adults to asses the efficiency. Results: The application of these stopping rules resulted in ASSR threshold values that were comparable to other multiple-ASSR research with normal hearing and hearing-impaired adults. Furthermore, in 80% of the cases, ASSR thresholds could be obtained within a time-frame of 1 hour. Investigating the significant response-amplitudes of the hearing-impaired adults through cumulative curves indicated that probably a higher noise-stop criterion than “≤ 4 nV” can be used. Conclusions: The proposed stopping rules can be used in adults to determine accurate ASSR thresholds within an acceptable time-frame of about 1 hour. However, additional research with infants and adults with varying degrees and configurations of hearing loss is needed to optimize these criteria

Approaches, applications, and challenges in physiological emotion recognition — a tutorial overview

An automatic emotion recognition system can serve as a fundamental framework for various applications in daily life from monitoring emotional well-being to improving the quality of life through better emotion regulation. Understanding the process of emotion manifestation becomes crucial for building emotion recognition systems. An emotional experience results in changes not only in interpersonal behavior but also in physiological responses. Physiological signals are one of the most reliable means for recognizing emotions since individuals cannot consciously manipulate them for a long duration. These signals can be captured by medical-grade wearable devices, as well as commercial smart watches and smart bands. With the shift in research direction from laboratory to unrestricted daily life, commercial devices have been employed ubiquitously. However, this shift has introduced several challenges, such as low data quality, dependency on subjective self-reports, unlimited movement-related changes, and artifacts in physiological signals. This tutorial provides an overview of practical aspects of emotion recognition, such as experiment design, properties of different physiological modalities, existing datasets, suitable machine learning algorithms for physiological data, and several applications. It aims to provide the necessary psychological and physiological backgrounds through various emotion theories and the physiological manifestation of emotions, thereby laying a foundation for emotion recognition. Finally, the tutorial discusses open research directions and possible solutions

Predictive decoding of neural data

In the last five decades the number of techniques available for non-invasive functional imaging has increased dramatically. Researchers today can choose from a variety of imaging modalities that include EEG, MEG, PET, SPECT, MRI, and fMRI. This doctoral dissertation offers a methodology for the reliable analysis of neural data at different levels of investigation. By using statistical learning algorithms the proposed approach allows single-trial analysis of various neural data by decoding them into variables of interest. Unbiased testing of the decoder on new samples of the data provides a generalization assessment of decoding performance reliability. Through consecutive analysis of the constructed decoder\u27s sensitivity it is possible to identify neural signal components relevant to the task of interest. The proposed methodology accounts for covariance and causality structures present in the signal. This feature makes it more powerful than conventional univariate methods which currently dominate the neuroscience field. Chapter 2 describes the generic approach toward the analysis of neural data using statistical learning algorithms. Chapter 3 presents an analysis of results from four neural data modalities: extracellular recordings, EEG, MEG, and fMRI. These examples demonstrate the ability of the approach to reveal neural data components which cannot be uncovered with conventional methods. A further extension of the methodology, Chapter 4 is used to analyze data from multiple neural data modalities: EEG and fMRI. The reliable mapping of data from one modality into the other provides a better understanding of the underlying neural processes. By allowing the spatial-temporal exploration of neural signals under loose modeling assumptions, it removes potential bias in the analysis of neural data due to otherwise possible forward model misspecification. The proposed methodology has been formalized into a free and open source Python framework for statistical learning based data analysis. This framework, PyMVPA, is described in Chapter 5

Systems engineering approaches to safety in transport systems

openDuring driving, driver behavior monitoring may provide useful information to prevent road traffic accidents caused by driver distraction. It has been shown that 90% of road traffic accidents are due to human error and in 75% of these cases human error is the only cause. Car manufacturers have been interested in driver monitoring research for several years, aiming to enhance the general knowledge of driver behavior and to evaluate the functional state as it may drastically influence driving safety by distraction, fatigue, mental workload and attention. Fatigue and sleepiness at the wheel are well known risk factors for traffic accidents. The Human Factor (HF) plays a fundamental role in modern transport systems. Drivers and transport operators control a vehicle towards its destination in according to their own sense, physical condition, experience and ability, and safety strongly relies on the HF which has to take the right decisions. On the other hand, we are experiencing a gradual shift towards increasingly autonomous vehicles where HF still constitutes an important component, but may in fact become the "weakest link of the chain", requiring strong and effective training feedback. The studies that investigate the possibility to use biometrical or biophysical signals as data sources to evaluate the interaction between human brain activity and an electronic machine relate to the Human Machine Interface (HMI) framework. The HMI can acquire human signals to analyse the specific embedded structures and recognize the behavior of the subject during his/her interaction with the machine or with virtual interfaces as PCs or other communication systems. Based on my previous experience related to planning and monitoring of hazardous material transport, this work aims to create control models focused on driver behavior and changes of his/her physiological parameters. Three case studies have been considered using the interaction between an EEG system and external device, such as driving simulators or electronical components. A case study relates to the detection of the driver's behavior during a test driver. Another case study relates to the detection of driver's arm movements according to the data from the EEG during a driver test. The third case is the setting up of a Brain Computer Interface (BCI) model able to detect head movements in human participants by EEG signal and to control an electronic component according to the electrical brain activity due to head turning movements. Some videos showing the experimental results are available at https://www.youtube.com/channel/UCj55jjBwMTptBd2wcQMT2tg.openXXXIV CICLO - INFORMATICA E INGEGNERIA DEI SISTEMI/ COMPUTER SCIENCE AND SYSTEMS ENGINEERING - Ingegneria dei sistemiZero, Enric

Enhancing precision in human neuroscience

Human neuroscience has always been pushing the boundary of what is measurable. During the last decade, concerns about statistical power and replicability - in science in general, but also specifically in human neuroscience - have fueled an extensive debate. One important insight from this discourse is the need for larger samples, which naturally increases statistical power. An alternative is to increase the precision of measurements, which is the focus of this review. This option is often overlooked, even though statistical power benefits from increasing precision as much as from increasing sample size. Nonetheless, precision has always been at the heart of good scientific practice in human neuroscience, with researchers relying on lab traditions or rules of thumb to ensure sufficient precision for their studies. In this review, we encourage a more systematic approach to precision. We start by introducing measurement precision and its importance for well-powered studies in human neuroscience. Then, determinants for precision in a range of neuroscientific methods (MRI, M/EEG, EDA, Eye-Tracking, and Endocrinology) are elaborated. We end by discussing how a more systematic evaluation of precision and the application of respective insights can lead to an increase in reproducibility in human neuroscience