Artifact-free EEG Signals Research Articles

On the Minimal Amount of EEG Data Required for Learning Distinctive Human Features for Task-Dependent Biometric Applications.

Biometrics is the process of measuring and analyzing human characteristics to verify a given person's identity. Most real-world applications rely on unique human traits such as fingerprints or iris. However, among these unique human characteristics for biometrics, the use of Electroencephalogram (EEG) stands out given its high inter-subject variability. Recent advances in Deep Learning and a deeper understanding of EEG processing methods have led to the development of models that accurately discriminate unique individuals. However, it is still uncertain how much EEG data is required to train such models. This work aims at determining the minimal amount of training data required to develop a robust EEG-based biometric model (+95% and +99% testing accuracies) from a subject for a task-dependent task. This goal is achieved by performing and analyzing 11,780 combinations of training sizes, by employing various neural network-based learning techniques of increasing complexity, and feature extraction methods on the affective EEG-based DEAP dataset. Findings suggest that if Power Spectral Density or Wavelet Energy features are extracted from the artifact-free EEG signal, 1 and 3 s of data per subject is enough to achieve +95% and +99% accuracy, respectively. These findings contributes to the body of knowledge by paving a way for the application of EEG to real-world ecological biometric applications and by demonstrating methods to learn the minimal amount of data required for such applications.

Cardiac Artifact Noise Removal From Sleep EEG Signals Using Hybrid Denoising Model

Sleep is one of the prime natural activities for human well-being in physical, emotional, and mental aspects. The assessment of sleep Electroencephalography (EEG) signals is required to diagnose sleep-related neurological disorders. It is found that sleep EEG signals are extremely vulnerable to highly energetic electrocardiogram (ECG) signals. The intermixing of ECG into EEG, commonly known as cardiac artifacts, might severely affect the sleep EEG data. In order to have artifact-free EEG signal, a hybrid signal denoising methodology which includes empirical wavelet transforms (EWT), adaptive threshold-based nonlinear Teager-Kaiser energy operator (TEO), and customized morphological filter in accompanying with modified ensemble average subtraction (MEAS) is proposed for automatic detection and suppression of cardiac artifact from a single-channel EEG. The efficacy of the proposed methodology presented in the paper has been evaluated over standard public datasets such as CinC Challenge 2014 dataset (synthetic), and MIT-BIH polysomnography data (clinical). It has been observed that the proposed method outperforms other state-of-the-art automated EEG artifact elimination methods in terms of few popular denoising performance indexes such as signal to artifact ratio, percentage root mean square difference, percentage distortion in power spectral density, structural similarity index measure, and execution time. The proposed method is robust, time-efficient, and preserves the majority of EEG data with minimal loss, making it suitable for neuro clinical EEG analysis.

Automatic Eyeblink Artifact Removal From EEG Signal Using Wavelet Transform With Heuristically Optimized Threshold.

This paper proposes an automatic eyeblink artifacts removal method from corrupted-EEG signals using discrete wavelet transform (DWT) and meta-heuristically optimized threshold. The novel idea of thresholding approximation-coefficients (ACs) instead of detail-coefficients (DCs) of DWT of EEG in a backward manner is proposed for the first time for the removal of eyeblink artifacts. EEG is very sensitive and easily gets affected by eyeblink artifacts. First, the eyeblink corrupted EEG signals are identified using support vector machine (SVM) as a classifier. Then the corrupted EEG signal is decomposed using DWT up to the sixth level. Both the mother wavelet and the level of decomposition are selected using appropriate techniques. Then the ACs are thresholded in backward manner using the optimum threshold values followed by inverse DWT operation to reconstruct the original EEG signal. The AC at level 6 is thresholded and is used in IDWT with DC to get back the AC at level 5. Likewise, the backward thresholding of the ACs followed by IDWT is continued till the artifact free EEG signal is reconstructed at level 1. The optimum values of the thresholds of the ACs at different levels are optimized using two meta-heuristic algorithms, particle swarm optimization (PSO) and grey wolf optimization (GWO) for comparison. The results reveal that the proposed methodology is superior to the recently reported methods in terms of average correlation coefficient (CC) which states that the proposed method is better in terms of the quality of reconstruction in addition to being fully automatic.

Automatic Artifact Removal in EEG of Normal and Demented Individuals Using ICA-WT during Working Memory Tasks.

Characterizing dementia is a global challenge in supporting personalized health care. The electroencephalogram (EEG) is a promising tool to support the diagnosis and evaluation of abnormalities in the human brain. The EEG sensors record the brain activity directly with excellent time resolution. In this study, EEG sensor with 19 electrodes were used to test the background activities of the brains of five vascular dementia (VaD), 15 stroke-related patients with mild cognitive impairment (MCI), and 15 healthy subjects during a working memory (WM) task. The objective of this study is twofold. First, it aims to enhance the recorded EEG signals using a novel technique that combines automatic independent component analysis (AICA) and wavelet transform (WT), that is, the AICA–WT technique; second, it aims to extract and investigate the spectral features that characterize the post-stroke dementia patients compared to the control subjects. The proposed AICA–WT technique is a four-stage approach. In the first stage, the independent components (ICs) were estimated. In the second stage, three-step artifact identification metrics were applied to detect the artifactual components. The components identified as artifacts were marked as critical and denoised through DWT in the third stage. In the fourth stage, the corrected ICs were reconstructed to obtain artifact-free EEG signals. The performance of the proposed AICA–WT technique was compared with those of two other techniques based on AICA and WT denoising methods using cross-correlation and peak signal to noise ratio (ANOVA, ˂ 0.05). The AICA–WT technique exhibited the best artifact removal performance. The assumption that there would be a deceleration of EEG dominant frequencies in VaD and MCI patients compared with control subjects was assessed with AICA–WT (ANOVA, ˂ 0.05). Therefore, this study may provide information on post-stroke dementia particularly VaD and stroke-related MCI patients through spectral analysis of EEG background activities that can help to provide useful diagnostic indexes by using EEG signal processing.

Power Spectral Analysis of EEG as a Potential Marker in the Diagnosis of Spastic Cerebralpalsycases

The detection and diagnosis of various neurological disorders are performed using different medical devices among which electroencephalogram (EEG) is one of the most cost effective technique. Though significant progress had been made in the analysis of EEG for diagnosis of different neurological disorders, yet detection of cerebral palsy (CP) is not quite clear. This study was performed to analyze the EEG power spectrum density (PSD) of spastic CP and normal children to find if any significant EEG patterns could be used for early detection of CP. Twenty children participated in this study out of which ten were spastic CP and other ten were normal healthy children. EEG of all the participants was recorded from C3 C4 and F3 F4 regions following montage 10-20 system. The artifact-free EEG signals of 15 minutes duration was extracted for spectral analysis using Fast Fourier Transformation (FFT) algorithm in MATLAB and power density spectrum (PSD) was plotted. The PSD revealed high intensity power peak at frequency of 50Hz and smaller at 100 Hz, which was consistent for all healthy subjects. In case of spastic CP children, high intensity peak at 100Hz were prominent and smaller peak was observed at 50Hz. The high intensity 100Hz peak observed in the PSD of spastic CP patients demonstrated that this tool can be used for early detection of spastic CP.

Excision of Ocular Artifacts from EEG Using NVFF-RLS Adaptive Algorithm

This paper presents a technique for the removal of ocular artifacts from electro-encephalogram (EEG) by using adaptive filtering. The major concern is electro-oculogram (EOG) signal present in a recorded EEG signal, which appears due to abrupt eye movements. In the presented method, we use separately recorded horizontal EOG (HEOG) and vertical EOG (VEOG) signals as two reference inputs, which are processed using finite impulse response (FIR) filters. The linear filter coefficients are adaptively updated using a numerical variable forgetting factor (NVFF) recursive least squares (RLS) algorithm, which tracks nonstationary EOG signals. Subsequently, the processed HEOG and VEOG signals are subtracted from recorded EEG signal to obtain an artifact-free EEG signal. Simulation is conducted using synthetic EEG signal corrupted by noise, synthetic HEOG and VEOG signals. The real-time recorded EEG signal (corrupted by EOG and noise) is also refined using the separately recorded reference EOG signals and FIR filtering technique. For synthetic and real-time signals, the simulation results are presented to demonstrate that linear NVFF-RLS algorithm-based artifact and noise excision technique outperforms conventional fixed forgetting factor RLS, fixed step-size NLMS and generalized variable step-size NLMS algorithms, in terms of the reduction in mean-squared error, under low as well as high signal-to-noise ratio conditions.

Study on Performance Metrics for Consideration of Efficiency of the Ocular Artifact Removal Algorithms for EEG Signals

A major challenge related to the removal of Ocular Artifacts (OA) from Electroencephalogram (EEG) signals is the lack of a consensus about one superior standard OA removal algorithm among all, due to unavailability of both natural artifactfree EEG signal and a standard performance evaluation criterion. The present paper discusses the various validated performance criteria often used in research papers for considering the efficiency of the OA removal algorithms. These metrics can be measured in the MATLAB software. In addition to natural EEG signals, artificial or simulated signals are also considered in this study. Some of these performance criteria are commonly used for both real and simulated signals, while others are appropriate only for one of them specifically. However, evaluation of a simulated signal can be easier than a real one because of the availability of artifact-free EEG signals in a simulation study, evaluation using a real signal is more trustworthy than simulated one. Hence, for more reliable and precise efficiency considering of an OA removal algorithm, employing both signals, is recommended. Further, because of the non-stationary nature of real EEG signals, the comparison of an algorithm with others, via implementing them on the same signals, will be meaningful and applicable. The present work will help researchers in recording the efficiency of their algorithms, as well as comparing the performance of their methods with others for reaching a consensus in OA removal.

Coupling between gamma oscillation and fMRI signal in the rat somatosensory cortex: Its dependence on systemic physiological parameters

The simultaneous recordings of neuronal and hemodynamic signals have revealed a significant involvement of high frequency bands (e.g., gamma range, 25-70 Hz) in neurovascular coupling. However, the dependence on a physiological parameter is unknown. In this study, we performed simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) recordings in 12 Wistar rats using a conventional forepaw stimulation paradigm and concurrently monitored the systemic physiological parameters of the partial pressure of arterial oxygen, partial pressure of arterial carbon dioxide, pH, mean arterial blood pressure, and heart rate through the rat femoral artery. The high frequency bands in the artifact-free EEG signals, especially those in the gamma range, demonstrated a maximum correlation with fMRI signals in the rat somatosensory cortex. A multiple linear regression analysis demonstrated that the correlation coefficient between the gamma power and fMRI signal depended on the actual values of the physiological parameters (R(2)=0.20, p<0.05), whereas the gamma power and fMRI signal by itself were independent. Among the parameters, the heart rate had a statistically significant slope (95% CI: 0.00027-0.0016, p<0.01) in a multiple linear regression model. These results indicate that neurovascular coupling is mainly driven by gamma oscillations, as expected, but coupling or potential decoupling is strongly influenced by systemic physiological parameters, which dynamically reflect the baseline vital status of the subject.

REG-ICA: A hybrid methodology combining Blind Source Separation and regression techniques for the rejection of ocular artifacts

There are so far two main methodological approaches for rejecting ocular artifacts from electroencephalographic (EEG) and magnetoencephalographic (MEG) signals: regression- and Blind Source Separation (BSS)-based techniques, both having merits, as well as, some serious limitations. In this piece of work, a hybrid methodology that combines the main advantages of these two methods is proposed. We hypothesize that the artifactual independent components (ICs) extracted by a BSS method include more ocular and less cerebral activity than the contaminated EEG signals. We thus propose to apply a regression algorithm to the ICs rather than directly to the recorded signals. The analysis was carried out with synthetic mixtures of real EEG and electroocculographic (EOG) recordings. A BSS method, the extended INFOMAX version of Independent Component Analysis (ICA), was initially used to decompose the artificially contaminated EEG signals into spatiotemporal ICs. Then, a regression scheme, based on a stable version of the Recursive Least Squares algorithm, sRLS, was applied to the artifactual components in order to remove only the ocular artifacts, maintaining the underlying neural signals intact. The processed ICs were then projected back, reconstructing the artifact-free EEG signals. The performance of the proposed technique was compared with two automatic techniques; a regression technique based on Least Mean Square (LMS) algorithm and a BSS-based artifact rejection technique called wavelet-ICA (W-ICA) on the artificially contaminated data. For comparison, two metrics were used to assess the different methods’ performance: the first quantified how successful each technique was in removing the ocular artifacts from the EEG recordings, and the second one quantified how much each technique distorted the ongoing brain activity in both time and frequency domains. Confirming our main hypothesis, results have shown that the artifactual ICs contained more ocular and less cerebral activity ( p < 0.04) (artifact to signal ratio (ASR) = 1.83 ± 3.65) in contrast to the contaminated electrode signals (ASR = 0.69 ± 3.40). Our results reveal that the proposed methodology, namely REG-ICA, removes the ocular artifacts more successfully than W-ICA ( p < 0.01) or LMS ( p < 0.01). It also distorts less the brain activity in the time domain when compared to W-ICA and LMS. In the frequency domain, it distorts the brain activity less than the W-ICA in all frequency bands, and less than the LMS for the delta, beta, and gamma bands. Our results suggest that the proposed methodology is evidently an attractive alternative to other already proposed artifact rejection methodologies.

Cortisol and Reduced Interhemispheric Coupling Between the Left Prefrontal and the Right Parietal Cortex

Back to table of contents Previous article Next article LetterFull AccessCortisol and Reduced Interhemispheric Coupling Between the Left Prefrontal and the Right Parietal CortexDennis J.L.G. Schutter, M.A., Jack Van Honk, Ph.D., Hans Koppeschaar, Ph.D., and RenË Kahn, M.D., Ph.D., Dennis J.L.G. SchutterSearch for more papers by this author, M.A., Jack Van HonkSearch for more papers by this author, Ph.D., Hans KoppeschaarSearch for more papers by this author, Ph.D., and RenË KahnSearch for more papers by this author, M.D., Ph.D., Affective Neuroscience Section, Helmholtz Research Institute (d.s., j.v.h.); Department of Endocrinology, University Medical Center (h.k.); Department of Psychiatry, University Medical Center (r.k.), Utrecht, NetherlandsPublished Online:1 Feb 2002AboutSectionsView EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InEmail SIR: Neuroimaging and electrophysiological studies have demonstrated a relationship between hypoactivation of the left prefrontal cortex and depression.1 There is also strong evidence for the involvement of the right parietal cortex in this unipolar disorder.Davidson1 proposes an interhemispheric circuit consisting of the left prefrontal and right parietal area to be dysfunctional in depression. Depression is often accompanied by defective feedback of the hypothalamic-pituitary-adrenal axis, resulting in elevated levels of the steroid hormone cortisol.2Interestingly, steroid hormones are involved in interhemispheric transmission.3 A valid method for quantifying interhemispheric transmission or functional connectivity is EEG spectral coherence analysis, which measures phase consistency at paired locations.4 A reliable measure for cortisol reaching the target tissues in the brain can be taken from saliva.5 We investigated the relationship between salivary cortisol and EEG spectral coherence over the left prefrontal and the right parietal cortex.Thirty nonclinical, right-handed subjects (15 females) ages 20 to 28 years participated. Written informed consent was obtained. To control for circadian hormonal rhythms, the sessions were conducted between 1:30 p.m. and 4:30 p.m. Salivary sampling was followed by a 10-minute EEG baseline recording from the F3, F4, P3, P4 electrodes, which were referenced to the right mastoid. Cortisol levels were determined by radioimmunoassay. Baseline cortisol did not (P<0.25) and normally does not differ between the sexes.5 Artifact-free EEG signal (bandpass filter: 1–30 Hz) was extracted through a Hamming window, and a fast Fourier transformation was used to derive estimates of spectral power (μV2). Squared correlation coefficients (coherences) were determined for the four electrodes in the 4–7 Hz (θ), 8–13 Hz (α), and 14–30 Hz (β) frequency domains.Correlational analyses showed significant inverse relations between cortisol and the F3–P4 coherence in the fast α (r=–0.43, P<0.01) and β (r=–0.39, P<0.03) EEG frequency bands. Thus, already in this nonclinical subject group, higher cortisol was accompanied by reductions in interhemispheric transmission between the left prefrontal and right parietal cortex. Our data might suggest a sensitive interhemispheric decoupling mechanism by which pathological levels of the steroid hormone cortisol could be implicated in depression.References1 Davidson RJ, Henriques J: Regional brain function in sadness and depression, in The Neuropsychology of Emotion, edited by Borod JC. New York, Oxford University Press, 2000, pp 269-297Google Scholar2 Holsboer F: The corticosteroid receptor hypothesis of depression. Neuropsychopharmacology 2000; 23:477-501Crossref, Medline, Google Scholar3 Hausmann M, Güntürkün O: Steroid fluctuations modify functional asymmetries: the hypothesis of progesterone-mediated interhemispheric decoupling. Neuropsychologia 2000; 38:1352-1374Crossref, Google Scholar4 Nunez PL: Toward a quantitative description of large-scale neocortical dynamic function and EEG. Behav Brain Sci 2000; 23:371-437Crossref, Medline, Google Scholar5 Kirschbaum C, Kudielka BM, Gaab J, et al: Impact of gender, menstrual cycle phase, and oral contraceptives on the activity of the hypothalamus-pituitary-adrenal axis. Psychosom Med 1999; 61:154-162Crossref, Medline, Google Scholar FiguresReferencesCited byDetailsCited ByQuantitative Electroencephalogram (qEEG) as a Natural and Non-Invasive Window into Living Brain and Mind in the Functional Continuum of Healthy and Pathological Conditions23 September 2022 | Applied Sciences, Vol. 12, No. 19Administration of Testosterone Increases Functional Connectivity in a Cortico-Cortical Depression CircuitDennis J.L.G. Schutter, Ph.D., Jiska S. Peper, M.S., Hans P.F. Koppeschaar, M.D., Ph.D., René S. Kahn, M.D., Ph.D., and Jack van Honk, Ph.D.1 August 2005 | The Journal of Neuropsychiatry and Clinical Neurosciences, Vol. 17, No. 3Acute Dystonia Caused by Low Dosage of OlanzapineBasil Alevizos, M.D., Charalabos Papageorgiou, M.D., and George N. Christodoulou, M.D., 1 May 2003 | The Journal of Neuropsychiatry and Clinical Neurosciences, Vol. 15, No. 2 Volume 14Issue 1 February 2002Pages 89-90 Metrics History Published online 1 February 2002 Published in print 1 February 2002