Detection of EEG transients in neonates and older children using a system based on dynamic time-warping template matching and spatial dipole clustering

Dynamic Time Warping Identifies Functionally Distinct fMRI Resting State Cortical Networks Specific to VTA and SNc: A Proof of Concept

Functional connectivity (FC) is determined by similarity between functional magnetic resonance imaging (fMRI) signals from distinct brain regions. However, traditional FC analyses ignore temporal phase differences. Here, we addressed this limitation, using dynamic time warping (DTW) within a machine-learning framework, to study cortical FC patterns of 2 spatially adjacent but functionally distinct subcortical regions, namely Substantia Nigra Pars Compacta (SNc) and ventral tegmental area (VTA). We evaluate: 1) the influence of pair of brain regions considered, 2) the influence of warping window sizes, 3) the classification efficacy of DTW, and 4) the uniqueness of features identified. Whole brain 7 Tesla resting state fMRI scans from 81 healthy participants were used. FC between 2 subcortical regions of interests (ROIs) and 360 cortical parcels were computed using: 1) Pearson correlations (PCs), 2) dynamic time-warped PCs (DTW-PC). The separability of SNc-cortical and VTA-cortical network was validated on 40 participants and tested on the remaining 41, using a support vector machine (SVM). The SVM separated the SNc-cortical versus VTA-cortical network with 74.39 and 97.56% test accuracy using PC and DTW-PC, respectively. SVM-recursive feature elimination yielded 20 DTW-PC features that most strongly contributed to the separation of the networks and revealed novel VTA versus SNc preferential connections (P < 0.05, Bonferroni-Holm corrected).

A comprehensive study on electroencephalography and magnetoencephalography sensitivity to cortical and subcortical sources

Signal-to-noise ratio (SNR) maps are a good way to visualize electroencephalography (EEG) and magnetoencephalography (MEG) sensitivity. SNR maps extend the knowledge about the modulation of EEG and MEG signals by source locations and orientations and can therefore help to better understand and interpret measured signals as well as source reconstruction results thereof. Our work has two main objectives. First, we investigated the accuracy and reliability of EEG and MEG finite element method (FEM)-based sensitivity maps for three different head models, namely an isotropic three and four-compartment and an anisotropic six-compartment head model. As a result, we found that ignoring the cerebrospinal fluid leads to an overestimation of EEG SNR values. Second, we examined and compared EEG and MEG SNR mappings for both cortical and subcortical sources and their modulation by source location and orientation. Our results for cortical sources show that EEG sensitivity is higher for radial and deep sources and MEG for tangential ones, which are the majority of sources. As to the subcortical sources, we found that deep sources with sufficient tangential source orientation are recordable by the MEG. Our work, which represents the first comprehensive study where cortical and subcortical sources are considered in highly detailed FEM-based EEG and MEG SNR mappings, sheds a new light on the sensitivity of EEG and MEG and might influence the decision of brain researchers or clinicians in their choice of the best modality for their experiment or diagnostics, respectively.

VME-DWT: An Efficient Algorithm for Detection and Elimination of Eye Blink From Short Segments of Single EEG Channel

OBJECTIVE

Recent advances in development of low-cost single-channel electroencephalography (EEG) headbands have opened new possibilities for applications in health monitoring and brain-computer interface (BCI) systems. These recorded EEG signals, however, are often contaminated by eye blink artifacts that can yield the fallacious interpretation of the brain activity. This paper proposes an efficient algorithm, VME-DWT, to remove eye blinks in a short segment of the single EEG channel.

METHOD

The proposed algorithm: (a) locates eye blink intervals using Variational Mode Extraction (VME) and (b) filters only contaminated EEG interval using an automatic Discrete Wavelet Transform (DWT) algorithm. The performance of VME-DWT is compared with an automatic Variational Mode Decomposition (AVMD) and a DWT-based algorithms, proposed for suppressing eye blinks in a short segment of the single EEG channel.

RESULTS

The VME-DWT detects and filters 95% of the eye blinks from the contaminated EEG signals with SNR ranging from -8 to +3 dB. The VME-DWT shows superiority to the AVMD and DWT with the higher mean value of correlation coefficient (0.92 vs. 0.83, 0.58) and lower mean value of RRMSE (0.42 vs. 0.59, 0.87).

SIGNIFICANCE

The VME-DWT can be a suitable algorithm for removal of eye blinks in low-cost single-channel EEG systems as it is: (a) computationally-efficient, the contaminated EEG signal is filtered in millisecond time resolution, (b) automatic, no human intervention is required, (c) low-invasive, EEG intervals without contamination remained unaltered, and (d) low-complexity, without need to the artifact reference.

Evaluation of Different Signal Processing Methods in Time and Frequency Domain for Brain-Computer Interface Applications

Brain-computer interface (BCI) has been widely introduced in many medical applications. One of the main challenges in BCI is to run the signal processing algorithms in real-time which is challenging and usually comes with high processing unit costs. BCIs based on motor imagery task are introduced for severe neurological diseases especially locked-in patients. A common concept is to detect one's movement intention and use it to control external devices such as wheelchair or rehabilitation devices. In real-time BCI, running the signal processing algorithms might not always be possible due to the complexity of the algorithms. Moreover, the speed of the affordable computational units is not usually enough for those applications. This study evaluated a range of feature extraction methods which are commonly used for such real-time BCI applications. Electroencephalogram (EEG) and Electrooculogram (EOG) data available through IEEE Brain Initiative repository was used to investigate the performance of different feature extraction methods including template matching, statistical moments, selective bandpower, and fast Fourier transform (FFT) power spectrum. The support vector machine (SVM) was used for classification. The result indicates that there is not a significant difference when utilizing different feature extraction methods in terms of movement prediction although there is a vast difference in the computational time needed to extract these features. The results suggest that computational time could be considered as the primary parameter when choosing the feature extraction methods as there is no significant difference between the results when different features extraction methods are used.

Electrooculograms for Human-Computer Interaction: A Review

Eye movements generate electric signals, which a user can employ to control his/her environment and communicate with others. This paper presents a review of previous studies on such electric signals, that is, electrooculograms (EOGs), from the perspective of human–computer interaction (HCI). EOGs represent one of the easiest means to estimate eye movements by using a low-cost device, and have been often considered and utilized for HCI applications, such as to facilitate typing on a virtual keyboard, moving a mouse, or controlling a wheelchair. The objective of this study is to summarize the experimental procedures of previous studies and provide a guide for researchers interested in this field. In this work the basic characteristics of EOGs, associated measurements, and signal processing and pattern recognition algorithms are briefly reviewed, and various applications reported in the existing literature are listed. It is expected that EOGs will be a useful source of communication in virtual reality environments, and can act as a valuable communication tools for people with amyotrophic lateral sclerosis.

EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges

Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those using motor-imagery (MI) data, have the potential to become groundbreaking technologies in both clinical and entertainment settings. MI data is generated when a subject imagines the movement of a limb. This paper reviews state-of-the-art signal processing techniques for MI EEG-based BCIs, with a particular focus on the feature extraction, feature selection and classification techniques used. It also summarizes the main applications of EEG-based BCIs, particularly those based on MI data, and finally presents a detailed discussion of the most prevalent challenges impeding the development and commercialization of EEG-based BCIs.

Altered electroencephalography functional connectivity in depression during the emotional face-word Stroop task

OBJECTIVE

Depression is a severe mental disorder. However, the neural mechanisms underlying affective interference (difficulties in directing attention away from negative distractors) in depression patients are still not well-understood. In particular, the connections between brain regions remain unclear. Using the emotional face-word Stroop task, we aimed to reveal the altered electroencephalography (EEG) functional connectivity in patients with depression, using concepts from event-related potentials (ERPs) and time series clustering.

APPROACH

In this study, the EEG signals of ten healthy participants and ten depression patients were collected from a 64-sensor cap. Subsequently, EEG signals were segmented into temporal windows corresponding to the ERPs. For each duration, the dynamic time warping algorithm was used to calculate the similarities between EEG signals from different electrodes, and differences of these similarities were compared between the groups. Finally, hierarchical clustering was used to identify functionally connected regions and examine changes in depression.

MAIN RESULTS

It was observed that during the time interval of 400-600 ms (N450 components), depression patients had more long-range connections than did healthy control patients and exhibited abnormal functional connectivity via the superior and middle frontal gyrus, specifically, the dorsolateral prefrontal cortex (DL-PFC, Brodmann's area 8 and 9), which is related to the control and resolution of affective interference. Moreover, the functionally connected region of depression patients was much larger than that of healthy participants, which is caused by brain resource reorganization.

SIGNIFICANCE

These findings thus provide new insights into the neural mechanisms of depression and further identify the DL-PFC and connections between certain electrodes as quantitative indicators of depression.

Effect of confounding variables on hemodynamic response function estimation using averaging and deconvolution analysis: An event-related NIRS study

Slow and rapid event-related designs are used in fMRI and functional near-infrared spectroscopy (fNIRS) experiments to temporally characterize the brain hemodynamic response to discrete events. Conventional averaging (CA) and the deconvolution method (DM) are the two techniques commonly used to estimate the Hemodynamic Response Function (HRF) profile in event-related designs. In this study, we conducted a series of simulations using synthetic and real NIRS data to examine the effect of the main confounding factors, including event sequence timing parameters, different types of noise, signal-to-noise ratio (SNR), temporal autocorrelation and temporal filtering on the performance of these techniques in slow and rapid event-related designs. We also compared systematic errors in the estimates of the fitted HRF amplitude, latency and duration for both techniques. We further compared the performance of deconvolution methods based on Finite Impulse Response (FIR) basis functions and gamma basis sets. Our results demonstrate that DM was much less sensitive to confounding factors than CA. Event timing was the main parameter largely affecting the accuracy of CA. In slow event-related designs, deconvolution methods provided similar results to those obtained by CA. In rapid event-related designs, our results showed that DM outperformed CA for all SNR, especially above -5 dB regardless of the event sequence timing and the dynamics of background NIRS activity. Our results also show that periodic low-frequency systemic hemodynamic fluctuations as well as phase-locked noise can markedly obscure hemodynamic evoked responses. Temporal autocorrelation also affected the performance of both techniques by inducing distortions in the time profile of the estimated hemodynamic response with inflated t-statistics, especially at low SNRs. We also found that high-pass temporal filtering could substantially affect the performance of both techniques by removing the low-frequency components of HRF profiles. Our results emphasize the importance of characterization of event timing, background noise and SNR when estimating HRF profiles using CA and DM in event-related designs.

Seizure detection using dynamic warping for patients with intellectual disability

Electroencephalography (EEG) is paramount for both retrospective analysis and real-time monitoring of epileptic seizures. Studies have shown that EEG-based seizure detection is very difficult for a specific epileptic population with intellectual disability due to the cerebral development disorders. In this work, a seizure detection method based on dynamic warping (DW) is proposed for patients with intellectual disability. It uses an EEG template of an individual subject's dominant seizure type, to extract the morphological features from EEG signals. A linear discriminant analysis (LDA) classifier is used to perform the seizure detection. Results show that the DW-based feature in the frequency domain is superior than that in the time domain, and the features extracted using wavelet transform method.

Automatic Identification of Interictal Epileptiform Discharges in Secondary Generalized Epilepsy

Ictal epileptiform discharges (EDs) are characteristic signal patterns of scalp electroencephalogram (EEG) or intracranial EEG (iEEG) recorded from patients with epilepsy, which assist with the diagnosis and characterization of various types of epilepsy. The EEG signal, however, is often recorded from patients with epilepsy for a long period of time, and thus detection and identification of EDs have been a burden on medical doctors. This paper proposes a new method for automatic identification of two types of EDs, repeated sharp-waves (sharps), and runs of sharp-and-slow-waves (SSWs), which helps to pinpoint epileptogenic foci in secondary generalized epilepsy such as Lennox-Gastaut syndrome (LGS). In the experiments with iEEG data acquired from a patient with LGS, our proposed method detected EDs with an accuracy of 93.76% and classified three different signal patterns with a mean classification accuracy of 87.69%, which was significantly higher than that of a conventional wavelet-based method. Our study shows that it is possible to successfully detect and discriminate sharps and SSWs from background EEG activity using our proposed method.

An unsupervised eye blink artifact detection method for real-time electroencephalogram processing

Electroencephalogram (EEG) is easily contaminated by unwanted physiological artifacts, among which electrooculogram (EOG) artifacts due to eye blinking are known to be most dominant. The eye blink artifacts are reported to affect theta and alpha rhythms of frontal EEG signals, and hard to be accurately detected in an unsupervised way due to large individual variability. In this study, we propose a new method for detecting eye blink artifacts automatically in real time without using any labeled training data. The proposed method combined our previous method for detecting eye blink artifacts based on digital filters with an automatic thresholding algorithm. The proposed method was evaluated using EEG data acquired from 24 participants. Two conventional algorithms were implemented and their performances were compared with that of the proposed method. The main contributions of this study are (1) confirming that individual thresholding is necessary for artifact detection, (2) proposing a novel algorithm structure to detect blink artifacts in a real-time environment without any a priori knowledge, and (3) demonstrating that the length of training data can be minimized through the use of a real-time adaption procedure.

Detection of eye blink artifacts from single prefrontal channel electroencephalogram

Eye blinks are one of the most influential artifact sources in electroencephalogram (EEG) recorded from frontal channels, and thereby detecting and rejecting eye blink artifacts is regarded as an essential procedure for improving the quality of EEG data. In this paper, a novel method to detect eye blink artifacts from a single-channel frontal EEG signal was proposed by combining digital filters with a rule-based decision system, and its performance was validated using an EEG dataset recorded from 24 healthy participants. The proposed method has two main advantages over the conventional methods. First, it uses single-channel EEG data without the need for electrooculogram references. Therefore, this method could be particularly useful in brain-computer interface applications using headband-type wearable EEG devices with a few frontal EEG channels. Second, this method could estimate the ranges of eye blink artifacts accurately. Our experimental results demonstrated that the artifact range estimated using our method was more accurate than that from the conventional methods, and thus, the overall accuracy of detecting epochs contaminated by eye blink artifacts was markedly increased as compared to conventional methods. The MATLAB package of our library source codes and sample data, named Eyeblink Master, is open for free download.

Amplitude spectral analyses of disorganized patterns on electroencephalograms in preterm infants

The aim of this study is to clarify the differences of EEG activities according to the presence or absence of disorganized patterns using amplitude spectral analysis. We compared EEGs of 17 preterm infants with disorganized patterns with those of 34 matched controls. Amplitude was defined as a square root of EEG power analyzed by fast Fourier transform algorithm, and was calculated in the 9 frequency bands. Six EEG segments of 10 s were collected from the part of EEG with continuous high voltage slow waves in the absence of artifacts. The results were separately evaluated according to the post-conceptional age at EEG recordings. In patients with disorganized patterns, reduced amplitude of delta waves in the central areas and increased amplitude of beta waves in the occipital areas were observed at 29-30 weeks of post-conceptional age. The results were almost similar at 31-32 weeks of post-conceptional age. Amplitude in theta or alpha frequency bands was not different between those with and without disorganized patterns either at 29-30 or 31-32 weeks of post-conceptional age. Amplitude spectral analyses will contribute to objective evaluation of disorganized patterns.

Kalman filter-based time-varying cortical connectivity analysis of newborn EEG

Multivariate Granger causality in the time-frequency domain as a representation of time-varying cortical connectivity in the brain has been investigated for the adult case. This is, however, not the case in newborns as the nature of the transient changes in the newborn EEG is different from that of adults. This paper aims to evaluate the performance of the time-varying versions of the two popular Granger causality measures, namely Partial Directed Coherence (PDC) and direct Directed Transfer Function (dDTF). The parameters of the time-varying AR, that models the inter-channel interactions, are estimated using Dual Extended Kalman Filter (DEKF) as it accounts for both non-stationarity and non-linearity behaviors of the EEG. Using simulated data, we show that fast changing cortical connectivity between channels can be measured more accurately using the time-varying PDC. The performance of the time-varying PDC is also tested on a neonatal EEG exhibiting seizure.

Automatic detection and analysis of the EEG sharp wave-slow wave patterns evoked by fluorinated inhalation anesthetics

OBJECTIVE

The aim of this study was to develop a method for the automatic detection of sharp wave-slow wave (SWSW) patterns evoked in EEG by volatile anesthetics and to identify the patterns' characteristics.

METHODS

The proposed method consisted in the k-NN classification with a reference set obtained using expert knowledge, the morphology of the EEG patterns and the condition for their synchronization. The decision rules were constructed and evaluated using 24h EEG records in ten patients.

RESULTS

The sensitivity, specificity and selectivity of the method were 0.88 ± 0.10, 0.81 ± 0.13 and 0.42 ± 0.16, respectively. SWSW patterns' recruitment was strictly dependent on anesthetic concentration. SWSW patterns evoked by different types of anesthetics expressed different characteristics.

CONCLUSIONS

Synchronization criterion and adequately selected morphological features of "slow wave" were sufficient to achieve the high sensitivity and specificity of the method.

SIGNIFICANCE

The monitoring of SWSW patterns is important in view of possible side effects of volatile anesthetics. The analysis of SWSW patterns' recruitment and morphology could be helpful in the diagnosis of the anesthesia effects on the CNS.