EEG Data In Order Research Articles

Tai Chi Practice Buffers Aging Effects in Functional Brain Connectivity.

Tai Chi (TC) practice has been shown to improve both cognitive and physical function in older adults. However, the neural mechanisms underlying the benefits of TC remain unclear. Our primary aims are to explore whether distinct age-related and TC-practice-related relationships can be identified with respect to either temporal or spatial (within/between-network connectivity) differences. This cross-sectional study examined recurrent neural network dynamics, employing an adaptive, data-driven thresholding approach to source-localized resting-state EEG data in order to identify meaningful connections across time-varying graphs, using both temporal and spatial features derived from a hidden Markov model (HMM). Mann-Whitney U tests assessed between-group differences in temporal and spatial features by age and TC practice using either healthy younger adult controls (YACs, n = 15), healthy older adult controls (OACs, n = 15), or Tai Chi older adult practitioners (TCOAs, n = 15). Our results showed that aging is associated with decreased within-network and between-network functional connectivity (FC) across most brain networks. Conversely, TC practice appears to mitigate these age-related declines, showing increased FC within and between networks in older adults who practice TC compared to non-practicing older adults. These findings suggest that TC practice may abate age-related declines in neural network efficiency and stability, highlighting its potential as a non-pharmacological intervention for promoting healthy brain aging. This study furthers the triple-network model, showing that a balancing and reorientation of attention might be engaged not only through higher-order and top-down mechanisms (i.e., FPN/DAN) but also via the coupling of bottom-up, sensory-motor (i.e., SMN/VIN) networks.

Developing a Deep Neural Network for Driver Fatigue Detection Using EEG Signals Based on Compressed Sensing

In recent years, driver fatigue has become one of the main causes of road accidents. As a result, fatigue detection systems have been developed to warn drivers, and, among the available methods, EEG signal analysis is recognized as the most reliable method for detecting driver fatigue. This study presents an automated system for a two-stage classification of driver fatigue, using a combination of compressed sensing (CS) theory and deep neural networks (DNNs), that is based on EEG signals. First, CS theory is used to compress the recorded EEG data in order to reduce the computational load. Then, the compressed EEG data is fed into the proposed deep convolutional neural network for automatic feature extraction/selection and classification purposes. The proposed network architecture includes seven convolutional layers together with three long short-term memory (LSTM) layers. For compression rates of 40, 50, 60, 70, 80, and 90, the simulation results for a single-channel recording show accuracies of 95, 94.8, 94.6, 94.4, 94.4, and 92%, respectively. Furthermore, by comparing the results to previous methods, the accuracy of the proposed method for the two-stage classification of driver fatigue has been improved and can be used to effectively detect driver fatigue.

Classification of visual comprehension based on EEG data using sparse optimal scoring

Objective. Understanding and differentiating brain states is an important task in the field of cognitive neuroscience with applications in health diagnostics, such as detecting neurotypical development vs. autism spectrum or coma/vegetative state vs. locked-in state. Electroencephalography (EEG) analysis is a particularly useful tool for this task as EEG data can detect millisecond-level changes in brain activity across a range of frequencies in a non-invasive and relatively inexpensive fashion. The goal of this study is to apply machine learning methods to EEG data in order to classify visual language comprehension across multiple participants. Approach. 26-channel EEG was recorded for 24 Deaf participants while they watched videos of sign language sentences played in time-direct and time-reverse formats to simulate interpretable vs. uninterpretable sign language, respectively. Sparse optimal scoring (SOS) was applied to EEG data in order to classify which type of video a participant was watching, time-direct or time-reversed. The use of SOS also served to reduce the dimensionality of the features to improve model interpretability. Main results. The analysis of frequency-domain EEG data resulted in an average out-of-sample classification accuracy of 98.89%, which was far superior to the time-domain analysis. This high classification accuracy suggests this model can accurately identify common neural responses to visual linguistic stimuli. Significance. The significance of this work is in determining necessary and sufficient neural features for classifying the high-level neural process of visual language comprehension across multiple participants.

Selection of Embedding Dimension and Delay Time in Phase Space Reconstruction via Symbolic Dynamics.

The modeling and prediction of chaotic time series require proper reconstruction of the state space from the available data in order to successfully estimate invariant properties of the embedded attractor. Thus, one must choose appropriate time delay and embedding dimension p for phase space reconstruction. The value of can be estimated from the Mutual Information, but this method is rather cumbersome computationally. Additionally, some researchers have recommended that should be chosen to be dependent on the embedding dimension p by means of an appropriate value for the time delay , which is the optimal time delay for independence of the time series. The C-C method, based on Correlation Integral, is a method simpler than Mutual Information and has been proposed to select optimally and . In this paper, we suggest a simple method for estimating and based on symbolic analysis and symbolic entropy. As in the C-C method, is estimated as the first local optimal time delay and as the time delay for independence of the time series. The method is applied to several chaotic time series that are the base of comparison for several techniques. The numerical simulations for these systems verify that the proposed symbolic-based method is useful for practitioners and, according to the studied models, has a better performance than the C-C method for the choice of the time delay and embedding dimension. In addition, the method is applied to EEG data in order to study and compare some dynamic characteristics of brain activity under epileptic episodes

Performance analysis of EEG seizure detection features

Automatic detection of epileptic seizures can serve as a valuable clinical tool which involves a more objective and computationally efficient method for the analysis of EEG data in order to generate increasingly accurate and reliable results. Automatic seizure detection is also an important component of closed-loop responsive cortical stimulation systems. The goal of this study is to evaluate EEG-based features recently proposed for seizure detection to discover the optimum ones for a reliable seizure detection system. We extracted seizure detection features from intracranial EEG signals that were recorded during invasive pre-surgical epilepsy monitoring of people with drug resistant focal epilepsy at the Epilepsy Center of the University Hospital of Freiburg. Features from time, frequency and phase space domains as well as similarity/dissimilarity features were considered. The performance of each feature was investigated using the statistical test ANOVA. Performance analysis was conducted separately on the recordings from the channels within the seizure-onset zone (SOZ-in) and the recordings from the channels outside the seizure-onset zone (SOZ-out). Similarity/dissimilarity features that measure dynamic properties of the EEG signal and the evolving phenomena of the seizures could significantly separate ictal (during seizure) states from pre-ictal (before seizure) states (p < 0.01). Among them, our proposed feature, Bhattacharyya-based dissimilarity index (BBDI), successfully passed Tukey's post-hoc test as well suggesting that it can distinguish both pre-ictal and post-ictal (after seizure) periods from ictal period. BBDI was further applied to detect epileptic seizures and achieved area under the curve of the receiver-operator characteristic (ROC) equal to 0.96 and 0.94 for SOZ-in and SOZ-out channels, respectively. No significant difference (p = 0.59) was observed in the performance of features between SOZ-in recordings and SOZ-out recordings. The discriminative value of EEG seizure detection features was determined by statistical tests. As a result, the best features to be selected for a reliable seizure detection system designed for people with drug-resistant focal epilepsy were suggested, which include similarity/dissimilarity indices.

Comparison of Complex-Valued Independent Component Analysis Algorithms for EEG Data

Independent Component Analysis (ICA) has been successfully applied to a variety of problems, from speaker identification and image processing to functional magnetic resonance imaging (fMRI) of the brain. In particular, it has been applied to analyze EEG data in order to estimate the sources form the measurements. However, it soon became clear that for EEG signals the solutions found by ICA often depends on the particular ICA algorithm, and that the solutions may not always have a physiologically plausible interpretation. Therefore, nowadays many researchers are using ICA largely for artifact detection and removal from EEG, but not for the actual analysis of signals from cortical sources. However, a recent modification of an ICA algorithm has been applied successfully to EEG signals from the resting state. The key idea was to perform a particular preprocessing and then apply a complexvalued ICA algorithm. In this paper, we consider multiple complex-valued ICA algorithms and compare their performance on real-world resting state EEG data. Such a comparison is problematic because the way of mixing the original sources (the “ground truth”) is not known. We address this by developing proper measures to compare the results from multiple algorithms. The comparisons consider the ability of an algorithm to find interesting independent sources, i.e. those related to brain activity and not to artifact activity. The performance of locating a dipole for each separated independent component is considered in the comparison as well. Our results suggest that when using complex-valued ICA algorithms on preprocessed signals the resting state EEG activity can be analyzed in terms of physiological properties. This reestablishes the suitability of ICA for EEG analysis beyond the detection and removal of artifacts with real-valued ICA applied to the signals in the time-domain.

EP 111. Phase-locking analysis of EEG-data in movement related tasks reveals functional network of synchronous activity

The performance of motor actions is a highly complex task where the movement of different body parts has to be coordinated by the activity of various brain regions. In particular, the interplay of brain regions of the motor cortex is crucial. In the study presented here, the interaction of different motor related brain regions during simple internally and externally initiated finger movements was investigated during preparation and execution of those movements. To this end, EEG data (64 channel system) were recorded from 18 right-handed healthy participants (22–35 years, 10 female). The participants performed a finger tapping paradigm that contained left and right index finger movements which were triggered either by a visual cue or by voluntary choice. We carried out inter-regional phase-locking analysis of Morlet wavelet phases in delta-theta (2–7 Hz), alpha (8–12 Hz) and beta (13–30 Hz) frequency bands of the Laplacian referenced EEG data in order to identify coupling between brain regions during the motor task. We analyzed connectivity in a network involving electrodes lying above the premotor areas (PM: FC3, FC4), the supplementary motor areas (SMA: Cz, FCz) and the primary motor cortex (M1: C3, C4) as nodes. We found significant changes of synchronization in the delta-theta frequency band during both movement preparation and execution. The construction of lateralization networks revealed that the phase-locking effect in these frequency bands was stronger for connections from motor regions to supplementary motor areas contralaterally to the moving hand than on the ipsilateral side. The interregional synchronizations appeared only when both regions showed strong intraregional phase locking. Thus, intraregional phase locking in the delta-theta frequencies seems to be a prerequisite for interregional phase locking. We hypothesize that both intra- and interregional phase locking form a functional network of synchronous activity. This activity enhances the excitability of the motor system so that transient cue signals are able to induce different kinds of movements during movement preparation. Additionally we found strong synchronization in the beta-frequency band that appeared after the movement has been executed for connections from the supplementary motor areas to the primary motor cortex and the pre-motor cortex contralaterally to the moving hand. Since this synchronization effect is expressed significantly stronger during the self-initiated than during visually-triggered finger movements, we suggest that it might be related to the suppression of the internal “go” cue after the movement was executed.

The dark side of the alpha rhythm: fMRI evidence for induced alpha modulation during complete darkness

The unique role of the EEG alpha rhythm in different states of cortical activity is still debated. The main theories regarding alpha function posit either sensory processing or attention allocation as the main processes governing its modulation. Closing and opening eyes, a well-known manipulation of the alpha rhythm, could be regarded as attention allocation from inward to outward focus though during light is also accompanied by visual change. To disentangle the effects of attention allocation and sensory visual input on alpha modulation, 14 healthy subjects were asked to open and close their eyes during conditions of light and of complete darkness while simultaneous recordings of EEG and fMRI were acquired. Thus, during complete darkness the eyes-open condition is not related to visual input but only to attention allocation, allowing direct examination of its role in alpha modulation. A data-driven ridge regression classifier was applied to the EEG data in order to ascertain the contribution of the alpha rhythm to eyes-open/eyes-closed inference in both lighting conditions. Classifier results revealed significant alpha contribution during both light and dark conditions, suggesting that alpha rhythm modulation is closely linked to the change in the direction of attention regardless of the presence of visual sensory input. Furthermore, fMRI activation maps derived from an alpha modulation time-course during the complete darkness condition exhibited a right frontal cortical network associated with attention allocation. These findings support the importance of top-down processes such as attention allocation to alpha rhythm modulation, possibly as a prerequisite to its known bottom-up processing of sensory input.

Sleep in dairy cows recorded with a non-invasive EEG technique

Sufficient sleep time is important for both an adequate metabolic system and the immune function. Sleep in animals is often estimated by behavioural observations, or recorded on restrained animals with invasive electroencephalogram (EEG) techniques, which might affect sleep patterns. Earlier studies on sleep in cows showed that they sleep about 4h per day and drowse almost twice the time. The aim of this study was to record and differentiate between vigilance states in dairy cows using a non-invasive EEG method.Brain activity (electroencephalography, EEG), eye movements (electrooculography, EOG) and muscle activity (electromyography, EMG) were recorded for 6h per animal using surface-attached electrodes to measure different vigilance states. Behaviour registrations from direct observations were combined with the EEG data in order to confirm the identification of different vigilance states from the EEG, EOG and EMG recordings. 8 dry dairy cows, lactation number 1–8 and age 3–11 years, of the Swedish Red breed from the research herd at Kungsängen Research Centre, Uppsala, Sweden, were used in the study.The EEG recordings showed that non-rapid eye movement (NREM) sleep displayed low frequency waves, sometimes with slow wave activity. Rapid eye movement (REM) sleep and alert wakefulness shared similar features of desynchronised waves with varying frequency and could be differentiated by reduced neck muscle activity during REM sleep.The main conclusion from this study is that it is possible to distinguish different vigilance states in dairy cows using surface-attached EEG electrodes.

The use of EEG in Alzheimer’s disease, with and without scopolamine – A pilot study

To use multivariate statistical analysis of EEG data in order to separate EEGs of patients with Alzheimer's disease (AD) from controls. A group of individuals with mild cognitive impairment (MCI) was evaluated using the same methodology. Additionally, the effects of scopolamine on this separation are studied. Statistical pattern recognition (SPR) is used in conjunction with information extracted from EEGs before and after administration of scopolamine. There was complete separation of the AD group and controls before administration of scopolamine. The separation increased after scopolamine had been given. Of the 10 MCI individuals, five seemed to belong to the AD group. Three of those progressed to AD within 1 year and one after 3 years. Using SPR on EEG recordings it is possible to separate AD from controls. This separation can be increased by the use of scopolamine but the medication is not a prerequisite for classification. The results indicate that SPR is useful for predicting progress of MCI to AD. EEG registration is a simple and noninvasive method. If these results are confirmed in other studies, this method could be more widely applied than the highly specialized methods used today in detection of early AD.

運動野と後頭頂葉での経頭蓋磁気刺激誘発反応の違い

The combination of transcranial magnetic stimulation (TMS) and electroencephalogram (EEG) is an effective tool for investigating the cortical reactivity and the functional connectivity in the brain. In our previous study, we reported a method of removing stimulus artifact during TMS with Sample-and-Hold circuit and EEG activity evoked by TMS could be measured successfully. In addition to this method, independent component analysis (ICA) was also applied to recorded EEG data in order to remove the stimulus artifact from for off-line analysis. By using these methods, short latency (< 15 ms) EEG responses to TMS could be obtained. In this paper, we focused on the propagation of EEG activity elicited by TMS. We observed both the EEG topography and the distribution of the current density over the whole head by changing the stimulus site. When motor cortex was stimulated, the propagation of EEG activity to contralateral hemisphere could be clearly observed. However, when posterior parietal cortex was stimulated, no or less propagation of EEG responses could be recognized. These results suggest that the responses evoked by TMS over motor cortex propagate to contralateral hemisphere along the axon through the corpus callosum.

Evoked gamma band response in male adolescent subjects at high risk for alcoholism during a visual oddball task

This study investigates early evoked gamma band activity in male adolescent subjects at high risk for alcoholism (HR; n = 68) and normal controls (LR; n = 27) during a visual oddball task. A time–frequency representation method was applied to EEG data in order to obtain stimulus related early evoked (phase-locked) gamma band activity (29–45 Hz) and was analyzed within a 0–150 ms time window range. Significant reduction of the early evoked gamma band response in the frontal and parietal regions during target stimulus processing was observed in HR subjects compared to LR subjects. Additionally, the HR group showed less differentiation between target and non-target stimuli in both frontal and parietal regions compared to the LR group, indicating difficulty in early stimulus processing, probably due to a dysfunctional frontoparietal attentional network. The results indicate that the deficient early evoked gamma band response may precede the development of alcoholism and could be a potential endophenotypic marker of alcoholism risk.

Suppression of early evoked gamma band response in male alcoholics during a visual oddball task

We investigated the early evoked gamma frequency band activity in alcoholics ( n = 122) and normal controls ( n = 72) during a visual oddball task. A time–frequency representation method was applied to EEG data in order to obtain phase-locked gamma band activity (29–45 Hz) and was analyzed within a 0–150 ms time window range. Significant reduction of the gamma band response in the frontal region during target stimulus processing was observed in alcoholic compared to control subjects. In contrast, significantly higher gamma band response for the non-target stimulus was observed in alcoholics compared to controls. It is suggested that the reduction in early evoked frontal gamma band response to targets may be associated with frontal lobe dysfunction commonly observed in alcoholics. This perhaps can be characterized by a deficient top-down processing mechanism.