Analysis Of EEG Patterns Research Articles

A Novel Approach to Quantify Microsleep in Drivers With Obstructive Sleep Apnea by Concurrent Analysis of EEG Patterns and Driving Attributes.

Accurate quantification of microsleep (MS) in drivers is crucial for preventing real-time accidents. We propose one-to-one correlation between events of high-fidelity driving simulator (DS) and corresponding brain patterns, unlike previous studies focusing general impact of MS on driving performance. Fifty professional drivers with obstructive sleep apnea (OSA) participated in a 50-minute driving simulation, wearing six-channel Electroencephalography (EEG) electrodes. 970 out-of-road OOR (microsleep) events (wheel and boundary contact ≥1 s), and 1020 on-road OR (wakefulness) events (wheel and boundary disconnection ≥1 s), were recorded. Power spectrum density, computed using discrete wavelet transform, analyzed power in different frequency bands and theta/alpha ratios were calculated for each event. We classified OOR (microsleep) events with higher theta/alpha ratio compared to neighboring OR (wakefulness) episodes as true MS and those with lower ratio as false MS. Comparative analysis, focusing on frontal brain, matched 791 of 970 OOR (microsleep) events with true MS episodes, outperforming other brain regions, and suggested that some unmatched instances were due to driving performance, not sleepiness. Combining frontal channels F3 and F4 yielded increased sensitivity in detecting MS, achieving 83.7% combined mean identification rate (CMIR), surpassing individual channel's MIR, highlighting potential for further improvement with additional frontal channels. We quantified MS duration, with 95% of total episodes lasting between 1 to 15 seconds, and pioneered a robust correlation (r = 0.8913, p<0.001) between maximum drowsiness level and MS density. Validating simulator's signals with EEG patterns by establishing a direct correlation improves reliability of MS identification for assessing fitness-to-drive of OSA-afflicted adults.

Multivariate pattern analysis of EEG reveals nuanced impact of negation on sentence processing in the N400 and later time windows.

The neurocognitive mechanism underlying negation processing remains controversial. While negation is suggested to modulate the access of word meaning, no such evidence has been observed in the event-related potential (ERP) literature on sentence processing. In the current study, we applied both univariate ERP and multivariate pattern analysis (MVPA) methods to examine the processing of sentence negation. We investigated two types of negative congruent/incongruent sentence pairs with truth-value evaluation (e.g., "A robin is a/not a bird") and without (e.g., "The woman reads a/no book"). In the N400 time window, ERPs consistently showed increased negativity for negative and incongruent conditions. MVPA, on the other hand, revealed nuanced interactions between polarity and congruency. In the later P600 time window, MVPA but not the ERPs revealed an effect of congruency, which may be functionally distinct from the N400 window. We further used cross-decoding to show that the cognitive processes underlying the N400 window for both affirmative and negative sentences are comparable, whereas in the P600 window, only for the truth sentences, negative sentences showed a distinct pattern from their affirmative counterparts. Our results thus speak for a more interactive, but nevertheless serial and biphasic, and potentially construction-specific processing account of negation. We also discuss the advantage of applying MVPA in addition to the classical univariate methods for a better understanding of the neurobiology of negation processing and language comprehension alike.

Emotional Stress Recognition Using Electroencephalogram Signals Based on a Three-Dimensional Convolutional Gated Self-Attention Deep Neural Network

The brain is more sensitive to stress than other organs and can develop many diseases under excessive stress. In this study, we developed a method to improve the accuracy of emotional stress recognition using multi-channel electroencephalogram (EEG) signals. The method combines a three-dimensional (3D) convolutional neural network with an attention mechanism to build a 3D convolutional gated self-attention neural network. Initially, the EEG signal is decomposed into four frequency bands, and a 3D convolutional block is applied to each frequency band to obtain EEG spatiotemporal information. Subsequently, long-range dependencies and global information are learned by capturing prominent information from each frequency band via a gated self-attention mechanism block. Using frequency band mapping, complementary features are learned by connecting vectors from different frequency bands, which is reflected in the final attentional representation for stress recognition. Experiments conducted on three benchmark datasets for assessing the performance of emotional stress recognition indicate that the proposed method outperforms other conventional methods. The performance analysis of proposed methods confirms that EEG pattern analysis can be used for studying human brain activity and can accurately distinguish the state of stress.

Smart neurocare approach for detection of epileptic seizures using deep learning based temporal analysis of EEG patterns

Smart neurocare approach for detection of epileptic seizures using deep learning based temporal analysis of EEG patterns

Automated Delimitation and Classification of Autistic Disorder Using EEG Signal

ABSTRACT Autism Spectrum Disorder (ASD) is a disturbing neuro-developmental disorder affecting the behavioural ability and communication skills of an individual. Early detection of such prevalent abnormality through EEG pattern analysis with the help of computer-aided diagnostic tools; provide significant relief to the health care professionals for better management and treatment of such patients. This work has been proposed as an investigation of the sensory responsiveness of the children affected by ASD. This ASD is analyzed by extracting the non-linear features of the EEG signal. A total of 73 EEG signals are collected from different patients out of which 41 are affected with ASD and 32 with normal neural activity. These signals are then pre-processed and filtered using a low pass filter and then separated using Independent Component Analysis (ICA) into additive subcomponents. Significant features are extracted, which can be further investigated to identify event-related potential and Non-biological Artifacts. These features are then classified using Support Vector Machine (SVM) with an accuracy of 90.41%. As a result, appropriate sensory profiles can be obtained that may help proper diagnosis and treatment at an early stage.

Analysis of EEG Patterns in Subjects with Panic Attacks

The dynamics of the photic driving reaction was studied in subjects with panic attacks, using wavelet and recurrence analyses of electroencephalographic (EEG) patterns. Quantitative measures of the driving reaction to the given frequency were estimated from the increase in the energy of the wavelet spectrum during photostimulation, the coefficient of photic driving, and parameters of a joint recurrence plot of the light stimulus and EEG pattern (the rate and time of recurrence of the EEG trajectory into the vicinity of a previous point). The parameters were determined before and after non-drug therapy, which included activation and training of artificial stable functional connections (ASFCs) in the brain. The formation of ASFCs was found to decrease both asymmetry in occipital lobe responses to the photostimulus and the degree of neuroticism in subjects with panic attacks. The changes were reflected in decreasing values of the photic driving coefficient and recurrence rate and increasing values of the recurrence time. The improvement of the subject’s psychophysiological state after a course of ASFC activation was confirmed by a positive dynamics of psychophysiological testing data.

Variability of motor performance and symptoms of motor disregard in the use of neurocomputer technologies in the rehabilitation of patients after a stroke

Currently, neurobiological feedback technologies based on neurocomputer interfaces are used in the rehabilitation of patients after a stroke (Yekutiel M, 2000). The ability to actively implement the capabilities of interface-brain-computer technology (IMC) remains limited for most patients (Cloutier S. et al., 2015). The IMC was used, based on the analysis of EEG patterns and recognition of the synchronization / desynchronization reaction of the sensoro-motor (mu-rhythm) in imagining the movements of the hands of patients. The data of 15 patients (7 men, 8 women), at the age of 64 (62.0, 69.0) years, the duration of the stroke of 8 (5, 19) months, with right hemispheric localization in 7 patients (46%), mild to plegia (MRCWS); with a focus of stroke etiology (ischemic / hemorrhagic), supratentorial localization (CT, MRI); received therapy IMK-an exoskeleton brush with kinesthetic feedback, 9.5 (8; 10) procedures (ClinicalTrials.gov (identifier: NCT02325947 )). Only 6 (40%) patients solved the mental motor problem with accuracy of the EEG data (presence of the mu-rhythm) above 70%, 4 (26%) failed to solve the problem. The accuracy of their representations according to EEG data was no more than 20%. The accuracy of the representations of the remaining 5 (33%) patients was in the range of 45–55%. What is consistent with the literature (Ahn and Chan, 2015; Hammer EM et al., 2014). The absolute majority of patients with stroke have motor disregard for the affected side. Even with a minimal motor deficit, patients unconsciously prefer to use a healthy hand in everyday life. Such patients need to restore the functional system of formation of target motility by training the lost or altered links of this functional system. The combination of neurocomputer technologies (IMC) and physical training in a certain sequence will facilitate their more successful interaction and will improve the efficiency of rehabilitation.

Detrended fluctuation analysis of EEG patterns associated with real and imaginary arm movements

Based on the detrended fluctuation analysis (DFA), we address the problem of recognizing the electrical activity of the brain associated with movements of the right arm and the imagination of this procedure. In experiments with untrained volunteers, we show the possibility to distinguish between the related EEG patterns. We also demonstrate significant distinctions in the scaling features between background records and EEG signals associated with mental intentions that can be transformed to control commands for brain–computer interfaces. This effect is observed in many EEG channels, although individual features of subjects provided various distributions of brain areas with the most prominent differences.

Pattern classification of EEG signals reveals perceptual and attentional states.

Pattern classification techniques have been widely used to differentiate neural activity associated with different perceptual, attentional, or other cognitive states, often using fMRI, but more recently with EEG as well. Although these methods have identified EEG patterns (i.e., scalp topographies of EEG signals occurring at certain latencies) that decode perceptual and attentional states on a trial-by-trial basis, they have yet to be applied to the spatial scope of attention toward global or local features of the display. Here, we initially used pattern classification to replicate and extend the findings that perceptual states could be reliably decoded from EEG. We found that visual perceptual states, including stimulus location and object category, could be decoded with high accuracy peaking between 125–250 ms, and that the discriminative spatiotemporal patterns mirrored and extended our (and other well-established) ERP results. Next, we used pattern classification to investigate whether spatiotemporal EEG signals could reliably predict attentional states, and particularly, the scope of attention. The EEG data were reliably differentiated for local versus global attention on a trial-by-trial basis, emerging as a specific spatiotemporal activation pattern over posterior electrode sites during the 250–750 ms interval after stimulus onset. In sum, we demonstrate that multivariate pattern analysis of EEG, which reveals unique spatiotemporal patterns of neural activity distinguishing between behavioral states, is a sensitive tool for characterizing the neural correlates of perception and attention.

Prognostic EEG patterns in patients resuscitated from cardiac arrest with particular focus on Generalized Periodic Epileptiform Discharges (GPEDs).

We assessed clinical and early electrophysiological characteristics, in particular Generalized Periodic Epileptiform Discharges (GPEDs) patterns, of consecutive patients during a 1-year period, hospitalized in the Intensive Care Unit (ICU) after resuscitation following cardiac arrest (CA). Consecutive patients resuscitated from cardiac arrest (CA) with first EEG recordings within 48hours were included. Clinical data were collected from hospital records, in particular therapeutic hypothermia. Electroencephalograms (EEGs) were re-analyzed retrospectively. Sixty-two patients were included. Forty-two patients (68%) were treated with therapeutic hypothermia according to international guidelines. Global mortality was 74% but not significantly different between patients who benefited from therapeutic hypothermia compared to those who did not. All the patients who did not have an initial background activity (36/62; 58%) died. By contrast, initial background activity was present in 26/62 (42%) and among these patients, 16/26 (61%) survived. Electroencephalography demonstrated GPEDs patterns in 5 patients, all treated by therapeutic hypothermia and antiepileptic drugs. One of these survived and showed persistent background activity with responsiveness to benzodiazepine intravenous injection. Patients presenting suppressed background activity, even when treated by hypothermia, have a high probability of poor outcome. Thorough analysis of EEG patterns might help to identify patients with a better chance of survival.

Analysis of EEG patterns and genotypes in patients with Angelman syndrome

We prospectively analyzed EEGs from participants in the ongoing NIH Rare Diseases Clinical Research Network Angelman Syndrome Natural History Study. Of the one-hundred-sixty enrolled patients (2006–2010), 115 had complete data (58 boys, median age 3.6years). Distinct EEG findings were intermittent rhythmic delta waves (83.5%), interictal epileptiform discharges (74.2%), intermittent rhythmic theta waves (43.5%), and posterior rhythm slowing (43.5%). Centro-occipital and centro-temporal delta waves decreased with age (p=0.01, p=0.03). There were no specific correlations between EEG patterns and genotypes. A classification tree allowed the prediction of deletions class-1 (5.9Mb) in patients with intermittent theta waves in <50% of EEG and interictal epileptiform abnormalities; UPD, UBE3A mutation or imprinting defects in patients with intermittent theta in <50% of EEG without interictal epileptiform abnormalities; deletions class-2 (5.0Mb) in patients with >50% theta and normal posterior rhythm; atypical deletions in patients with >50% theta but abnormal posterior rhythm. EEG patterns are important biomarkers in Angelman syndrome and may suggest the underlying genetic etiology.

EEG Pattern Analysis for Physiological Indicators of Mental Fatigue in Simulated Air Traffic Control Tasks

Objective: This study was designed to identify potential neurophysiologic markers and patterns of mental fatigue among air traffic controllers in their work environment.Background: The monitoring of mental fatigue in air traffic controllers has been of interest as their tasks involve high cognitive workload and are also critical to the safety of the public.Method: High-density electroencephalogram (EEG) was used to record 2-hour long air traffic control studies in eleven participants. Participants were asked to perform realistic tasks in a simulation, to operate a virtual air traffic control system. Fourier Transforms were used to estimate EEG power spectrum, statistical tests were implemented to reveal EEG spatial pattern changes caused by the time-on-task. The concept of mental state transition was introduced to study the development of certain mental states which are related to the mental fatigue. Results: The observation of EEG spectral data over a period of time revealed statistically significant changes spatially localized to central and parietal cortices. Rhythmic EEG activity within theta, alpha, and beta bands indicates transitions among mental states, which appear to be promising indicators for the development of mental fatigue. Mental fatigue indicated by the transition of mental states was estimated to approximately 70 minutes after the time on task. Application: This study can build the foundation to develop promising technologies for real time monitoring of mental fatigue, which will increase public safety and proper human resource planning.

High resolution parametric description of slow wave sleep

We propose a new framework for quantitative analysis of sleep EEG, compatible with the traditional analysis, based upon adaptive time–frequency approximation of signals. Using a high resolution description of EEG rhythms and transients in terms of their time occurrence and width, frequency and amplitude, we present a detailed detection and parameterization of delta waves, including also the time occupied by each delta wave—a parameter inaccessible directly by previously applied signal processing methods. To validate the proposed parameterization, we construct a simple detector of sleep stages 3 and 4, based explicitly upon the classical criteria related to delta waves. To properly compare its performance to the inter-expert agreements and other expert systems, we sort out and discuss the methodology of reporting concordance in this context. Since the proposed parameterization proves to be compatible with the visual analysis of EEG, we can derive new variables for quantitative analysis of EEG patterns recognized for decades. As examples, we present a continuous description of delta waves and sleep spindles in the overnight sleep, and compare results to the traditional FFT-based estimates.

A three-dimensional spatio-temporal EEG pattern analyzing system *

Spatio-temporal pattern analysis of EEG is an important tool in brain research. An EEG pattern analysis system based on a hierarchical multi-method approach is proposed here. The system consists of multiple steps including extraction of target signal, acquisition of intracranial electric activity distribution, adaptive segmentation of EEG and spatio-temporal pattern recognition. Some modern signal processing methods such as common spatial subspace decomposition, hidden Markov model are adopted. This paper also proposes an algorithm named LORETA-FOCUSS to estimate the current density inside the brain with a high spatial resolution. Microstate analysis of EEG is extended to the 3-D situation. The system was applied to the brain computer interface problem and achieved the highest accuracy of 88.89% with an average accuracy of 81.48% when classifying two imaginary movement tasks, while the data were not manually preselected. The result has proved spatio-temporal EEG pattern analysis is an efficient way in brain research.

Reliability of Human Subject - Artificial System Interactions

Main problems related to reliability of interaction between human subject and artificial system (namely of the transportation character) are discussed. The paper consists of three mayor parts:The first one is devoted to the theoretical backgrounds of the problem from the both theory of system reliability and neurology/psychology views.Second part presents the discussion of relevant methodologies of the classification and prediction of the reliability decline. The methodology based on EEG pattern analysis is chosen as the appropriate one for the presented task. The key phenomenon of "micro-sleep" is discussed in detail.The last part presents some latest experimental results in context of presented knowledge. Proposals for the future studies are presented at the end of the presented article. The special interest should be devoted to the analysis and in-time prediction of fatal attention decreases and to the design and construction of the respective on-board applicable warning system.

Thalamo-cortical relationships in cat during slow wave sleep studied by computerized EEG pattern analysis : N.V. Chayanov, and A.F. Iznak (All Uniion Mental Research Center, Moscow, USSR)

Thalamo-cortical relationships in cat during slow wave sleep studied by computerized EEG pattern analysis : N.V. Chayanov, and A.F. Iznak (All Uniion Mental Research Center, Moscow, USSR)

Quantitative Analysis of EEG Patterns of Premature Infants, Especially in Relation to the Development of Sleep

Quantitative Analysis of EEG Patterns of Premature Infants, Especially in Relation to the Development of Sleep