Real-time Electroencephalography Research Articles

AWAreness during REsuscitation - II: A multi-center study of consciousness and awareness in cardiac arrest

IntroductionCognitive activity and awareness during cardiac arrest (CA) are reported but ill understood. This first of a kind study examined consciousness and its underlying electrocortical biomarkers during cardiopulmonary resuscitation (CPR). MethodsIn a prospective 25-site in-hospital study, we incorporated a) independent audiovisual testing of awareness, including explicit and implicit learning using a computer and headphones, with b) continuous real-time electroencephalography(EEG) and cerebral oxygenation(rSO2) monitoring into CPR during in-hospital CA (IHCA). Survivors underwent interviews to examine for recall of awareness and cognitive experiences. A complementary cross-sectional community CA study provided added insights regarding survivors’ experiences. ResultsOf 567 IHCA, 53(9.3%) survived, 28 of these (52.8%) completed interviews, and 11(39.3%) reported CA memories/perceptions suggestive of consciousness. Four categories of experiences emerged: 1) emergence from coma during CPR (CPR-induced consciousness [CPRIC]) 2/28(7.1%), or 2) in the post-resuscitation period 2/28(7.1%), 3) dream-like experiences 3/28(10.7%), 4) transcendent recalled experience of death (RED) 6/28(21.4%). In the cross-sectional arm, 126 community CA survivors’ experiences reinforced these categories and identified another: delusions (misattribution of medical events). Low survival limited the ability to examine for implicit learning. Nobody identified the visual image, 1/28(3.5%) identified the auditory stimulus. Despite marked cerebral ischemia (Mean rSO2 = 43%) normal EEG activity (delta, theta and alpha) consistent with consciousness emerged as long as 35–60 minutes into CPR. ConclusionsConsciousness. awareness and cognitive processes may occur during CA. The emergence of normal EEG may reflect a resumption of a network-level of cognitive activity, and a biomarker of consciousness, lucidity and RED (authentic “near-death” experiences).

EEG-based real-time diagnostic system with developed dynamic 2TEMD and dynamic ApEn algorithms.

In real-time electroencephalography (EEG) analysis, the problem of observing dynamic changes and the problem of binary classification is a promising direction. EEG energy and complexity are important evaluation metrics in brain death determination in the field of EEG analysis. We developed two algorithms, dynamic turning tangent empirical mode decomposition to compute EEG energy and dynamic approximate entropy to compute EEG complexity for brain death determination. The developed algorithm is applied to analyze 50 EEG data of coma patients and 50 EEG data of brain death patients. The validity of the dynamic analysis is confirmed by the accuracy rate derived from the comparison with turning tangent empirical mode decomposition and approximate entropy algorithms. We evaluated the EEG data of three patients using the built diagnostic system. The experimental results visually showed that the EEG energy ratio was higher in a coma state than that in brain death, while the complexity was lower than that in brain death.

Combining survey-based and neuroscience measurements in customer acceptance of self-driving technology

In recent years, the issue of consumer acceptance of self-driving cars has come to the forefront of interest among policymakers, researchers and automotive industry experts. Anchored in the Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT), these studies are typically based on survey data from respondents who have not used self-driving vehicles. The survey, being a perception-based measure has several limitations, such as social desirability bias, inaccuracy due to time pressure, just to name a few. In addition, the change in intention to use self-driving vehicles as a result of actual test use deserves more academic attention. To address this limitation, volunteers were invited to participate in a test drive as passengers in a self-driving vehicle, testing their acceptance of technology using an adapted version of UTAUT2 questionnaire before and after the ride. Neuroscience measurements were also performed: real-time electroencephalography (EEG) and eye-tracking were recorded during the ride. The explanatory power of our regression model was high (97%) using this combined research method. Our preliminary results suggest, that in a real-life test technology acceptance was related more to emotional experience during the ride and less to other elements of the UTAUT2 model – which challenges the results of previous methods based solely on surveys.

Pánik próbája a mérés : Avagy önvezető technológiák elfogadásának valós idejű vizsgálata neurotudományi mérésekkel

There is a broad international research interest in the study of consumer acceptance of self-driving technology. Most researchers use questionnaires based on different versions of TAM and UTAUT models to investigate this topic. However, the vast majority of respondents fill out the questionnaires, without any first-hand experience of self-driving technology. Addressing this limitation, the authors offered their participants a short test drive as passengers in a self-driving vehicle. In addition to the questionnaires, in the course of these trials they collected real-time electroencephalography (EEG) and eye movement data from each participant. A linear regression model revealed high explanatory power (97%), when physiological measurements were combined with a follow-up UTAUT-2 questionnaire. The results suggest that when surveys are combined with in real-time in-situ measurements, explanatory variables for technology adoption relate to experience and emotion. Neuroscientific measures may play an important role in detecting the latter.

Affective Brain-Computer Music Interface in Emotion Regulation and Neurofeedback: A Research Protocol

Introduction: Emotion regulation is an integral part of mental health, dynamically impacting brain function as one’s emotions change continuously throughout the day. Impairments in emotion regulation are associated with a range of psychiatric disorders. Although the implications of emotion regulation are crucial to mental health, few studies have examined training emotion regulation strategies with respect to the brain. Thus, this manuscript will propose an affective brain-computer music interface (aBCMI) prototype for emotion regulation that continuously generates music by estimating emotions from real-time electroencephalography (EEG) signals. Methods: In this proposal, we describe our prototype consisting of an emotion classifier that detects the expression of emotions from EEG signals, and a music generator that generates music reflective of those emotions. We evaluate the prototype in three separate studies. In study 1, the accuracy of the music generator is tested. In study 2, the accuracy of the emotion classifier is tested by assessing its correlation with real-time, self-reported emotions. In study 3, the generative music algorithm is assessed to explore emotion regulation strategies. Discussion: The proposed BCMI is expected to accurately estimate emotions, provide musical feedback of participants’ emotions, and enable users to intentionally modulate their emotions from musical feedback. This involves capturing the listener’s emotions in real-time using EEG signals, providing the opportunity to regulate one’s emotional state with musical feedback. Thus, in addition to enabling greater neurofeedback training of emotions, our prototype can enhance the understanding of affective computing and emotions with EEG and machine learning. Conclusion: Clinical applications of this prototype may have a tremendous impact as a neurofeedback tool in music therapy for training emotion regulation. Future research may benefit from using the proposed BCMI as a neurofeedback treatment in mood disorders.

What Does Electroencephalography Coherence Tell Us about Memory Encoding in Adolescents at High Risk of Suicide?

Background: Suicide is known to be closely related to depression, which is accompanied by cognitive decline. Objective: This study examined whether memory performance and cortical networking differ between high suicide risk and control groups depending on task difficulty. Methods: The participants were 28 high school students consisting of 14 suicide risk and 14 control subjects. Real-time electroencephalography signals were collected during a working memory task. Inter- and intrahemispheric coherences were analyzed. Results: Higher cortical networking during memory encoding was found in suicide risk adolescents compared to the control group. An increase in task difficulty heightened interhemispheric coherence. Conclusions: Higher cortical networking in suicide risk adolescents seems to reflect activation of compensatory mechanisms in an attempt to minimize behavioral decline.

A phase lag index hardware calculation for real-time electroencephalography studies.

Among the different techniques used for the analysis of electroencephalograms, the phase lag index has become an important method for the calculation of the functional brain connectivity. Currently, this method is implemented offline due to its high computational complexity restricting it from real-time applications that would require an online neurofeedback. In this paper, we propose a new architecture to calculate the phase lag index of electroencephalograms in real-time. As a proof of concept, a 32 bit and 16-channel system running at 188.32 MHz was synthesized on a Stratix IV GX FPGA. The system was tested and the simulations demonstrated that the system could perform the calculation of the Phase lag index at least 66 times faster than the MATLAB software with a mean square error of less than 5.72×10-6.

No trace of phase: Corticomotor excitability is not tuned by phase of pericentral mu-rhythm

BackgroundThe motor potentials evoked by transcranial magnetic stimulation (TMS) over the motor hand area (M1-HAND) show substantial inter-trial variability. Pericentral mu-rhythm oscillations, might contribute to inter-trial variability. Recent studies targeting mu-activity based on real-time electroencephalography (EEG) reported an influence of mu-power and mu-phase on the amplitude of motor evoked potentials (MEPs) in a preselected group with strong pericentral mu-activity. Other studies that determined mu-power or mu-phase based on post-hoc trial sorting according in non-preselected individuals were largely negative. ObjectivesTo reassess if cortico-spinal activity is modulated by the mu-rhythm, we applied single-pulse TMS to the M1-HAND conditional on the phase of the intrinsically expressed pericentral mu-rhythm in 14 non-preselected healthy young participants. MethodsTMS was given at 0, 90, 180, and 270° of the mu-phase. Based on the absence of effects of mu-phase or mu-power when analyzing the mean MEP amplitudes, we also computed a linear mixed effects model, which included mu-phase, mu-power, inter-stimulus interval (ISIs) as fixed effects, treating the subject factor as a random effect. ResultsMixed model analysis revealed a significant effect of mu-power and ISI, but no effect of mu-phase and no interactions. MEP amplitude scaled linearly with lower mu-power or longer ISIs, but these modulatory effects were very small relative to inter-trial MEP variability. ConclusionOur largely negative results are in agreement with previous offline TMS-EEG studies and point to a possible influence of ISI. Future research needs to clarify under which circumstances the responsiveness of human the M1-HAND to TMS depends on the synchronicity with mu-power and mu-phase.

Use of amplitude-integrated electroencephalography in decision-making for extracorporeal membrane oxygenation in comatose cardiac arrest patients whose eventual neurologic recovery is uncertain.

Comatose cardiac arrest patients frequently experience cardiogenic shock or recurrent arrest. Extracorporeal membrane oxygenation (ECMO) can be used to salvage patients with cardiogenic shock or cardiac arrest refractory to conventional therapies. However, in comatose cardiac arrest patients whose neurologic recovery is uncertain, the use of ECMO is restricted because it requires considerable financial and human resources. Amplitude-integrated electroencephalography is an easily applicable, real-time electroencephalography monitoring tool that has been increasingly used to monitor brain activity in comatose cardiac arrest patients. We describe our experience of using amplitude-integrated electroencephalography in decision-making to place ECMO for comatose cardiac arrest patients whose eventual neurologic recovery appeared uncertain at the time of ECMO placement.

Monitoring the Relationship Between Changes in Cerebral Oxygenation and Electroencephalography Patterns During Cardiopulmonary Resuscitation: A Feasibility Study.

To date, no studies have examined real-time electroencephalography and cerebral oximetry monitoring during cardiopulmonary resuscitation as markers of the magnitude of global ischemia. We therefore sought to assess the feasibility of combining cerebral oximetry and electroencephalography in patients undergoing cardiopulmonary resuscitation and further to evaluate the electroencephalography patterns during cardiopulmonary resuscitation and their relationship with cerebral oxygenation as measured by cerebral oximetry. Extended case series of in-hospital and out-of-hospital cardiac arrest subjects. Tertiary Medical Center. Inclusion criteria: Convenience sample of 16 patients undergoing cardiopulmonary resuscitation during working hours between March 2014 and March 2015, greater than or equal to 18 years. A portable electroencephalography (Legacy; SedLine, Masimo, Irvine, CA) and cerebral oximetry (Equanox 7600; Nonin Medical, Plymouth, MN) system was used to measure cerebral resuscitation quality. Real-time regional cerebral oxygen saturation and electroencephalography readings were observed during cardiopulmonary resuscitation. The regional cerebral oxygen saturation values and electroencephalography patterns were not used to manage patients by clinical staff. In total, 428 electroencephalography images from 16 subjects were gathered; 40.7% (n = 174/428) were artifactual, therefore 59.3% (n = 254/428) were interpretable. All 16 subjects had interpretable images. Interpretable versus noninterpretable images were not related to a function of time or duration of cardiopulmonary resuscitation but to artifacts that were introduced to the raw data such as diaphoresis, muscle movement, or electrical interference. Interpretable data were able to be obtained immediately after application of the electrode strip. Seven distinct electroencephalography patterns were identified. Voltage suppression was commonest and seen during 78% of overall cardiopulmonary resuscitation time and in 15 of 16 subjects at some point during their cardiopulmonary resuscitation. Other observed patterns and their relative prevalence in relation to overall cardiopulmonary resuscitation time were theta background activity 8%, delta background activity 5%, bi frontotemporal periodic discharge 4%, burst suppression 2%, spike and wave 2%, and rhythmic delta activity 1%. Eight of 16 subjects had greater than one interpretable pattern. At regional cerebral oxygen saturation levels less than or equal to 19%, the observed electroencephalography pattern was exclusively voltage suppression. Delta background activity was only observed at regional cerebral oxygen saturation levels greater than 40%. The remaining patterns were observed throughout regional cerebral oxygen saturation categories above a threshold of 20%. Real-time monitoring of cerebral oxygenation and function during cardiac arrest resuscitation is feasible. Although voltage suppression is the commonest electroencephalography pattern, other distinct patterns exist that may correlate with the quality of cerebral resuscitation and oxygen delivery.

Multi-Scale Entropy and Neural Networks for Detection of Depth of Anaesthesia within EEG Signals

In recent years, multi-scale sample entropy (MSSE) is rapidly gaining popularity as an interesting tool for exploring neurophysiological mechanisms. In this paper, we propose a new method based on MSSE for on-line monitoring of the depth of anaesthesia (DoA) to quantify the anaesthetic effect with real-time electroencephalography by using MSSE. Empirical mode decomposition (EMD) was used successfully to filter EEG recordings of artefacts before the imitation of the recognition phases. Artificial neural networks were used to classify three scales of the MSSE into three stages of hypnosis. Results showed 95% accuracy of the tested clinical anaesthesia EEG recordings obtained for 20 patients.

Real time electroencephalography analysis of brainwaves during perception of visual illusions

Real time electroencephalography analysis of brainwaves during perception of visual illusions

Evaluation of an Adaptive Game that Uses EEG Measures Validated during the Design Process as Inputs to a Biocybernetic Loop.

Biocybernetic adaptation is a form of physiological computing whereby real-time data streaming from the brain and body is used by a negative control loop to adapt the user interface. This article describes the development of an adaptive game system that is designed to maximize player engagement by utilizing changes in real-time electroencephalography (EEG) to adjust the level of game demand. The research consists of four main stages: (1) the development of a conceptual framework upon which to model the interaction between person and system; (2) the validation of the psychophysiological inference underpinning the loop; (3) the construction of a working prototype; and (4) an evaluation of the adaptive game. Two studies are reported. The first demonstrates the sensitivity of EEG power in the (frontal) theta and (parietal) alpha bands to changing levels of game demand. These variables were then reformulated within the working biocybernetic control loop designed to maximize player engagement. The second study evaluated the performance of an adaptive game of Tetris with respect to system behavior and user experience. Important issues for the design and evaluation of closed-loop interfaces are discussed.

Study on a Real-Time BEAM System for Diagnosis Assistance Based on a System on Chips Design

As an innovative as well as an interdisciplinary research project, this study performed an analysis of brain signals so as to establish BrainIC as an auxiliary tool for physician diagnosis. Cognition behavior sciences, embedded technology, system on chips (SOC) design and physiological signal processing are integrated in this work. Moreover, a chip is built for real-time electroencephalography (EEG) processing purposes and a Brain Electrical Activity Mapping (BEAM) system, and a knowledge database is constructed to diagnose psychosis and body challenges in learning various behaviors and signals antithesis by a fuzzy inference engine. This work is completed with a medical support system developed for the mentally disabled or the elderly abled.

Cortically coupled computer vision for rapid image search

We describe a real-time electroencephalography (EEG)-based brain-computer interface system for triaging imagery presented using rapid serial visual presentation. A target image in a sequence of nontarget distractor images elicits in the EEG a stereotypical spatiotemporal response, which can be detected. A pattern classifier uses this response to reprioritize the image sequence, placing detected targets in the front of an image stack. We use single-trial analysis based on linear discrimination to recover spatial components that reflect differences in EEG activity evoked by target versus nontarget images. We find an optimal set of spatial weights for 59 EEG sensors within a sliding 50-ms time window. Using this simple classifier allows us to process EEG in real time. The detection accuracy across five subjects is on average 92%, i.e., in a sequence of 2500 images, resorting images based on detector output results in 92% of target images being moved from a random position in the sequence to one of the first 250 images (first 10% of the sequence). The approach leverages the highly robust and invariant object recognition capabilities of the human visual system, using single-trial EEG analysis to efficiently detect neural signatures correlated with the recognition event.