Evidence of a Task-Independent Neural Signature in the Spectral Shape of the Electroencephalogram

Genetic and neurophysiological studies of electroencephalogram (EEG) have shown that an individual’s brain activity during a given cognitive task is, to some extent, determined by their genes. In fact, the field of biometrics has successfully used this property to build systems capable of identifying users from their neural activity. These studies have always been carried out in isolated conditions, such as relaxing with eyes closed, identifying visual targets or solving mathematical operations. Here we show for the first time that the neural signature extracted from the spectral shape of the EEG is to a large extent independent of the recorded cognitive task and experimental condition. In addition, we propose to use this task-independent neural signature for more precise biometric identity verification. We present two systems: one based on real cepstrums and one based on linear predictive coefficients. We obtained verification accuracies above 89% on 4 of the 6 databases used. We anticipate this finding will create a new set of experimental possibilities within many brain research fields, such as the study of neuroplasticity, neurodegenerative diseases and brain machine interfaces, as well as the mentioned genetic, neurophysiological and biometric studies. Furthermore, the proposed biometric approach represents an important advance towards real world deployments of this new technology.

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Psychological, Neurophysiological, and Mental Factors Associated With Gamma-Enhancing Neurofeedback Success

Basic and Clinical Neuroscience Journal ◽

10.32598/bcn.11.5.1878.1 ◽

2020 ◽

Vol 11 (5) ◽

pp. 701-714

Author(s):

Zeynab Khodakarami ◽

◽

Mohammad Firoozabadi ◽

Keyword(s):

Fluid Intelligence ◽

Brain Activity ◽

Cognitive Task ◽

Gain Control ◽

Learning Performance ◽

Gamma Activity ◽

Alpha Power ◽

Neurofeedback Training ◽

Eyes Closed ◽

Gamma Power

Introduction: Regarding the neurofeedback training process, previous studies indicate that 10%-50% of subjects cannot gain control over their brain activity even after repeated training sessions. This study is conducted to overcome this problem by investigating inter-individual differences in neurofeedback learning to propose some predictors for the trainability of subjects. Methods: Eight healthy female students took part in 8 (electroencephalography) EEG neurofeedback training sessions for enhancing EEG gamma power at the Oz channel. We studied participants’ preexisting fluid intelligence and EEG frequency sub-bands’ power during 2-min eyes-closed rest and a cognitive task as psychological and neurophysiological factors, concerning neurofeedback learning performance. We also assessed the self-reports of participants about mental strategies used by them during neurofeedback to identify the most effective successful strategies. Results: The results revealed that a significant percentage of individuals (25% in this study) cannot learn how to control their brain gamma activity using neurofeedback. Our findings suggest that fluid intelligence, gamma power during a cognitive task, and alpha power at rest can predict gamma-enhancing neurofeedback performance of individuals. Based on our study, neurofeedback learning is a form of implicit learning. We also found that learning without a user’s mental efforts to find out successful mental strategies, in other words, unconscious learning, lead to more success in gamma-enhancing neurofeedback. Conclusion: Our results may improve gamma neurofeedback efficacy for further clinical usage and studies by giving insight about both non-trainable individuals and effective mental strategies.

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Functional Anatomical Correlates of Controlled and Automatic Processing

Journal of Cognitive Neuroscience ◽

10.1162/08989290152541403 ◽

2001 ◽

Vol 13 (6) ◽

pp. 730-743 ◽

Cited By ~ 170

Author(s):

Johan Martijn Jansma ◽

Nick F. Ramsey ◽

Heleen A. Slagter ◽

Rene S. Kahn

Keyword(s):

Brain Activity ◽

Cognitive Task ◽

Three Dimensional ◽

Poor Performance ◽

Automatic Processing ◽

Practice Effects ◽

Controlled Processing ◽

Behavioral Studies ◽

Dorsolateral Prefrontal ◽

Types Of Information

Behavioral studies have shown that consistent practice of a cognitive task can increase the speed of performance and reduce variability of responses and error rate, reflecting a shift from controlled to automatic processing. This study examines how the shift from controlled to automatic processing changes brain activity. A verbal Sternberg task was used with continuously changing targets (novel task, NT) and with constant, practiced targets (practiced task, PT). NT and PT were presented in a blocked design and contrasted to a choice reaction time (RT) control task (CT) to isolate working memory (WM)-related activity. The three-dimensional (3-D) PRESTO functional magnetic resonance imaging (fMRI) sequence was used to measure hemodynamic responses. Behavioral data revealed that task processing became automated after practice, as responses were faster, less variable, and more accurate. This was accompanied specifically by a decrease in activation in regions related to WM (bilateral but predominantly left dorsolateral prefrontal cortex (DLPFC), right superior frontal cortex (SFC), and right frontopolar area) and the supplementary motor area. Results showed no evidence for a shift of foci of activity within or across regions of the brain. The findings have theoretical implications for understanding the functional anatomical substrates of automatic and controlled processing, indicating that these types of information processing have the same functional anatomical substrate, but differ in efficiency. In addition, there are practical implications for interpreting activity as a measure for task performance, such as in patient studies. Whereas reduced activity can reflect poor performance if a task is not sensitive to practice effects, it can reflect good performance if a task is sensitive to practice effects.

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Studying the effect of Heartfulness Meditation on Brain Activity

Inquiry@Queen's Undergraduate Research Conference Proceedings ◽

10.24908/iqurcp.10470 ◽

2018 ◽

Author(s):

Pallavi Gupta ◽

Jahnavi Mundluru ◽

Arth Patel ◽

Shankar Pathmakanthan

Keyword(s):

Problem Solving ◽

Creative Thinking ◽

Brain Activity ◽

Cognitive Task ◽

Thinking Skills ◽

Cognitive Tasks ◽

Logical Thinking ◽

Meditation Practice ◽

Guided Relaxation

Long-term meditation practice is increasingly recognized for its health benefits. Heartfulness meditation represents a quickly growing set of practices that is largely unstudied. Heartfulness is unique in that it is a meditation practice that focuses on the Heart. It helps individuals to connect to themselves and find inner peace. In order to deepen ones’ meditation, the element of Yogic Energy (‘pranahuti’) is used as an aid during meditation. The purpose of this study was to determine whether consistent EEG effects of Heartfulness meditation be observed in sixty experienced Heartfulness meditators, each of whom attended 6 testing sessions. In each session, participants performed three conditions: a set of cognitive tasks, Heartfulness guided relaxation, and Heartfulness Meditation. Participants during the cognitive portion were required to answer questions that tested their logical thinking (Cognitive Reflective Test) and creative thinking skills. (Random Associative Test) The order of condition was randomly counter balanced across six sessions. It was hypothesized that Heartfulness meditation would bring increased alpha (8-12Hz) brain activity during meditation and better cognitive task scores in sessions where the tasks followed meditation. Heartfulness meditation produces a significant decrease in brain activity (as indexed by higher levels of alpha during the early stages of meditation. As the meditation progressed deep meditative state (as indexed by higher levels of delta) were observed until the end of the condition. This lead to the conclusion that Heartfulness Meditation produces a state that is clearly distinguishable from effortful problem solving.

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Caffeine boosts preparatory attention for reward-related stimulus information

10.1101/697177 ◽

2019 ◽

Author(s):

Berry van den Berg ◽

Marlon de Jong ◽

Marty G. Woldorff ◽

Monicque M. Lorist

Keyword(s):

Brain Activity ◽

Cognitive Task ◽

Relevant Information ◽

Caffeine Intake ◽

Alpha Power ◽

Behavioral Measures ◽

Placebo Condition ◽

Electrical Brain Activity ◽

Preparatory Attention ◽

Stroop Stimulus

AbstractBoth the intake of caffeine-containing substances and the prospect of reward for performing a cognitive task have been associated with improved behavioral performance. To investigate the possible common and interactive influences of caffeine and reward-prospect on preparatory attention, we tested 24 participants during a 2-session experiment in which they performed a cued-reward color-word Stroop task. On each trial, participants were presented with a cue to inform them whether they had to prepare for presentation of a Stroop stimulus and whether they could receive a reward if they performed well on that trial. Prior to each session, participants received either coffee with caffeine (3 mg/kg bodyweight) or with placebo (3 mg/kg bodyweight lactose). In addition to behavioral measures, electroencephalography (EEG) measures of electrical brain activity were recorded. Results showed that both the intake of caffeine and the prospect of reward improved speed and accuracy, with the effects of caffeine and reward-prospect being additive on performance. Neurally, reward-prospect resulted in an enlarged contingent negative variation (CNV) and reduced posterior alpha power (indicating increased cortical activity), both hallmark neural markers for preparatory attention. Moreover, the CNV enhancement for reward-prospect trials was considerably more pronounced in the caffeine condition as compared to the placebo condition. These results thus suggest that caffeine intake boosts preparatory attention for task-relevant information, especially when performance on that task can lead to reward.

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Increased signal diversity/complexity of spontaneous EEG, but not evoked EEG responses, in ketamine-induced psychedelic state in humans

10.1101/508697 ◽

2019 ◽

Cited By ~ 2

Author(s):

Nadine Farnes ◽

Bjørn E. Juel ◽

André S. Nilsen ◽

Luis G. Romundstad ◽

Johan F. Storm

Keyword(s):

Brain Activity ◽

Altered States Of Consciousness ◽

Altered States ◽

Eeg Signal ◽

Eyes Closed ◽

Electrical Brain Activity ◽

States Of Consciousness ◽

Significant Difference ◽

Eyes Open ◽

Signal Diversity

AbstractObjectiveHow and to what extent electrical brain activity is affected in pharmacologically altered states of consciousness, where it is mainly the phenomenological content rather than the level of consciousness that is altered, is not well understood. An example is the moderately psychedelic state caused by low doses of ketamine. Therefore, we investigated whether and how measures of evoked and spontaneous electroencephalographic (EEG) signal diversity are altered by sub-anaesthetic levels of ketamine compared to normal wakefulness, and how these measures relate to subjective assessments of consciousness.MethodsHigh-density electroencephalography (EEG, 62 channels) was used to record spontaneous brain activity and responses evoked by transcranial magnetic stimulation (TMS) in 10 healthy volunteers before and after administration of sub-anaesthetic doses of ketamine in an open-label within-subject design. Evoked signal diversity was assessed using the perturbational complexity index (PCI), calculated from the global EEG responses to local TMS perturbations. Signal diversity of spontaneous EEG, with eyes open and eyes closed, was assessed by Lempel Ziv complexity (LZc), amplitude coalition entropy (ACE), and synchrony coalition entropy (SCE).ResultsAlthough no significant difference was found in the index of TMS-evoked complexity (PCI) between the sub-anaesthetic ketamine condition and normal wakefulness, all the three measures of spontaneous EEG signal diversity showed significantly increased values in the sub-anaesthetic ketamine condition. This increase in signal diversity also correlated with subjective assessment of altered states of consciousness. Moreover, spontaneous signal diversity was significantly higher when participants had eyes open compared to eyes closed, both during normal wakefulness and during influence of sub-anaesthetic ketamine doses.ConclusionThe results suggest that PCI and spontaneous signal diversity may be complementary and potentially measure different aspects of consciousness. Thus, our results seem compatible with PCI being indicative of the brain’s ability to sustain consciousness, as indicated by previous research, while it is possible that spontaneous EEG signal diversity may be indicative of the complexity of conscious content. The observed sensitivity of the latter measures to visual input seems to support such an interpretation. Thus, sub-anaesthetic ketamine may increase the complexity of both the conscious content (experience) and the brain activity underlying it, while the level, degree, or general capacity of consciousness remains largely unaffected.

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Sleeping While Awake: The Intrusion of Neural Activity Associated with Sleep Onset in the Awake Human Brain

10.1101/2020.06.04.133603 ◽

2020 ◽

Cited By ~ 2

Author(s):

Stephanie Hawes ◽

Carrie R. H. Innes ◽

Nicholas Parsons ◽

Sean P.A. Drummond ◽

Karen Caeyensberghs ◽

...

Keyword(s):

Human Brain ◽

Basal Forebrain ◽

Brain Activity ◽

Cognitive Task ◽

Sleep Onset ◽

Theoretical Modelling ◽

Related Activity ◽

Independent Dataset ◽

Time Graph ◽

The Brain

AbstractSleep can intrude into the awake human brain when sleep deprived or fatigued, even while performing cognitive tasks. However, how the brain activity associated with sleep onset can co-exist with the activity associated with cognition in the awake humans remains unexplored. Here, we used simultaneous fMRI and EEG to generate fMRI activity maps associated with EEG theta (4-7 Hz) activity associated with sleep onset. We implemented a method to track these fMRI activity maps in individuals performing a cognitive task after well-rested and sleep-deprived nights. We found frequent intrusions of the fMRI maps associated with sleep-onset in the task-related fMRI data. These sleep events elicited a pattern of transient fMRI activity, which was spatially distinct from the task-related activity in the frontal and parietal areas of the brain. They were concomitant with reduced arousal as indicated by decreased pupil size and increased response time. Graph theoretical modelling showed that the activity associated with sleep onset emerges from the basal forebrain and spreads anterior-posteriorly via the brain’s structural connectome. We replicated the key findings in an independent dataset, which suggests that the approach can be reliably used in understanding the neuro-behavioural consequences of sleep and circadian disturbances in humans.

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Stimulus Familiarity and Expectation Jointly Modulate Neural Activity in the Visual Ventral Stream

Journal of Cognitive Neuroscience ◽

10.1162/jocn_a_01281 ◽

2018 ◽

Vol 30 (9) ◽

pp. 1366-1377 ◽

Cited By ~ 12

Author(s):

Mariya E. Manahova ◽

Pim Mostert ◽

Peter Kok ◽

Jan-Mathijs Schoffelen ◽

Floris P. de Lange

Keyword(s):

Prior Knowledge ◽

Neural Activity ◽

Brain Activity ◽

Ventral Stream ◽

Sensory Response ◽

Inferotemporal Cortex ◽

Response Dynamics ◽

Lateral Occipital Complex ◽

Neurophysiological Studies ◽

Stimulus Familiarity

Prior knowledge about the visual world can change how a visual stimulus is processed. Two forms of prior knowledge are often distinguished: stimulus familiarity (i.e., whether a stimulus has been seen before) and stimulus expectation (i.e., whether a stimulus is expected to occur, based on the context). Neurophysiological studies in monkeys have shown suppression of spiking activity both for expected and for familiar items in object-selective inferotemporal cortex. It is an open question, however, if and how these types of knowledge interact in their modulatory effects on the sensory response. To address this issue and to examine whether previous findings generalize to noninvasively measured neural activity in humans, we separately manipulated stimulus familiarity and expectation while noninvasively recording human brain activity using magnetoencephalography. We observed independent suppression of neural activity by familiarity and expectation, specifically in the lateral occipital complex, the putative human homologue of monkey inferotemporal cortex. Familiarity also led to sharpened response dynamics, which was predominantly observed in early visual cortex. Together, these results show that distinct types of sensory knowledge jointly determine the amount of neural resources dedicated to object processing in the visual ventral stream.

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Fundamental Study for Relationship between Cognitive Task and Brain Activity During Car Driving

Engineering Psychology and Cognitive Ergonomics - Lecture Notes in Computer Science ◽

10.1007/978-3-642-02728-4_46 ◽

2009 ◽

pp. 434-440 ◽

Cited By ~ 6

Author(s):

Shunji Shimizu ◽

Nobuhide Hirai ◽

Fumikazu Miwakeichi ◽

Senichiro Kikuchi ◽

Yasuhito Yoshizawa ◽

...

Keyword(s):

Brain Activity ◽

Cognitive Task ◽

Fundamental Study ◽

Car Driving

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Distinct Montages of Slow Oscillatory Transcranial Direct Current Stimulation (so-tDCS) Constitute Different Mechanisms during Quiet Wakefulness

Brain Sciences ◽

10.3390/brainsci9110324 ◽

2019 ◽

Vol 9 (11) ◽

pp. 324

Author(s):

Ping Koo-Poeggel ◽

Verena Böttger ◽

Lisa Marshall

Keyword(s):

Slow Wave Sleep ◽

Brain Activity ◽

Electrode Placement ◽

Brain State ◽

Electrode Pair ◽

Alpha Power ◽

Applied Current ◽

Large Power ◽

Quiet Wakefulness ◽

Eyes Closed

Slow oscillatory- (so-) tDCS has been applied in many sleep studies aimed to modulate brain rhythms of slow wave sleep and memory consolidation. Yet, so-tDCS may also modify coupled oscillatory networks. Efficacy of weak electric brain stimulation is however variable and dependent upon the brain state at the time of stimulation (subject and/or task-related) as well as on stimulation parameters (e.g., electrode placement and applied current. Anodal so-tDCS was applied during wakefulness with eyes-closed to examine efficacy when deviating from the dominant brain rhythm. Additionally, montages of different electrodes size and applied current strength were used. During a period of quiet wakefulness bilateral frontolateral stimulation (F3, F4; return electrodes at ipsilateral mastoids) was applied to two groups: ‘Group small’ (n = 16, f:8; small electrodes: 0.50 cm2; maximal current per electrode pair: 0.26 mA) and ‘Group Large’ (n = 16, f:8; 35 cm2; 0.35 mA). Anodal so-tDCS (0.75 Hz) was applied in five blocks of 5 min epochs with 1 min stimulation-free epochs between the blocks. A finger sequence tapping task (FSTT) was used to induce comparable cortical activity across sessions and subject groups. So-tDCS resulted in a suppression of alpha power over the parietal cortex. Interestingly, in Group Small alpha suppression occurred over the standard band (8–12 Hz), whereas for Group Large power of individual alpha frequency was suppressed. Group Small also revealed a decrease in FSTT performance at retest after stimulation. It is essential to include concordant measures of behavioral and brain activity to help understand variability and poor reproducibility in oscillatory-tDCS studies.

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SIFNet: Electromagnetic Source Imaging Framework Using Deep Neural Networks

10.1101/2020.05.11.089185 ◽

2020 ◽

Author(s):

Rui Sun ◽

Abbas Sohrabpour ◽

Shuai Ye ◽

Bin He

Keyword(s):

Neural Networks ◽

Brain Research ◽

Focal Epilepsy ◽

Brain Activity ◽

Simulated Data ◽

Epileptogenic Zone ◽

Source Imaging ◽

Accurate Localization ◽

Interictal Eeg ◽

Ill Posed

AbstractElectroencephalography (EEG) and magnetoencephalography (MEG) are used to measure brain activity, noninvasively, and are useful tools for brain research and clinical management of brain disorders. Tremendous effort has been made in solving the inverse source imaging problem from EEG/MEG measurements. This is a challenging ill-posed problem, since the number of measurements is much smaller than the number of possible sources in the brain. Various methods have been developed to estimate underlying brain sources from noninvasive EEG/MEG as this can offer insight about the underlying brain electrical activity with significantly improved spatial resolution. In this work, we propose a novel data-driven Source Imaging Framework using deep learning neural networks (SIFNet), where (1) a simulation pipeline is designed to model realistic brain activation and EEG/MEG signals to train generalizable neural networks, (2) and a residual convolutional neural network is trained using the simulated data, capable of estimating source distributions from EEG/MEG recordings. The performance of our proposed SIFNet approach is evaluated in a series of computer simulations, which indicates the excellent performance of SIFNet outperforming conventional weighted minimum norm algorithms that were tested in this work. The SIFNet is further tested by analyzing interictal EEG data recorded in a clinical setting from a focal epilepsy patient. The results of this clinical data analysis indicate accurate localization of epileptogenic activity as validated by the epileptogenic zone clinically determined in this patient. In sum, the proposed SIFNet approach promises to offer an alternative solution to the EEG/MEG inverse source imaging problem, shows promising signs of being robust against measurement noise, and is easy to implement, therefore, being translatable to clinical practice.

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