scholarly journals Artifacts in EEG-Based BCI Therapies: Friend or Foe?

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 96
Author(s):  
Eric James McDermott ◽  
Philipp Raggam ◽  
Sven Kirsch ◽  
Paolo Belardinelli ◽  
Ulf Ziemann ◽  
...  
Keyword(s):  
Classification Accuracy ◽  
Therapeutic Potential ◽  
Brain Activity ◽  
Mental States ◽  
Extraction Methods ◽  
Brain State ◽  
Learning Approaches ◽  
Eeg Signal ◽  
Motor Tasks ◽  
Brain States

EEG-based brain–computer interfaces (BCI) have promising therapeutic potential beyond traditional neurofeedback training, such as enabling personalized and optimized virtual reality (VR) neurorehabilitation paradigms where the timing and parameters of the visual experience is synchronized with specific brain states. While BCI algorithms are often designed to focus on whichever portion of a signal is most informative, in these brain-state-synchronized applications, it is of critical importance that the resulting decoder is sensitive to physiological brain activity representative of various mental states, and not to artifacts, such as those arising from naturalistic movements. In this study, we compare the relative classification accuracy with which different motor tasks can be decoded from both extracted brain activity and artifacts contained in the EEG signal. EEG data were collected from 17 chronic stroke patients while performing six different head, hand, and arm movements in a realistic VR-based neurorehabilitation paradigm. Results show that the artifactual component of the EEG signal is significantly more informative than brain activity with respect to classification accuracy. This finding is consistent across different feature extraction methods and classification pipelines. While informative brain signals can be recovered with suitable cleaning procedures, we recommend that features should not be designed solely to maximize classification accuracy, as this could select for remaining artifactual components. We also propose the use of machine learning approaches that are interpretable to verify that classification is driven by physiological brain states. In summary, whereas informative artifacts are a helpful friend in BCI-based communication applications, they can be a problematic foe in the estimation of physiological brain states.

scholarly journals Artifacts in EEG-based BCI therapies: friend or foe?

2021 ◽  
Author(s):  
Eric James McDermott ◽  
Philipp Raggam ◽  
Sven Kirsch ◽  
Paolo Belardinelli ◽  
Ulf Ziemann ◽  
...  
Keyword(s):  
Therapeutic Potential ◽  
Brain Activity ◽  
Mental States ◽  
Extraction Methods ◽  
Brain State ◽  
Eeg Signal ◽  
Motor Tasks ◽  
Computer Interfaces ◽  
Eeg Data ◽  
Brain States

EEG-based brain-computer interfaces (BCI) have promising therapeutic potential beyond traditional neurofeedback training, such as enabling personalized and optimized virtual reality (VR) neurorehabilitation paradigms where the timing and parameters of the visual experience is synchronized with specific brain-states. While BCI algorithms are often designed to focus on whichever portion of a signal is most informative, in these brain-state-synchronized applications, it is of critical importance that the resulting decoder is sensitive to physiological brain activity representative of various mental states, and not to artifacts such as those arising from naturalistic movements. In this study, we compare the relative classification accuracy with which different motor tasks can be decoded from both extracted brain activity and artifacts contained in the EEG signal. EEG data was collected from 17 chronic stroke patients while performing six different head, hand, and arm movements in a realistic VR-based neurorehabilitation paradigm. Results show that the artifact component of the EEG signal is significantly more informative than brain activity to the classifier. This finding is consistent across different feature extraction methods and classification pipelines. Whereas informative artifacts are a helpful friend in BCI-based communication applications, they can be a problematic foe in the estimation of physiological brain states.

scholarly journals Macroscopic quantities of collective brain activity during wakefulness and anesthesia

2021 ◽  
Author(s):  
Adrián Ponce-Alvarez ◽  
Lynn Uhrig ◽  
Nikolas Deco ◽  
Camilo M. Signorelli ◽  
Morten L. Kringelbach ◽  
...  
Keyword(s):  
Brain Activity ◽  
Brain Regions ◽  
Brain State ◽  
Loss Of Consciousness ◽  
Information Theoretic ◽  
Maximum Entropy Models ◽  
Information Theoretic Measures ◽  
Brain States ◽  
Parietal Cortices ◽  
The Brain

AbstractThe study of states of arousal is key to understand the principles of consciousness. Yet, how different brain states emerge from the collective activity of brain regions remains unknown. Here, we studied the fMRI brain activity of monkeys during wakefulness and anesthesia-induced loss of consciousness. Using maximum entropy models, we derived collective, macroscopic properties that quantify the system’s capabilities to produce work, to contain information and to transmit it, and that indicate a phase transition from critical awake dynamics to supercritical anesthetized states. Moreover, information-theoretic measures identified those parameters that impacted the most the network dynamics. We found that changes in brain state and in state of consciousness primarily depended on changes in network couplings of insular, cingulate, and parietal cortices. Our findings suggest that the brain state transition underlying the loss of consciousness is predominantly driven by the uncoupling of specific brain regions from the rest of the network.

The Temporal Dynamics of Spontaneous Emotional Brain States and Their Implications for Mental Health

2021 ◽  
pp. 1-14
Author(s):  
Philip A. Kragel ◽  
Ahmad R. Hariri ◽  
Kevin S. LaBar
Keyword(s):  
Mental Health ◽  
Temporal Dynamics ◽  
Brain Activity ◽  
Resting State Fmri ◽  
State Transitions ◽  
Brain State ◽  
Emotional States ◽  
Affective States ◽  
Emotional Responding ◽  
Brain States

Abstract Temporal processes play an important role in elaborating and regulating emotional responding during routine mind wandering. However, it is unknown whether the human brain reliably transitions among multiple emotional states at rest and how psychopathology alters these affect dynamics. Here, we combined pattern classification and stochastic process modeling to investigate the chronometry of spontaneous brain activity indicative of six emotions (anger, contentment, fear, happiness, sadness, and surprise) and a neutral state. We modeled the dynamic emergence of these brain states during resting-state fMRI and validated the results across two population cohorts—the Duke Neurogenetics Study and the Nathan Kline Institute Rockland Sample. Our findings indicate that intrinsic emotional brain dynamics are effectively characterized as a discrete-time Markov process, with affective states organized around a neutral hub. The centrality of this network hub is disrupted in individuals with psychopathology, whose brain state transitions exhibit greater inertia and less frequent resetting from emotional to neutral states. These results yield novel insights into how the brain signals spontaneous emotions and how alterations in their temporal dynamics contribute to compromised mental health.

scholarly journals States and traits of neural irregularity in the age-varying human brain

2017 ◽  
Author(s):  
Leonhard Waschke ◽  
Malte Wöestmann ◽  
Jonas Obleser
Keyword(s):  
Brain Activity ◽  
Physical World ◽  
Permutation Entropy ◽  
Brain State ◽  
Information Theoretic ◽  
Age Related ◽  
Brain States ◽  
Sensory Encoding ◽  
Physically Identical ◽  
Human Electroencephalogram

AbstractSensory representations of the physical world and thus human percepts are susceptible to fluctuations in brain state or “neural irregularity”. Furthermore, aging brains display altered levels of irregularity. We here show that a single, within-trial information-theoretic measure (weighted permutation entropy) captures neural irregularity in the human electroencephalogram as a proxy for both, trait-like differences between individuals of varying age, and state-like fluctuations that bias perceptual decisions. First, the overall level of neural irregularity increased with participants‘ age, paralleled by a decrease in variability over time, likely indexing age-related disintegration on structural and functional levels of brain activity. Second, states of higher neural irregularity were associated with optimized sensory encoding and a subsequently increased probability of choosing the first of two physically identical stimuli. In sum, neural irregularity not only characterizes behaviorally relevant brain states, but also can identify trait-like changes that come with age.

scholarly journals Motor imagery recognition in electroencephalograms using convolutional neural networks

2020 ◽  
Vol 44 (3) ◽  
pp. 482-487
Author(s):  
A.D. Bragin ◽  
V.G. Spitsyn
Keyword(s):  
Neural Networks ◽  
Motor Imagery ◽  
Classification Accuracy ◽  
Brain Activity ◽  
Processing Unit ◽  
Eeg Signal ◽  
Good Tool ◽  
Markov Transition ◽  
Transition Field ◽  
The Brain

Electroencephalography is a widespread method to record brain signals with the use of electrodes located on the surface of the head. This method of recording the brain activity has become popular because it is relatively cheap, compact, and does not require implanting the electrodes directly into the brain. The article is devoted to a problem of recognition of motor imagery by electroencephalogram signals. The nature of such signals is complex. Characteristics of electroencephalograms are individual for every person, also depending on their age and mental state, as well as the presence of noise and interference. The multitude of these parameters should be taken into account when analyzing encephalograms. Artificial neural networks are a good tool for solving this class of problems. Their application allows combining the tasks of extracting, selecting and classifying features in one signal processing unit. Electroencephalograms are time signals and we note that Gramian Angular Fields and Markov Transition Field transforms are used to represent time series in the form of images. The article shows the possibility of using the Gramian Angular Fields and Markov Transition Field transformations of the electroencephalogram (EEG) signal for motor imagery recognition using examples of imaginary movements with the right and left hand, also studying the effect of the resolution of Gramian Angular Fields and Markov Transition Field images on the classification accuracy. The best classification accuracy of the EEG signal into the motion and state-of-rest classes is about 99%. In future, the research results can be applied in constructing the brain-computer interface.

Can the Theory of the Contingent Identity Between Sensation-States and Brain-States Be Made Empirical?

1974 ◽  
Vol 3 (3) ◽  
pp. 405-417
Author(s):  
Norman Swartz
Keyword(s):  
Mental States ◽  
Brain State ◽  
Contingent Identity ◽  
Mental Entity ◽  
Brain States

Since its inception, roughly sixteen years ago, the theory of the contingent identity of mental-states and brain-states has been argued on many fronts. I want here to examine and to try to meet one in particular of the objections raised in connection with this theory. The objection has been stated with especial force by Peter Herbst.Let us then investigate a proposition that there is a particular mental entity which is contingently identical with a particular brain state. In order to be able to test it, we must know which mental entity is supposed to be identical with what brain state. Therefore we need at least two clear and independent identifying references to serve as the basis of our proposition of identity. They must each be sufficient to individuate an entity, or else we cannot say what is identical with what, and they must be independent of each other, or else the identity proposition expressed in terms of them becomes tautologous.

scholarly journals Movie viewing elicits rich and reliable brain state dynamics

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Johan N. van der Meer ◽  
Michael Breakspear ◽  
Luke J. Chang ◽  
Saurabh Sonkusare ◽  
Luca Cocchi
Keyword(s):  
Resting State ◽  
Brain Function ◽  
Temporal Dynamics ◽  
Brain Activity ◽  
State Transitions ◽  
Brain State ◽  
The Social ◽  
Brain States ◽  
Functional States ◽  
Intrinsic Brain Activity

Abstract Adaptive brain function requires that sensory impressions of the social and natural milieu are dynamically incorporated into intrinsic brain activity. While dynamic switches between brain states have been well characterised in resting state acquisitions, the remodelling of these state transitions by engagement in naturalistic stimuli remains poorly understood. Here, we show that the temporal dynamics of brain states, as measured in fMRI, are reshaped from predominantly bistable transitions between two relatively indistinct states at rest, toward a sequence of well-defined functional states during movie viewing whose transitions are temporally aligned to specific features of the movie. The expression of these brain states covaries with different physiological states and reflects subjectively rated engagement in the movie. In sum, a data-driven decoding of brain states reveals the distinct reshaping of functional network expression and reliable state transitions that accompany the switch from resting state to perceptual immersion in an ecologically valid sensory experience.

scholarly journals Altered temporal, but intact spatial, features of transient network dynamics in psychosis

2021 ◽  
Author(s):  
Danhong Wang ◽  
Xiaolong Peng ◽  
Andrea Pelletier-Baldelli ◽  
Natasza Orlov ◽  
Amy Farabaugh ◽  
...  
Keyword(s):  
Brain Activity ◽  
Blood Oxygen Level Dependent ◽  
Occurrence Rate ◽  
Psychotic Symptoms ◽  
Brain State ◽  
Blood Oxygen Level ◽  
Spatial Features ◽  
Brain States ◽  
The Moment ◽  
Core Symptoms

AbstractContemporary models of psychosis suggest that a continuum of severity of psychotic symptoms exists, with subthreshold psychotic experiences (PEs) potentially reflecting some genetic and environmental risk factors shared with clinical psychosis. Thus, identifying abnormalities in brain activity that manifest across this continuum can shed new light on the pathophysiology of psychosis. Here, we investigated the moment-to-moment engagement of brain networks (“states”) in individuals with schizophrenia (SCZ) and PEs and identified features of these states that are associated with psychosis-spectrum symptoms. Transient brain states were defined by clustering “single snapshots” of blood oxygen level-dependent images, based on spatial similarity of the images. We found that individuals with SCZ (n = 35) demonstrated reduced recruitment of three brain states compared to demographically matched healthy controls (n = 35). Of these three illness-related states, one specific state, involving primarily the visual and salience networks, also occurred at a lower rate in individuals with persistent PEs (n = 22), compared to demographically matched healthy youth (n = 22). Moreover, the occurrence rate of this marker brain state was negatively correlated with the severity of PEs (r = −0.26, p = 0.003, n = 130). In contrast, the spatial map of this state appeared to be unaffected in the SCZ or PE groups. Thus, reduced engagement of a brain state involving the visual and salience networks was demonstrated across the psychosis continuum, suggesting that early disruptions of perceptual and affective function may underlie some of the core symptoms of the illness.

scholarly journals A Review on Mental Stress Assessment Methods Using EEG Signals

2021 ◽  
Author(s):  
Rateb Katmah ◽  
Fares Al-Shargie ◽  
Usman Tariq ◽  
Fabio Babiloni ◽  
Fadwa Al-Mughairbi ◽  
...  
Keyword(s):  
Mental Stress ◽  
Region Of Interest ◽  
Mental States ◽  
Learning Approaches ◽  
Eeg Signal ◽  
Diverse Range ◽  
Standardized Protocol ◽  
Analysis Methods ◽  
Research Findings ◽  
Rich Information

Mental stress is one of the serious factors that lead to many health problems. Scientists and physicians have developed various tools to assess the level of mental stress in its early stages. Several neuroimaging tools have been proposed in the literature to assess mental stress in the workplace. Electroencephalogram (EEG) signal is one important candidate because it contain rich information about mental states and condition. In this paper, we review the existing EEG signal analysis methods on the assessment of mental stress. The review highlights the critical differences between the research findings and argues that variations of the data analysis methods contribute to several contradictory results. The variations in results could be due to various factors including lack of standardized protocol, the brain region of interest, stressor type, experiment duration, proper EEG processing, feature extraction mechanism, and type of classifier. Therefore, the significant part related to mental stress recognition is choosing the most appropriate features. In particular, a complex and diverse range of EEG features, including time-varying, functional, and dynamic brain connections, requires integration of various methods to understand their associations with mental stress. Over this, the review suggests fusing the cortical activations with the connectivity network measures and deep learning approaches to improve the accuracy of mental stress level assessment.

scholarly journals LSD flattens the brain′s energy landscape: evidence from receptor-informed network control theory

2021 ◽  
Author(s):  
S. Parker Singleton ◽  
Andrea I Luppi ◽  
Robin L. Carhart-Harris ◽  
Josephine Cruzat ◽  
Leor Roseman ◽  
...  
Keyword(s):  
Control Theory ◽  
Subjective Experience ◽  
Brain Activity ◽  
Energy Landscape ◽  
Network Control ◽  
State Transitions ◽  
Brain State ◽  
Control Analysis ◽  
Brain States ◽  
The Brain

Psychedelics like lysergic acid diethylamide (LSD) offer a powerful window into the function of the human brain and mind, by temporarily altering subjective experience through their neurochemical effects. The RElaxed Beliefs Under Psychedelics (REBUS) model postulates that 5-HT2a receptor agonism allows the brain to explore its dynamic landscape more readily, as suggested by more diverse (entropic) brain activity. Formally, this effect is theorized to correspond to a reduction in the energy required to transition between different brain-states, i.e. a ″flattening of the energy landscape.″ However, this hypothesis remains thus far untested. Here, we leverage network control theory to map the brain′s energy landscape, by quantifying the energy required to transition between recurrent brain states. In accordance with the REBUS model, we show that LSD reduces the energy required for brain-state transitions, and, furthermore, that this reduction in energy correlates with more frequent state transitions and increased entropy of brain-state dynamics. Through network control analysis that incorporates the spatial distribution of 5-HT2a receptors, we demonstrate the specific role of this receptor in flattening the brain′s energy landscape. Also, in accordance with REBUS, we show that the occupancy of bottom-up states is increased by LSD. In addition to validating fundamental predictions of the REBUS model of psychedelic action, this work highlights the potential of receptor-informed network control theory to provide mechanistic insights into pharmacological modulation of brain dynamics.

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