scholarly journals Robust anticipation of continuous steering actions from electroencephalographic data during simulated driving

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Giovanni M. Di Liberto ◽  
Michele Barsotti ◽  
Giovanni Vecchiato ◽  
Jonas Ambeck-Madsen ◽  
Maria Del Vecchio ◽  
...  
Keyword(s):  
Neural Activity ◽  
Low Frequency ◽  
Action Planning ◽  
Cognitive State ◽  
Motor Tasks ◽  
Neural Signals ◽  
Individual Subject ◽  
Emergency Braking ◽  
Driving Task ◽  
Linear Decoding

AbstractDriving a car requires high cognitive demands, from sustained attention to perception and action planning. Recent research investigated the neural processes reflecting the planning of driving actions, aiming to better understand the factors leading to driving errors and to devise methodologies to anticipate and prevent such errors by monitoring the driver’s cognitive state and intention. While such anticipation was shown for discrete driving actions, such as emergency braking, there is no evidence for robust neural signatures of continuous action planning. This study aims to fill this gap by investigating continuous steering actions during a driving task in a car simulator with multimodal recordings of behavioural and electroencephalography (EEG) signals. System identification is used to assess whether robust neurophysiological signatures emerge before steering actions. Linear decoding models are then used to determine whether such cortical signals can predict continuous steering actions with progressively longer anticipation. Results point to significant EEG signatures of continuous action planning. Such neural signals show consistent dynamics across participants for anticipations up to 1 s, while individual-subject neural activity could reliably decode steering actions and predict future actions for anticipations up to 1.8 s. Finally, we use canonical correlation analysis to attempt disentangling brain and non-brain contributors to the EEG-based decoding. Our results suggest that low-frequency cortical dynamics are involved in the planning of steering actions and that EEG is sensitive to that neural activity. As a result, we propose a framework to investigate anticipatory neural activity in realistic continuous motor tasks.

scholarly journals An EEG Experimental Study Evaluating the Performance of Texas Instruments ADS1299

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3721 ◽  
Author(s):  
Usman Rashid ◽  
Imran Niazi ◽  
Nada Signal ◽  
Denise Taylor
Keyword(s):  
Experimental Study ◽  
Mixed Models ◽  
Signal To Noise Ratio ◽  
Low Cost ◽  
Low Frequency ◽  
Low Power Consumption ◽  
Motor Tasks ◽  
Negative Peak ◽  
Eeg Data ◽  
Healthy Participants

Texas Instruments ADS1299 is an attractive choice for low cost electroencephalography (EEG) devices owing to its low power consumption and low input referred noise. To date, there have been no rigorous evaluations of its performance. In this EEG experimental study we evaluated the performance of the ADS1299 against a high quality laboratory-based system. Two self-paced lower limb motor tasks were performed by 22 healthy participants. Recorded power across delta, theta, alpha, and beta EEG bands, the power ratio across the motor tasks, pre-movement noise, and signal-to-noise ratio were obtained for evaluation. The amplitude and time of the negative peak in the movement-related cortical potentials (MRCPs) extracted from the EEG data were also obtained. Using linear mixed models, no statistically significant differences (p > 0.05) were found in any of these measures across the two systems. These findings were further supported by evaluation of cosine similarity, waveform differences, and topographic maps. There were statistically significant differences in MRCPs across the motor tasks in both systems. We conclude that the performance of the ADS1299 in combination with wet Ag/AgCl electrodes is analogous to that of a laboratory-based system in a low frequency (<40 Hz) EEG recording.

scholarly journals Predictive coding of action intentions in dorsal and ventral visual stream is based on visual anticipations, memory-based information and motor preparation

2018 ◽  
Author(s):  
Simona Monaco ◽  
Giulia Malfatti ◽  
Alessandro Zendron ◽  
Elisa Pellencin ◽  
Luca Turella
Keyword(s):  
Visual Information ◽  
Visual Memory ◽  
Visual Recognition ◽  
Predictive Coding ◽  
Action Planning ◽  
Neural Signals ◽  
Motor Representation ◽  
Visual Stream ◽  
Ventral Visual Stream ◽  
Action Intention

AbstractPredictions of upcoming movements are based on several types of neural signals that span the visual, somatosensory, motor and cognitive system. Thus far, pre-movement signals have been investigated while participants viewed the object to be acted upon. Here, we studied the contribution of information other than vision to the classification of preparatory signals for action, even in absence of online visual information. We used functional magnetic resonance imaging (fMRI) and multivoxel pattern analysis (MVPA) to test whether the neural signals evoked by visual, memory-based and somato-motor information can be reliably used to predict upcoming actions in areas of the dorsal and ventral visual stream during the preparatory phase preceding the action, while participants were lying still. Nineteen human participants (nine women) performed one of two actions towards an object with their eyes open or closed. Despite the well-known role of ventral stream areas in visual recognition tasks and the specialization of dorsal stream areas in somato-motor processes, we decoded action intention in areas of both streams based on visual, memory-based and somato-motor signals. Interestingly, we could reliably decode action intention in absence of visual information based on neural activity evoked when visual information was available, and vice-versa. Our results show a similar visual, memory and somato-motor representation of action planning in dorsal and ventral visual stream areas that allows predicting action intention across domains, regardless of the availability of visual information.

scholarly journals Patterns of EEG Activity in Individuals with Autism Spectrum Disorders

2016 ◽  
Vol 21 (3) ◽  
pp. 47-55
Author(s):  
M.A. Zhukova
Keyword(s):  
Autism Spectrum Disorders ◽  
Neural Activity ◽  
Low Frequency ◽  
Strong Relationship ◽  
Autism Spectrum ◽  
Semantic Integration ◽  
Adults With Autism ◽  
Eeg Activity ◽  
Spectrum Disorders ◽  
Cortical Regions

The article reviews most recent findings on neural activity in children and adults with autism spectrum disorders (ASD). Most of the studies demonstrate decreased connectivity in cortical regions, excitatory/inhibitory imbalance and atypical processing of language in people with ASD. It is argued that difficulties in semantic integration are connected to selective insensitivity to language, which is manifested in atypical N400 ERP component. In the article we analyze the data suggesting a strong relationship between ASD and epilepsy and argue that the comorbidity is more prevalent among individuals who have cognitive dysfunction. The EEG profile of people with ASD suggests U-shaped alterations with excess in high- and low-frequency EEG bands. We critically analyze the “broken mirror” hypothesis of ASD and demonstrate findings which challenge this theory.

scholarly journals Dissociable oscillatory networks support gain and loss processing in human orbitofrontal cortex

2021 ◽  
Author(s):  
Ignacio Saez ◽  
Jack Lin ◽  
Edward Chang ◽  
Josef Parvizi ◽  
Robert T. Knight ◽  
...  
Keyword(s):  
Neural Activity ◽  
Orbitofrontal Cortex ◽  
Low Frequency ◽  
Brain Regions ◽  
Reward Processing ◽  
Neural Oscillations ◽  
Beta Band ◽  
Neuronal Ensembles ◽  
Gain And Loss

AbstractHuman neuroimaging and animal studies have linked neural activity in orbitofrontal cortex (OFC) to valuation of positive and negative outcomes. Additional evidence shows that neural oscillations, representing the coordinated activity of neuronal ensembles, support information processing in both animal and human prefrontal regions. However, the role of OFC neural oscillations in reward-processing in humans remains unknown, partly due to the difficulty of recording oscillatory neural activity from deep brain regions. Here, we examined the role of OFC neural oscillations (<30Hz) in reward processing by combining intracranial OFC recordings with a gambling task in which patients made economic decisions under uncertainty. Our results show that power in different oscillatory bands are associated with distinct components of reward evaluation. Specifically, we observed a double dissociation, with a selective theta band oscillation increase in response to monetary gains and a beta band increase in response to losses. These effects were interleaved across OFC in overlapping networks and were accompanied by increases in oscillatory coherence between OFC electrode sites in theta and beta band during gain and loss processing, respectively. These results provide evidence that gain and loss processing in human OFC are supported by distinct low-frequency oscillations in networks, and provide evidence that participating neuronal ensembles are organized functionally through oscillatory coherence, rather than local anatomical segregation.

scholarly journals Dynamic Altered Amplitude of Low-Frequency Fluctuations in Patients With Major Depressive Disorder

2021 ◽  
Vol 12 ◽  
Author(s):  
Ruiping Zheng ◽  
Yuan Chen ◽  
Yu Jiang ◽  
Mengmeng Wen ◽  
Bingqian Zhou ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Neural Activity ◽  
Temporal Variability ◽  
Brain Activity ◽  
Low Frequency ◽  
First Episode ◽  
Posterior Lobe ◽  
Major Depressive ◽  
Drug Naïve ◽  
The Right

Background: Major depressive disorder (MDD) has demonstrated abnormalities of static intrinsic brain activity measured by amplitude of low-frequency fluctuation (ALFF). Recent studies regarding the resting-state functional magnetic resonance imaging (rs-fMRI) have found the brain activity is inherently dynamic over time. Little is known, however, regarding the temporal dynamics of local neural activity in MDD. Here, we investigated whether temporal dynamic changes in spontaneous neural activity are influenced by MDD.Methods: We recruited 81 first-episode, drug-naive MDD patients and 64 age-, gender-, and education-matched healthy controls who underwent rs-fMRI. A sliding-window approach was then adopted for the estimation of dynamic ALFF (dALFF), which was used to measure time-varying brain activity and then compared between the two groups. The relationship between altered dALFF variability and clinical variables in MDD patients was also analyzed.Results: MDD patients showed increased temporal variability (dALFF) mainly focused on the bilateral thalamus, the bilateral superior frontal gyrus, the right middle frontal gyrus, the bilateral cerebellum posterior lobe, and the vermis. Furthermore, increased dALFF variability values in the right thalamus and right cerebellum posterior lobe were positively correlated with MDD symptom severity.Conclusions: The overall results suggest that altered temporal variability in corticocerebellar–thalamic–cortical circuit (CCTCC), involved in emotional, executive, and cognitive, is associated with drug-naive, first-episode MDD patients. Moreover, our study highlights the vital role of abnormal dynamic brain activity in the cerebellar hemisphere associated with CCTCC in MDD patients. These findings may provide novel insights into the pathophysiological mechanisms of MDD.

scholarly journals Interpersonal Comparison of Utility by Measuring Neural Activity

2021 ◽  
Author(s):  
Kaosu Matsumori ◽  
Kazuki Iijima ◽  
Yukihito Yomogida ◽  
Kenji Matsumoto
Keyword(s):  
Neural Activity ◽  
Optimal Taxation ◽  
Cost Benefit ◽  
Third Party ◽  
Impossibility Theorem ◽  
Interpersonal Comparison ◽  
Neural Signals ◽  
Collective Decisions ◽  
Economic Goods ◽  
Household Incomes

Aggregating welfare across individuals to reach collective decisions is one of the most fundamental problems in our society. Interpersonal comparison of utility is pivotal and inevitable for welfare aggregation, because if each person's utility is not interpersonally comparable, there is no rational aggregation procedure that simultaneously satisfies even some very mild conditions for validity (Arrow's impossibility theorem). However, scientific methods for interpersonal comparison of utility have thus far not been available. Here, we have developed a method for interpersonal comparison of utility based on brain signals, by measuring the neural activity of participants performing gambling tasks. We found that activity in the medial frontal region was correlated with changes in expected utility, and that, for the same amount of money, the activity evoked was larger for participants with lower household incomes than for those with higher household incomes. Furthermore, we found that the ratio of neural signals from lower-income participants to those of higher-income participants coincided with estimates of their psychological pleasure by "impartial spectators", i.e. disinterested third-party participants satisfying specific conditions. Finally, we derived a decision rule based on aggregated welfare from our experimental data, and confirmed that it was applicable to a distribution problem. These findings suggest that our proposed method for interpersonal comparison of utility enables scientifically reasonable welfare aggregation by escaping from Arrow's impossibility and has implications for the fair distribution of economic goods. Our method can be further applied for evidence-based policy making in nations that use cost-benefit analyses or optimal taxation theory for policy evaluation.

scholarly journals Including measures of high gamma power can improve the decoding of natural speech from EEG

2019 ◽  
Author(s):  
Shyanthony R. Synigal ◽  
Emily S. Teoh ◽  
Edmund C. Lalor
Keyword(s):  
Brain Imaging ◽  
Neural Activity ◽  
Speech Processing ◽  
Signal To Noise Ratio ◽  
Low Frequency ◽  
Natural Speech ◽  
Low Frequencies ◽  
High Gamma ◽  
Gamma Power ◽  
Speech Tracking

ABSTRACTThe human auditory system is adept at extracting information from speech in both single-speaker and multi-speaker situations. This involves neural processing at the rapid temporal scales seen in natural speech. Non-invasive brain imaging (electro-/magnetoencephalography [EEG/MEG]) signatures of such processing have shown that the phase of neural activity below 16 Hz tracks the dynamics of speech, whereas invasive brain imaging (electrocorticography [ECoG]) has shown that such rapid processing is even more strongly reflected in the power of neural activity at high frequencies (around 70-150 Hz; known as high gamma). The aim of this study was to determine if high gamma power in scalp recorded EEG carries useful stimulus-related information, despite its reputation for having a poor signal to noise ratio. Furthermore, we aimed to assess whether any such information might be complementary to that reflected in well-established low frequency EEG indices of speech processing. We used linear regression to investigate speech envelope and attention decoding in EEG at low frequencies, in high gamma power, and in both signals combined. While low frequency speech tracking was evident for almost all subjects as expected, high gamma power also showed robust speech tracking in a minority of subjects. This same pattern was true for attention decoding using a separate group of subjects who undertook a cocktail party attention experiment. For the subjects who showed speech tracking in high gamma power, the spatiotemporal characteristics of that high gamma tracking differed from that of low-frequency EEG. Furthermore, combining the two neural measures led to improved measures of speech tracking for several subjects. Overall, this indicates that high gamma power EEG can carry useful information regarding speech processing and attentional selection in some subjects and combining it with low frequency EEG can improve the mapping between natural speech and the resulting neural responses.

scholarly journals Disrupted Spontaneous Neural Activity in Patients With Thyroid-Associated Ophthalmopathy: A Resting-State fMRI Study Using Amplitude of Low-Frequency Fluctuation

2021 ◽  
Vol 15 ◽  
Author(s):  
Wen Chen ◽  
Qian Wu ◽  
Lu Chen ◽  
Jiang Zhou ◽  
Huan-Huan Chen ◽  
...  
Keyword(s):  
Neural Activity ◽  
Resting State ◽  
Low Frequency ◽  
Occipital Lobe ◽  
Frequency Fluctuation ◽  
Clinical Activity ◽  
Spontaneous Neural Activity ◽  
Thyroid Associated Ophthalmopathy ◽  
Middle Occipital Gyrus ◽  
Left Middle

PurposeThe purpose of the study was to investigate the brain functional alteration in patients with thyroid-associated ophthalmopathy (TAO) by evaluating the spontaneous neural activity changes using resting-state functional magnetic resonance imaging (rs-fMRI) with the amplitude of low-frequency fluctuation (ALFF) method.Materials and MethodsThe rs-fMRI data of 30 TAO patients (15 active and 15 inactive) and 15 healthy controls (HCs) were included for analyses. The ALFF values were calculated and compared among groups. Correlations between ALFF values and clinical metrics were assessed.ResultsCompared with HCs, active TAOs showed significantly decreased ALFF values in the left middle occipital gyrus, superior occipital gyrus, and cuneus. Compared with inactive TAOs, active TAOs showed significantly increased ALFF values in the bilateral precuneus. Additionally, inactive TAOs showed significantly decreased ALFF values in the left middle occipital gyrus, superior occipital gyrus, cuneus, and bilateral precuneus than HCs. The ALFF value in the right precuneus of TAOs was positively correlated with clinical activity score (r = 0.583, P &lt; 0.001) and Mini-Mental State Examination (MMSE) score (r = 0.377, P = 0.040), and negatively correlated with disease duration (r = −0.382, P = 0.037). Moreover, the ALFF value in the left middle occipital gyrus of TAOs was positively correlated with visual acuity (r = 0.441, P = 0.015).ConclusionTAO patients had altered spontaneous brain activities in the left occipital lobe and bilateral precuneus. The neuropsychological aspect of the disease should be noticed during clinical diagnosis and treatment.

scholarly journals Prestimulus feedback connectivity biases the content of visual experiences

2019 ◽  
Vol 116 (32) ◽  
pp. 16056-16061 ◽  
Author(s):  
Elie Rassi ◽  
Andreas Wutz ◽  
Nadia Müller-Voggel ◽  
Nathan Weisz
Keyword(s):  
Neural Activity ◽  
Activity Patterns ◽  
Low Frequency ◽  
Brain Regions ◽  
Stimulus Onset ◽  
Neural Excitability ◽  
Gamma Frequency ◽  
Face Area ◽  
Oscillatory Power ◽  
The Impact

Ongoing fluctuations in neural excitability and in networkwide activity patterns before stimulus onset have been proposed to underlie variability in near-threshold stimulus detection paradigms—that is, whether or not an object is perceived. Here, we investigated the impact of prestimulus neural fluctuations on the content of perception—that is, whether one or another object is perceived. We recorded neural activity with magnetoencephalography (MEG) before and while participants briefly viewed an ambiguous image, the Rubin face/vase illusion, and required them to report their perceived interpretation in each trial. Using multivariate pattern analysis, we showed robust decoding of the perceptual report during the poststimulus period. Applying source localization to the classifier weights suggested early recruitment of primary visual cortex (V1) and ∼160-ms recruitment of the category-sensitive fusiform face area (FFA). These poststimulus effects were accompanied by stronger oscillatory power in the gamma frequency band for face vs. vase reports. In prestimulus intervals, we found no differences in oscillatory power between face vs. vase reports in V1 or in FFA, indicating similar levels of neural excitability. Despite this, we found stronger connectivity between V1 and FFA before face reports for low-frequency oscillations. Specifically, the strength of prestimulus feedback connectivity (i.e., Granger causality) from FFA to V1 predicted not only the category of the upcoming percept but also the strength of poststimulus neural activity associated with the percept. Our work shows that prestimulus network states can help shape future processing in category-sensitive brain regions and in this way bias the content of visual experiences.

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