scholarly journals The human brain reveals resting state activity patterns that are predictive of biases in attitudes towards robots.

2021 ◽  
Author(s):  
Cesco Willemse ◽  
Francesco Bossi ◽  
Jacopo Cavazza ◽  
Serena Marchesi ◽  
Vittorio Murino ◽  
...  
Keyword(s):  
Resting State ◽  
Humanoid Robot ◽  
Social Role ◽  
Activity Patterns ◽  
Gamma Activity ◽  
Beta Activity ◽  
Eeg Signal ◽  
Intentional Agents ◽  
The Mind ◽  
Electroencephalogram Eeg

The increasing presence of robots in society necessitates a deeper understanding into what attitudes people have toward robots. People may treat robots as mechanistic artifacts or may consider them to be intentional agents. This might result in explaining robots’ behavior as stemming from operations of the mind (intentional interpretation) or as a result of mechanistic design (mechanistic interpretation). Here, we examined whether individual attitudes toward robots can be differentiated on the basis of default neural activity pattern during resting state, measured with electroencephalogram (EEG). Participants observed scenarios in which a humanoid robot was depicted performing various actions embedded in daily contexts. Before they were introduced to the task, we measured their resting state EEG activity. We found that resting state EEG beta activity differentiated people who were later inclined toward interpreting robot behaviors as either mechanistic or intentional. This pattern is similar to the pattern of activity in the default mode network, which was previously demonstrated to have a social role. In addition, gamma activity observed when participants were making decisions about a robot’s behavior indicates a relationship between theory of mind and said attitudes. Thus, we provide evidence that individual biases toward treating robots as either intentional agents or mechanistic artifacts can be detected at the neural level, already in a resting state EEG signal.

scholarly journals The human brain reveals resting state activity patterns that are predictive of biases in attitudes toward robots

2020 ◽  
Vol 5 (46) ◽  
pp. eabb6652 ◽  
Author(s):  
Francesco Bossi ◽  
Cesco Willemse ◽  
Jacopo Cavazza ◽  
Serena Marchesi ◽  
Vittorio Murino ◽  
...  
Keyword(s):  
Resting State ◽  
Humanoid Robot ◽  
Social Role ◽  
Activity Patterns ◽  
Gamma Activity ◽  
Beta Activity ◽  
Eeg Signal ◽  
Intentional Agents ◽  
The Mind ◽  
Electroencephalogram Eeg

The increasing presence of robots in society necessitates a deeper understanding into what attitudes people have toward robots. People may treat robots as mechanistic artifacts or may consider them to be intentional agents. This might result in explaining robots’ behavior as stemming from operations of the mind (intentional interpretation) or as a result of mechanistic design (mechanistic interpretation). Here, we examined whether individual attitudes toward robots can be differentiated on the basis of default neural activity pattern during resting state, measured with electroencephalogram (EEG). Participants observed scenarios in which a humanoid robot was depicted performing various actions embedded in daily contexts. Before they were introduced to the task, we measured their resting state EEG activity. We found that resting state EEG beta activity differentiated people who were later inclined toward interpreting robot behaviors as either mechanistic or intentional. This pattern is similar to the pattern of activity in the default mode network, which was previously demonstrated to have a social role. In addition, gamma activity observed when participants were making decisions about a robot’s behavior indicates a relationship between theory of mind and said attitudes. Thus, we provide evidence that individual biases toward treating robots as either intentional agents or mechanistic artifacts can be detected at the neural level, already in a resting state EEG signal.

High Frequencies in QEEG Are Related to the Level of Insight in Patients With Schizophrenia

2018 ◽  
Vol 49 (5) ◽  
pp. 316-320 ◽  
Author(s):  
Mehmet Kemal Arikan ◽  
Baris Metin ◽  
Sinem Zeynep Metin ◽  
Emine Elif Tülay ◽  
Nevzat Tarhan
Keyword(s):  
Negative Correlation ◽  
Resting State ◽  
Psychotic Disorders ◽  
Treatment Compliance ◽  
Self Report ◽  
Gamma Activity ◽  
Beta Activity ◽  
High Frequencies ◽  
Gamma Power

Lack of insight is a neurocognitive problem commonly encountered in patients with psychotic disorders that negatively affects treatment compliance and prognosis. Measurement of insight is based on self-report scales, which are limited due to subjectivity. This study aimed to determine the correlation between resting state beta and gamma power in 23 patients with schizophrenia and insight. It was observed that as beta and gamma power measured via qualitative electroencephalography (qEEG) increased the level of insight decreased. Negative correlation was found in F3, C3, Cz for gamma activity and in F3 and C3 for beta activity. This finding indicates that resting state qEEG could be used to evaluate the level of insight in patients with schizophrenia.

scholarly journals Cortical and Striatal Electroencephalograms and Apomorphine Effects in the FUS Mouse Model of Amyotrophic Lateral Sclerosis

2021 ◽  
pp. 1-15
Author(s):  
Vasily Vorobyov ◽  
Alexander Deev ◽  
Frank Sengpiel ◽  
Vladimir Nebogatikov ◽  
Aleksey A. Ustyugov
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Motor Neurons ◽  
Cortical Neurons ◽  
Primary Motor Cortex ◽  
Beta Activity ◽  
Systemic Injection ◽  
Eeg Spectra ◽  
Electroencephalogram Eeg ◽  
Lateral Sclerosis

Background: Amyotrophic lateral sclerosis (ALS) is characterized by degeneration of motor neurons resulting in muscle atrophy. In contrast to the lower motor neurons, the role of upper (cortical) neurons in ALS is yet unclear. Maturation of locomotor networks is supported by dopaminergic (DA) projections from substantia nigra to the spinal cord and striatum. Objective: To examine the contribution of DA mediation in the striatum-cortex networks in ALS progression. Methods: We studied electroencephalogram (EEG) from striatal putamen (Pt) and primary motor cortex (M1) in ΔFUS(1–359)-transgenic (Tg) mice, a model of ALS. EEG from M1 and Pt were recorded in freely moving young (2-month-old) and older (5-month-old) Tg and non-transgenic (nTg) mice. EEG spectra were analyzed for 30 min before and for 60 min after systemic injection of a DA mimetic, apomorphine (APO), and saline. Results: In young Tg versus nTg mice, baseline EEG spectra in M1 were comparable, whereas in Pt, beta activity in Tg mice was enhanced. In older Tg versus nTg mice, beta dominated in EEG from both M1 and Pt, whereas theta and delta 2 activities were reduced. In younger Tg versus nTg mice, APO increased theta and decreased beta 2 predominantly in M1. In older mice, APO effects in these frequency bands were inversed and accompanied by enhanced delta 2 and attenuated alpha in Tg versus nTg mice. Conclusion: We suggest that revealed EEG modifications in ΔFUS(1–359)-transgenic mice are associated with early alterations in the striatum-cortex interrelations and DA transmission followed by adaptive intracerebral transformations.

AGE-BASED VARIATIONS OF FRACTAL STRUCTURE OF EEG SIGNAL IN PATIENTS WITH EPILEPSY

Fractals ◽  
2018 ◽  
Vol 26 (04) ◽  
pp. 1850051 ◽  
Author(s):  
HAMIDREZA NAMAZI ◽  
SAJAD JAFARI
Keyword(s):  
Fractal Dimension ◽  
Brain Research ◽  
Age Groups ◽  
Eeg Signal ◽  
Brain Disorders ◽  
Eeg Signals ◽  
Important Challenge ◽  
Patients With Epilepsy ◽  
Process Complexity ◽  
Electroencephalogram Eeg

It is known that aging affects neuroplasticity. On the other hand, neuroplasticity can be studied by analyzing the electroencephalogram (EEG) signal. An important challenge in brain research is to study the variations of neuroplasticity during aging for patients suffering from epilepsy. This study investigates the variations of the complexity of EEG signal during aging for patients with epilepsy. For this purpose, we employed fractal dimension as an indicator of process complexity. We classified the subjects in different age groups and computed the fractal dimension of their EEG signals. Our investigations showed that as patients get older, their EEG signal will be more complex. The method of investigation that has been used in this study can be further employed to study the variations of EEG signal in case of other brain disorders during aging.

scholarly journals A Survey on Machine Learning Algorithms for Vision State Classification and Prediction Through Electroencephalogram (EEG) Signal

2020 ◽  
pp. 426-429
Author(s):  
Devipriya A ◽  
Brindha D ◽  
Kousalya A
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Problem Area ◽  
Machine Learning Algorithms ◽  
Eeg Signal ◽  
Ensemble Machine Learning ◽  
State Classification ◽  
Machine Learning Model ◽  
Knn Classification ◽  
Electroencephalogram Eeg

Eye state ID is a sort of basic time-arrangement grouping issue in which it is additionally a problem area in the late exploration. Electroencephalography (EEG) is broadly utilized in a vision state in order to recognize people perception form. Past examination was approved possibility of AI & measurable methodologies of EEG vision state arrangement. This research means to propose novel methodology for EEG vision state distinguishing proof utilizing Gradual Characteristic Learning (GCL) in light of neural organizations. GCL is a novel AI methodology which bit by bit imports and prepares includes individually. Past examinations have confirmed that such a methodology is appropriate for settling various example acknowledgment issues. Nonetheless, in these past works, little examination on GCL zeroed in its application to temporal-arrangement issues. Thusly, it is as yet unclear if GCL will be utilized for adapting the temporal-arrangement issues like EEG vision state characterization. Trial brings about this examination shows that, with appropriate element extraction and highlight requesting, GCL cannot just productively adapt to time-arrangement order issues, yet additionally display better grouping execution as far as characterization mistake rates in correlation with ordinary and some different methodologies. Vision state classification is performed and discussed with KNN classification and accuracy is enriched finally discussed the vision state classification with ensemble machine learning model.

scholarly journals Beyond the low frequency fluctuations morning and evening differences in human brain

2019 ◽  
Author(s):  
Magdalena Fafrowicz ◽  
Bartosz Bohaterewicz ◽  
Anna Ceglarek ◽  
Monika Cichocka ◽  
Koryna Lewandowska ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Human Performance ◽  
Brain Activity ◽  
Activity Patterns ◽  
Low Frequency ◽  
Basic Mechanism ◽  
Time Of Day ◽  
Resting State Functional Connectivity ◽  
Circadian Typology

Human performance, alertness, and most biological functions express rhythmic fluctuations across a 24-hour-period. This phenomenon is believed to originate from differences in both circadian and homeostatic sleep-wake regulatory processes. Interactions between these processes result in time-of-day modulations of behavioral performance as well as brain activity patterns. Although the basic mechanism of the 24-hour clock is conserved across evolution, there are interindividual differences in the timing of sleep-wake cycles, subjective alertness and functioning throughout the day. The study of circadian typology differences has increased during the last few years, especially research on extreme chronotypes, which provide a unique way to investigate the effects of sleep-wake regulation on cerebral mechanisms. Using functional magnetic resonance imaging (fMRI), we assessed the influence of chronotype and time-of-day on resting-state functional connectivity. 29 extreme morning- and 34 evening-type participants underwent two fMRI sessions: about one hour after wake-up time (morning) and about ten hours after wake-up time (evening), scheduled according to their declared habitual sleep-wake pattern on a regular working day. Analysis of obtained neuroimaging data disclosed only an effect of time of day on resting-state functional connectivity; there were different patterns of functional connectivity between morning and evening sessions. The results of our study showed no differences between extreme morning-type and evening-type individuals. We demonstrate that circadian and homeostatic influences on the resting-state functional connectivity have a universal character, unaffected by circadian typology.

scholarly journals Prefrontal High Gamma in ECoG tags periodicity of musical rhythms in perception and imagination

2019 ◽  
Author(s):  
S. A. Herff ◽  
C. Herff ◽  
A. J. Milne ◽  
G. D. Johnson ◽  
J. J. Shih ◽  
...  
Keyword(s):  
Auditory Processing ◽  
Right Hemisphere ◽  
Brain Activity ◽  
Activity Patterns ◽  
Superior Temporal Gyrus ◽  
Gamma Activity ◽  
Rhythm Perception ◽  
High Gamma ◽  
The Right ◽  
Musical Rhythms

AbstractRhythmic auditory stimuli are known to elicit matching activity patterns in neural populations. Furthermore, recent research has established the particular importance of high-gamma brain activity in auditory processing by showing its involvement in auditory phrase segmentation and envelope-tracking. Here, we use electrocorticographic (ECoG) recordings from eight human listeners, to see whether periodicities in high-gamma activity track the periodicities in the envelope of musical rhythms during rhythm perception and imagination. Rhythm imagination was elicited by instructing participants to imagine the rhythm to continue during pauses of several repetitions. To identify electrodes whose periodicities in high-gamma activity track the periodicities in the musical rhythms, we compute the correlation between the autocorrelations (ACC) of both the musical rhythms and the neural signals. A condition in which participants listened to white noise was used to establish a baseline. High-gamma autocorrelations in auditory areas in the superior temporal gyrus and in frontal areas on both hemispheres significantly matched the autocorrelation of the musical rhythms. Overall, numerous significant electrodes are observed on the right hemisphere. Of particular interest is a large cluster of electrodes in the right prefrontal cortex that is active during both rhythm perception and imagination. This indicates conscious processing of the rhythms’ structure as opposed to mere auditory phenomena. The ACC approach clearly highlights that high-gamma activity measured from cortical electrodes tracks both attended and imagined rhythms.

scholarly journals Tonic resting-state hubness supports high-frequency activity defined verbal-memory encoding network in epilepsy

2019 ◽  
Author(s):  
Chaitanya Ganne ◽  
Walter Hinds ◽  
James Kragel ◽  
Xiaosong He ◽  
Noah Sideman ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Verbal Memory ◽  
Resting State ◽  
High Frequency ◽  
Cognitive Networks ◽  
Gamma Activity ◽  
List Type ◽  
Healthy Controls ◽  
Memory Encoding ◽  
Memory Network

AbstractHigh-frequency gamma activity of verbal-memory encoding using invasive-electroencephalogram coupled has laid the foundation for numerous studies testing the integrity of memory in diseased populations. Yet, the functional connectivity characteristics of networks subserving these HFA-memory linkages remains uncertain. By integrating this electrophysiological biomarker of memory encoding from IEEG with resting-state BOLD fluctuations, we estimated the segregation and hubness of HFA-memory regions in drug-resistant epilepsy patients and matched healthy controls. HFA-memory regions express distinctly different hubness compared to neighboring regions in health and in epilepsy, and this hubness was more relevant than segregation in predicting verbal memory encoding. The HFA-memory network comprised regions from both the cognitive control and primary processing networks, validating that effective verbal-memory encoding requires multiple functions, and is not dominated by a central cognitive core. Our results demonstrate a tonic intrinsic set of functional connectivity, which provides the necessary conditions for effective, phasic, task-dependent memory encoding.HighlightsHigh frequency memory activity in IEEG corresponds to specific BOLD changes in resting-state data.HFA-memory regions had lower hubness relative to control brain nodes in both epilepsy patients and healthy controls.HFA-memory network displayed hubness and participation (interaction) values distinct from other cognitive networks.HFA-memory network shared regional membership and interacted with other cognitive networks for successful memory encoding.HFA-memory network hubness predicted both concurrent task (phasic) and baseline (tonic) verbal-memory encoding success.

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