scholarly journals Retrospective confidence judgements in general-knowledge questions: Magnetoencephalograhy correlates

2021 ◽  
Author(s):  
Beatriz Martín-Luengo ◽  
Dmitrii Altukhov ◽  
Maria Alexeeva ◽  
Alina Leminen
Keyword(s):  
Linear Models ◽  
Time Window ◽  
Brain Activity ◽  
General Knowledge ◽  
Theta Band ◽  
Mixed Effect ◽  
Online Assessments ◽  
Memory Monitoring ◽  
The Brain

Memory monitoring processes are online assessments of the quality of our retrieval. Despite their importance for cognition, few studies on episodic memory and perceptual discrimination studied their neural dynamics and reported diverse results. Also, research showed increased theta in correct lexical identifications, but its monitoring was not investigated. We used MEG to study the brain activity underpinning memory monitoring of retrospective confidence judgments. 29 participants answered multiple-choice general knowledge questions and rated the confidence of their choice, while MEG was recorded. Mixed-effect linear models in the averaged single-trial responses showed a marginal difference for high versus low confidence answers in left dorso-parietal and occipital sensors at 260-320 ms after the presentation of alternatives. Signal power analysis in the 400-800 ms time window showed differences in theta band for low versus high confidence hits and miss trials. However, no differences were found for high hits and misses, which may reflect that in terms of monitoring, both answers are equal for participants. These results support the findings of increased theta power for correct semantic identification extending them to the monitoring processes.

Adaptation of Rehabilitation System Based on User’s Mental Engagement

2015 ◽  
Author(s):  
Ehsan T. Esfahani ◽  
Shrey Pareek ◽  
Pramod Chembrammel ◽  
Mostafa Ghobadi ◽  
Thenkurussi Kesavadas
Keyword(s):  
Brain Activity ◽  
Difficulty Level ◽  
The Novel ◽  
Theta Band ◽  
Robotic Rehabilitation ◽  
Designed Experiments ◽  
Power Spectral ◽  
Rehabilitation System ◽  
Alpha Beta ◽  
The Brain

Recognition of user’s mental engagement is imperative to the success of robotic rehabilitation. The paper explores the novel paradigm in robotic rehabilitation of using Passive BCI as opposed to the conventional Active ones. We have designed experiments to determine a user’s level of mental engagement. In our experimental study, we record the brain activity of 3 healthy subjects during multiple sessions where subjects need to navigate through a maze using a haptic system with variable resistance/assistance. Using the data obtained through the experiments we highlight the drawbacks of using conventional workload metrics as indicators of human engagement, thus asserting that Motor and Cognitive Workloads be differentiated. Additionally we propose a new set of features: differential PSD of Cz-Poz at alpha, Beta and Sigma band, (Mental engagement) and relative C3-C4 at beta (Motor Workload) to distinguish Normal Cases from those instances when haptic where applied with an accuracy of 92.93%. Mental engagement is calculated using the power spectral density of the Theta band (4–7 Hz) in the parietal-midline (Pz) with respect to the central midline (Cz). The above information can be used to adjust robotic rehabilitation parameters I accordance with the user’s needs. The adjustment may be in the force levels, difficulty level of the task or increasing the speed of the task.

scholarly journals Beta-band desynchronization reflects uncertainty in effector selection during motor planning

2021 ◽  
Author(s):  
Milou J.L. van Helvert ◽  
Leonie Oostwoud Wijdenes ◽  
Linda Geerligs ◽  
W. Pieter Medendorp
Keyword(s):  
Time Window ◽  
Brain Activity ◽  
Motor Planning ◽  
Reaction Times ◽  
Motor Preparation ◽  
Motion Artifact ◽  
Theta Band ◽  
Beta Band ◽  
Target Uncertainty ◽  
Band Power

AbstractWhile beta-band activity during motor planning is known to be modulated by uncertainty about where to act, less is known about its modulations to uncertainty about how to act. To investigate this issue, we recorded oscillatory brain activity with EEG while human participants (n = 17) performed a hand choice reaching task. The reaching hand was either predetermined or of participants’ choice, and the target was close to one of the two hands or at about equal distance from both. To measure neural activity in a motion-artifact-free time window, the location of the upcoming target was cued 1000-1500 ms before the presentation of the target, whereby the cue was valid in 50% of trials. As evidence for motor planning during the cueing phase, behavioral observations showed that the cue affected later hand choice. Furthermore, reaction times were longer in the choice than in the predetermined trials, supporting the notion of a competitive process for hand selection. Modulations of beta-band power over central cortical regions, but not alpha-band or theta-band power, were in line with these observations. During the cueing period, reaches in predetermined trials were preceded by larger decreases in beta-band power than reaches in choice trials. Cue direction did not affect reaction times or beta-band power, which may be due to the cue being invalid in 50% of trials, retaining effector uncertainty during motor planning. Our findings suggest that effector uncertainty, similar to target uncertainty, selectively modulates beta-band power during motor planning.New & NoteworthyWhile reach-related beta-band power in central cortical areas is known to modulate with the number of potential targets, here we show, using a cueing paradigm, that the power in this frequency band, but not in the alpha or theta-band, is also modulated by the uncertainty of which hand to use. This finding supports the notion that multiple possible effector-specific actions can be specified in parallel up to the level of motor preparation.

Classification Algorithms for EEG-Based Brain-Computer Interface

2019 ◽  
pp. 52-73
Author(s):  
Sravanth Kumar Ramakuri ◽  
Chinmay Chakraboirty ◽  
Anudeep Peddi ◽  
Bharat Gupta
Keyword(s):  
Brain Activity ◽  
Biomedical Signal Processing ◽  
Computer Interface ◽  
Eeg Signals ◽  
Large Sets ◽  
Eeg Data ◽  
Important Measurement ◽  
The Brain

In recent years, a vast research is concentrated towards the development of electroencephalography (EEG)-based human-computer interface in order to enhance the quality of life for medical as well as nonmedical applications. The EEG is an important measurement of brain activity and has great potential in helping in the diagnosis and treatment of mental and brain neuro-degenerative diseases and abnormalities. In this chapter, the authors discuss the classification of EEG signals as a key issue in biomedical research for identification and evaluation of the brain activity. Identification of various types of EEG signals is a complicated problem, requiring the analysis of large sets of EEG data. Representative features from a large dataset play an important role in classifying EEG signals in the field of biomedical signal processing. So, to reduce the above problem, this research uses three methods to classify through feature extraction and classification schemes.

scholarly journals Looking for effects of qualia on event-related brain potentials of close others in search for a cause of the similarity of qualia assumed across individuals

F1000Research ◽  
2018 ◽  
Vol 3 ◽  
pp. 316
Author(s):  
Sheila Bouten ◽  
Hugo Pantecouteau ◽  
J. Bruno Debruille
Keyword(s):  
Time Window ◽  
Brain Activity ◽  
Conscious Experience ◽  
Event Related Potentials ◽  
The Other ◽  
Brain Potentials ◽  
Frontal Electrode ◽  
Related Potentials ◽  
The Individual ◽  
The Brain

Qualia, the individual instances of subjective conscious experience, are private events. However, in everyday life, we assume qualia of others and their perceptual worlds, to be similar to ours. One way this similarity is possible is if qualia of others somehow contribute to the production of qualia by our own brain and vice versa. To test this hypothesis, we focused on the mean voltages of event-related potentials (ERPs) in the time-window of the P600 component, whose amplitude correlates positively with conscious awareness. These ERPs were elicited by images of the international affective picture system in 16 pairs of friends, siblings or couples going side by side through hyperscanning without having to interact. Each of the 32 members of these 16 pairs faced one half of the screen and could not see what the other member was presented with on the other half. One stimulus occurred on each half simultaneously. The sameness of these stimulus pairs was manipulated as well as the participants’ belief in that sameness by telling subjects’ pairs that they were going to be presented with the same stimuli in two blocks and with different ones in the two others. ERPs were more positive at all electrode subsets for stimulus pairs that were inconsistent with the belief than for those that were consistent. In the N400 time window, at frontal electrode sites, ERPs were again more positive for inconsistent than for consistent stimuli. As participants had no way to see the stimulus their partner was presented with and thus no way to detect inconsistence, these data might reveal an impact of the qualia of a person on the brain activity of another. Such impact could provide a research avenue when trying to explain the similarity of qualia across individuals.

Electroencephalographic Evidence on the Strategies of Adaptation to the Factors of Monotony

2009 ◽  
Vol 12 (1) ◽  
pp. 32-45 ◽  
Author(s):  
Elena V. Aslanyan ◽  
Valery N. Kiroy
Keyword(s):  
Power Plants ◽  
Work Performance ◽  
Brain Activity ◽  
The State ◽  
High Quality ◽  
High Lability ◽  
Occupational Selection ◽  
The Brain

In a series of studies, in which 19 apparently healthy male volunteers participated, on the basis of a comparative analysis of the bioelectric brain activity and work performance, it is shown that two strategies of adaptation to the factors of monotony are possible. One of them is based on the maintenance of a high quality of activity even at the price of a considerable reduction in the functional state of the brain; the second is based on the maintenance of the functional status of the brain even at the expense of the short-term loss of control over realizable performance. The factor conditioning the long term inability to support continual high quality of performance under the conditions of monotony is a high lability in nervous processes. The resistance to the effects of the factors of monotony is connected, on the other hand, with the low lability of nervous processes with a certain predominance of excitatory processes over inhibiting processes. The electrographic correlates of the development of the state of monotony represent an increase in the EEG of an alert person of the slow spectra (theta and alpha), and also beta-2 waves, as well as a reduction in the intrahemispheric coherence of alpha-waves. These results can be used for the development of control systems for the state of the operators who work in conditions of monotony (pilots, the operators of electric trains, the operators of power plants, including atomic power plants, and others), as well as in the occupational selection of individuals for jobs involving work under such conditions.

scholarly journals Conditional Generative Neural Decoding with Structured CNN Feature Prediction

2020 ◽  
Vol 34 (03) ◽  
pp. 2629-2636 ◽  
Author(s):  
Changde Du ◽  
Changying Du ◽  
Lijie Huang ◽  
Huiguang He
Keyword(s):  
State Of The Art ◽  
Structural Information ◽  
Brain Activity ◽  
Training Data ◽  
Visual Images ◽  
Neural Decoding ◽  
Scientific Value ◽  
Decoding Accuracy ◽  
The Brain

Decoding visual contents from human brain activity is a challenging task with great scientific value. Two main facts that hinder existing methods from producing satisfactory results are 1) typically small paired training data; 2) under-exploitation of the structural information underlying the data. In this paper, we present a novel conditional deep generative neural decoding approach with structured intermediate feature prediction. Specifically, our approach first decodes the brain activity to the multilayer intermediate features of a pretrained convolutional neural network (CNN) with a structured multi-output regression (SMR) model, and then inverts the decoded CNN features to the visual images with an introspective conditional generation (ICG) model. The proposed SMR model can simultaneously leverage the covariance structures underlying the brain activities, the CNN features and the prediction tasks to improve the decoding accuracy and interpretability. Further, our ICG model can 1) leverage abundant unpaired images to augment the training data; 2) self-evaluate the quality of its conditionally generated images; and 3) adversarially improve itself without extra discriminator. Experimental results show that our approach yields state-of-the-art visual reconstructions from brain activities.

scholarly journals Representational pattern similarity of electrical brain activity reveals rapid and specific prediction during language comprehension

2020 ◽  
Author(s):  
Ryan J. Hubbard ◽  
Kara D. Federmeier
Keyword(s):  
Time Window ◽  
Brain Activity ◽  
Activity Patterns ◽  
Early Time ◽  
Final Word ◽  
Critical Function ◽  
Electrical Brain Activity ◽  
Pattern Similarity ◽  
Use Context ◽  
The Brain

AbstractPredicting upcoming stimuli and events is a critical function of the brain, and understanding the mechanisms of prediction has thus become a central topic in neuroscientific research. Language provides a fertile testing ground for examining predictive mechanisms, as comprehenders use context to predict different features of upcoming words. Although there is a substantive body of research on prediction in language, many aspects of the mechanisms of prediction remain elusive, in part due to a lack of methodological tools to probe prediction formation in the moment. To elucidate what features are neurally pre-activated and when, we used representational similarity analysis (RSA) on data from a sentence reading task (Federmeier et al., 2007). We compared EEG activity patterns elicited by expected and unexpected sentence final words to patterns from the preceding words of the sentence, in both strongly and weakly constraining sentences. Pattern similarity with the final word was increased in an early time window (suggestive of visual feature activation) following the presentation of the pre-final word, and this increase was modulated by both expectancy and constraint (greatest for strongly constrained expected words). This was not seen at earlier words, suggesting that predictions are precisely timed. Additionally, pre-final word activity – the predicted representation - had negative similarity with later final word activity, but only for strongly expected words. Together, these findings shed light on the mechanisms of prediction in the brain: features of upcoming stimuli are rapidly pre-activated following related cues, but the predicted information may receive reduced subsequent processing upon confirmation.

scholarly journals Importance of Brain gym as exercise in physiotherapy and rehabilitation

2020 ◽  
Vol 11 (SPL4) ◽  
pp. 1386-1389
Author(s):  
Vaishnavi V Siroya ◽  
Waqar M Naqvi ◽  
Chaitanya A Kulkarni
Keyword(s):  
Personal Development ◽  
Brain Activity ◽  
Corporate Member ◽  
Age Group ◽  
Brain Gym ◽  
Neuroscience Research ◽  
Educational Kinesiology ◽  
Brain Based Learning ◽  
The Brain

This is not unprecedented that a whole industry, effectively promoted, has grown to promote the notion of 'brain-based' learning among other ideas. Much attention has been put on connecting advances in neuroscience research with educational approaches to boost learning. Brain Gym is a curriculum basically focused on theories of neuroscience and educational kinesiology. Intervention of brain gym consists of integrated, cross-lateral, balance-requiring movements that mechanically activate both hemispheres of the brain through the motor and sensory cortexes. The study describes the importance of brain gym exercise in physiotherapy. Exercise can stimulate the brain in such a way that neurons are often in a condition to handle the different data from outside and are capable of responding to a "corporate member" of their duty in compliance with parts of brain activity by means of the principle of "brain-body link". Brain Gym is a great source of personal development, enabling individuals to obtain rapid transformations and also improve quality of life in different age group.

scholarly journals Synthetic Vocal Tracts - A Review

2020 ◽  
Vol 11 (SPL3) ◽  
pp. 1837-1842
Author(s):  
Kandhal Yazhini P ◽  
Yuvaraj Babu K
Keyword(s):  
High Speed ◽  
Speech Synthesis ◽  
Vocal Tract ◽  
Assessment Tool ◽  
Brain Activity ◽  
Severe Disabilities ◽  
Viable Solution ◽  
Promising Development ◽  
The Brain

Synthetic vocal tracts are gadgets powered by a computer system capable of translating the brain activity into synthesized speech, by decoding the movements of muscles involved in vocalization, using advanced computer programming. New standardized method for the development of synthetic vocal tract is the 3D vocal tract model with the binary conversion. Few of the typical features considered while creating the synthetic vocal tract are fundamental frequency, perturbation measure, jitter and change in pitch. The tissue engineered larynx is the promising development in synthetic vocal tract treatment in case of patients with vocal fold repair and regeneration. The future of this interesting technology lies in using high speed video endoscopy based synthetic vocal cords. The review was done based on the articles obtained from various platforms. This review article elaborates about the principle of synthetic vocal tract. Quality of the article used was assessed using a quality assessment tool and graded as strong, moderate and weak. The aim of this study is to understand the concept of synthetic vocal tract and its significance. Synthetic vocal tract is a recently established biomedical tool that has come as a boon in treating patients with severe disabilities. Speech synthesis is evolving as a viable solution as more research is being carried out on this. To understand the full significance of this crucial technique, more research has to be carried out on this field of biomedical engineering and viable solutions need to be developed, so that this novel technique is fully utilized.

scholarly journals Semantic representations during language comprehension are affected by context

2021 ◽  
Author(s):  
Fatma Deniz ◽  
Christine Tseng ◽  
Leila Wehbe ◽  
Jack L Gallant
Keyword(s):  
Natural Language ◽  
Semantic Information ◽  
Brain Activity ◽  
Word Meaning ◽  
Brain Responses ◽  
Neuroimaging Data ◽  
The Brain ◽  
Model Approach ◽  
Prefrontal Cortices

The meaning of words in natural language depends crucially on context. However, most neuroimaging studies of word meaning use isolated words and isolated sentences with little context. Because the brain may process natural language differently from how it processes simplified stimuli, there is a pressing need to determine whether prior results on word meaning generalize to natural language. We investigated this issue by directly comparing the brain representation of semantic information across four conditions that vary in context. fMRI was used to record human brain activity while four subjects (two female) read words presented in four different conditions: narratives (Narratives), isolated sentences (Sentences), blocks of semantically similar words (Semantic Blocks), and isolated words (Single Words). Using a voxelwise encoding model approach, we find two clear and consistent effects of increasing context. First, stimuli with more context (Narratives, Sentences) evoke brain responses with substantially higher SNR across bilateral visual, temporal, parietal, and prefrontal cortices compared to stimuli with little context (Semantic Blocks, Single Words). Second, increasing context increases the representation of semantic information across bilateral temporal, parietal, and prefrontal cortices at the group level. However, in individual subjects, only natural language stimuli (Narratives) consistently evoke widespread representation of semantic information across the cortical surface. These results show that context has large effects on both the quality of neuroimaging data and on the representation of meaning in the brain, and they imply that the results of neuroimaging studies that use stimuli with little context may not generalize well to the natural regime.

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