Aligning Brain Activity and Sketch in Multi-Modal CAD Interface

2014 ◽  
Author(s):  
Somayeh B. Shafiei ◽  
Ehsan Tarkesh Esfahani
Keyword(s):  
Time Window ◽  
Mental Workload ◽  
Brain Activity ◽  
Time Lag ◽  
Cognitive State ◽  
Eeg Signals ◽  
Scalp Eeg ◽  
Cognitive States ◽  
Series Of Experiments ◽  
Dynamic Time

This paper investigates the proper synchronization of sketch data and cognitive states in a multi-modal CAD interface. In a series of experiments, 5 subjects were instructed to watch and then explain 6 mechanical mechanisms by sketching them on a touch based screen. Simultaneously, subject’s brain waves were recorded in terms of electroencephalogram (EEG) signals from 9 locations on the scalp. EEG signals were analyzed and translated into mental workload and cognitive state. A dynamic time window was then constructed to align these features with sketch features such that the combination of two modalities maximizes the classification of gesture from non-gesture strokes. Quadratic Discriminant Analysis (QDA) was used as classification method. Our experimental results show that the best temporal alignment for workload and sketch analysis starts from 30% time lag with previous stroke and ends before 30% time lag with next stroke.

scholarly journals Decode Brain System: A Dynamic Adaptive Convolutional Quorum Voting Approach for Variable-Length EEG Data

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Tao Xu ◽  
Yun Zhou ◽  
Zekai Hou ◽  
Wenlan Zhang
Keyword(s):  
Time Window ◽  
Brain Activity ◽  
Sliding Window ◽  
Variable Length ◽  
Cognitive State ◽  
Human Beings ◽  
Processing Variable ◽  
Cognitive States ◽  
Eeg Data ◽  
Eeg Recordings

The brain is a complex and dynamic system, consisting of interacting sets and the temporal evolution of these sets. Electroencephalogram (EEG) recordings of brain activity play a vital role to decode the cognitive process of human beings in learning research and application areas. In the real world, people react to stimuli differently, and the duration of brain activities varies between individuals. Therefore, the length of EEG recordings in trials gathered in the experiment is variable. However, current approaches either fix the length of EEG recordings in each trial which would lose information hidden in the data or use the sliding window which would consume large computation on overlapped parts of slices. In this paper, we propose TOO (Traverse Only Once), a new approach for processing variable-length EEG trial data. TOO is a convolutional quorum voting approach that breaks the fixed structure of the model through convolutional implementation of sliding windows and the replacement of the fully connected layer by the 1 × 1 convolutional layer. Each output cell generated from 1 × 1 convolutional layer corresponds to each slice created by a sliding time window, which reflects changes in cognitive states. Then, TOO employs quorum voting on output cells and determines the cognitive state representing the entire single trial. Our approach provides an adaptive model for trials of different lengths with traversing EEG data of each trial only once to recognize cognitive states. We design and implement a cognitive experiment and obtain EEG data. Using the data collecting from this experiment, we conducted an evaluation to compare TOO with a state-of-art sliding window end-to-end approach. The results show that TOO yields a good accuracy (83.58%) at the trial level with a much lower computation (11.16%). It also has the potential to be used in variable signal processing in other application areas.

This Is Your Brain on Autopilot: Neural Indices of Driver Workload and Engagement During Partial Vehicle Automation

2021 ◽  
pp. 001872082110390
Author(s):  
Amy S. McDonnell ◽  
Trent G. Simmons ◽  
Gus G. Erickson ◽  
Monika Lohani ◽  
Joel M. Cooper ◽  
...  
Keyword(s):  
Mental Workload ◽  
Cognitive State ◽  
Alpha Power ◽  
Age Cohorts ◽  
Cognitive States ◽  
Driver Workload ◽  
Automated Technology ◽  
Vehicle Automation ◽  
Visual Engagement ◽  
Insight Into

Objective This research explores the effect of partial vehicle automation on neural indices of mental workload and visual engagement during on-road driving. Background There is concern that the introduction of automated technology in vehicles may lead to low driver stimulation and subsequent disengagement from the driving environment. Simulator-based studies have examined the effect of automation on a driver’s cognitive state, but it is unknown how the conclusions translate to on-road driving. Electroencephalographic (EEG) measures of frontal theta and parietal alpha can provide insight into a driver’s mental workload and visual engagement while driving under various conditions. Method EEG was recorded from 71 participants while driving on the roadway. We examined two age cohorts, on two different highway configurations, in four different vehicles, with partial vehicle automation both engaged and disengaged. Results Analysis of frontal theta and parietal alpha power revealed that there was no change in mental workload or visual engagement when driving manually compared with driving under partial vehicle automation. Conclusion Drivers new to the technology remained engaged with the driving environment when operating under partial vehicle automation. These findings suggest that the concern surrounding driver disengagement under vehicle automation may need to be tempered, at least for drivers new to the experience. Application These findings expand our understanding of the effects of partial vehicle automation on drivers’ cognitive states.

Reducing Sketch Inhibition During Concept Generation: Psychophysiological Evidence of the Effect of Interventions

2015 ◽  
Author(s):  
Wan-Lin Hu ◽  
Joran Booth ◽  
Tahira Reid
Keyword(s):  
Mental Workload ◽  
Self Report ◽  
Cognitive State ◽  
Concept Generation ◽  
Warm Up ◽  
Cognitive States ◽  
Test Conditions ◽  
Skin Response ◽  
Nasa Tlx ◽  
Art Activities

This research investigated the effect of warm-up activities on cognitive states during concept generation. Psychophysiological tools including electroencephalography (EEG) and galvanic skin response (GSR) were used along with self-report measures (NASA TLX). Participants were divided into 3 test conditions: 1) no warm-up activity; 2) simple warm-up activities; 3) sketch-inhibition reducing activities. All participants did the same short design task. Results show that those who did a warm-up prior to ideation had a decrease in stress, especially for those who were personally familiar with the design problem. The art activities especially improved engagement for younger participants. We also saw that females who used the art-based activities reported lower mental workload during ideation and greater pride in their sketches. However, the warm-ups did not produce any difference in the number of ideas or other metrics of performance. These preliminary results indicate that warm-up activities, especially the art-based ones, help reduce inhibition by calming the cognitive state.

scholarly journals A framework for closed-loop neurofeedback for real-time EEG decoding

2019 ◽  
Author(s):  
Greta Tuckute ◽  
Sofie Therese Hansen ◽  
Troels Wesenberg Kjaer ◽  
Lars Kai Hansen
Keyword(s):  
Real Time ◽  
Closed Loop ◽  
Brain Activity ◽  
Time Signal ◽  
Software Framework ◽  
Cognitive State ◽  
Scalp Eeg ◽  
State Classification ◽  
Dry Electrode ◽  
Wide Range

AbstractNeurofeedback based on real-time brain imaging allows for targeted training of brain activity with demonstrated clinical applications. A rapid technical development of electroen-cephalography (EEG)-based systems and an increasing interest in cognitive training has lead to a call for accessible and adaptable software frameworks. Here, we present and outline the core components of a novel open-source neurofeedback framework based on scalp EEG signals for real-time neuroimaging, state classification and closed-loop feedback.The software framework includes real-time signal preprocessing, adaptive artifact rejection, and cognitive state classification from scalp EEG. The framework is implemented using exclusively Python source code to allow for diverse functionality, high modularity, and easy extendibility of software development depending on the experimenter’s needs.As a proof of concept, we demonstrate the functionality of our new software framework by implementing an attention training paradigm using a consumer-grade, dry-electrode EEG system. Twenty-two participants were trained on a single neurofeedback session with behavioral pre- and post-training sessions within three consecutive days. We demonstrate a mean decoding error rate of 34.3% (chance=50%) of subjective attentional states. Hence, cognitive states were decoded in real-time by continuously updating classification models on recently recorded EEG data without the need for any EEG recordings prior to the neurofeedback session.The proposed software framework allows a wide range of users to actively engage in the development of novel neurofeedback tools to accelerate improvements in neurofeedback as a translational and therapeutic tool.

scholarly journals Oscillation-based connectivity architecture is dominated by an intrinsic spatial organization, not cognitive state or frequency

2020 ◽  
Author(s):  
Parham Mostame ◽  
Sepideh Sadaghiani
Keyword(s):  
Information Exchange ◽  
Spatial Organization ◽  
Brain Activity ◽  
The State ◽  
Neural Oscillations ◽  
Cognitive State ◽  
State Dependency ◽  
Frequency Bands ◽  
Cognitive States ◽  
The Impact

AbstractFunctional connectivity (FC) of neural oscillations (~1-150Hz) is thought to facilitate neural information exchange across brain areas by forming malleable neural ensembles in the service of cognitive processes. However, neural oscillations and their FC are not restricted to certain cognitive demands and continuously unfold in all cognitive states. To what degree is the spatial organization of oscillation-based FC affected by cognitive state or governed by an intrinsic architecture? And what is the impact of oscillation frequency and FC mode (phase-versus amplitude coupling)? Using ECoG recordings of 18 presurgical patients, we quantified the state-dependency of oscillation-based FC in five canonical frequency bands and across an array of 6 task states. For both phase- and amplitude coupling, static FC analysis revealed a spatially largely state-invariant (i.e. intrinsic) component in all frequency bands. Further, the observed intrinsic FC pattern was spatially similar across all frequency bands. However, temporally independent FC dynamics in each frequency band allow for frequency-specific malleability in information exchange. In conclusion, the spatial organization of oscillation-based FC is largely stable over cognitive states, i.e. primarily intrinsic in nature, and shared across frequency bands. The state-invariance is in line with prior findings at the other temporal extreme of brain activity, the infraslow range (~<0.1Hz) observed in fMRI. Our observations have implications for conceptual frameworks of oscillation-based FC and the analysis of task-related FC changes.

scholarly journals A Systematic Review for Cognitive State-Based QoE/UX Evaluation

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3439
Author(s):  
Edgar Bañuelos-Lozoya ◽  
Gabriel González-Serna ◽  
Nimrod González-Franco ◽  
Olivia Fragoso-Diaz ◽  
Noé Castro-Sánchez
Keyword(s):  
Systematic Review ◽  
User Experience ◽  
Mental Workload ◽  
Cognitive State ◽  
Sources Of Information ◽  
Cognitive States ◽  
Starting Point ◽  
The Relationship

Traditional evaluation of user experience is subjective by nature, for what is sought is to use data from physiological and behavioral sensors to interpret the relationship that the user’s cognitive states have with the elements of a graphical interface and interaction mechanisms. This study presents the systematic review that was developed to determine the cognitive states that are being investigated in the context of Quality of Experience (QoE)/User Experience (UX) evaluation, as well as the signals and characteristics obtained, machine learning models used, evaluation architectures proposed, and the results achieved. Twenty-nine papers published in 2014–2019 were selected from eight online sources of information, of which 24% were related to the classification of cognitive states, 17% described evaluation architectures, and 41% presented correlations between different signals, cognitive states, and QoE/UX metrics, among others. The amount of identified studies was low in comparison with cognitive state research in other contexts, such as driving or other critical activities; however, this provides a starting point to analyze and interpret states such as mental workload, confusion, and mental stress from various human signals and propose more robust QoE/UX evaluation architectures.

Complexity Measures of Brain Electrophysiological Activity

2010 ◽  
Vol 24 (2) ◽  
pp. 131-135 ◽  
Author(s):  
Włodzimierz Klonowski ◽  
Pawel Stepien ◽  
Robert Stepien
Keyword(s):  
Brain Activity ◽  
Deterministic Chaos ◽  
Epileptic Seizures ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Complexity Measures ◽  
Electrophysiological Activity ◽  
Signal Complexity ◽  
Physiological Sleep ◽  
The Brain

Over 20 years ago, Watt and Hameroff (1987 ) suggested that consciousness may be described as a manifestation of deterministic chaos in the brain/mind. To analyze EEG-signal complexity, we used Higuchi’s fractal dimension in time domain and symbolic analysis methods. Our results of analysis of EEG-signals under anesthesia, during physiological sleep, and during epileptic seizures lead to a conclusion similar to that of Watt and Hameroff: Brain activity, measured by complexity of the EEG-signal, diminishes (becomes less chaotic) when consciousness is being “switched off”. So, consciousness may be described as a manifestation of deterministic chaos in the brain/mind.

scholarly journals EEG Fractal Analysis Reflects Brain Impairment after Stroke

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 592
Author(s):  
Maria Rubega ◽  
Emanuela Formaggio ◽  
Franco Molteni ◽  
Eleonora Guanziroli ◽  
Roberto Di Marco ◽  
...  
Keyword(s):  
Early Stage ◽  
Brain Activity ◽  
Phase 1 ◽  
Behavioral Changes ◽  
Stroke Survivors ◽  
Eeg Signals ◽  
Novel Treatments ◽  
Fractal Measures ◽  
Underlying Mechanisms ◽  
Random Fluctuations

Stroke is the commonest cause of disability. Novel treatments require an improved understanding of the underlying mechanisms of recovery. Fractal approaches have demonstrated that a single metric can describe the complexity of seemingly random fluctuations of physiological signals. We hypothesize that fractal algorithms applied to electroencephalographic (EEG) signals may track brain impairment after stroke. Sixteen stroke survivors were studied in the hyperacute (<48 h) and in the acute phase (∼1 week after stroke), and 35 stroke survivors during the early subacute phase (from 8 days to 32 days and after ∼2 months after stroke): We compared resting-state EEG fractal changes using fractal measures (i.e., Higuchi Index, Tortuosity) with 11 healthy controls. Both Higuchi index and Tortuosity values were significantly lower after a stroke throughout the acute and early subacute stage compared to healthy subjects, reflecting a brain activity which is significantly less complex. These indices may be promising metrics to track behavioral changes in the very early stage after stroke. Our findings might contribute to the neurorehabilitation quest in identifying reliable biomarkers for a better tailoring of rehabilitation pathways.

Context-specific abnormalities of the central executive network in first-episode psychosis: relationship with cognition

2020 ◽  
pp. 1-10
Author(s):  
Deepak K. Sarpal ◽  
Goda Tarcijonas ◽  
Finnegan J. Calabro ◽  
William Foran ◽  
Gretchen L. Haas ◽  
...  
Keyword(s):  
Cognitive Control ◽  
Psychotic Disorders ◽  
First Episode Psychosis ◽  
First Episode ◽  
Cognitive State ◽  
Cognitive States ◽  
Episode Psychosis ◽  
Dorsolateral Prefrontal ◽  
Parietal Lobule ◽  
Context Specific

Abstract Background Cognitive impairments, which contribute to the profound functional deficits observed in psychotic disorders, have found to be associated with abnormalities in trial-level cognitive control. However, neural tasks operate within the context of sustained cognitive states, which can be assessed with ‘background connectivity’ following the removal of task effects. To date, little is known about the integrity of brain processes supporting the maintenance of a cognitive state in individuals with psychotic disorders. Thus, here we examine background connectivity during executive processing in a cohort of participants with first-episode psychosis (FEP). Methods The following fMRI study examined background connectivity of the dorsolateral prefrontal cortex (DLPFC), during working memory engagement in a group of 43 patients with FEP, relative to 35 healthy controls (HC). Findings were also examined in relation to measures of executive function. Results The FEP group relative to HC showed significantly lower background DLPFC connectivity with bilateral superior parietal lobule (SPL) and left inferior parietal lobule. Background connectivity between DLPFC and SPL was also positively associated with overall cognition across all subjects and in our FEP group. In comparison, resting-state frontoparietal connectivity did not differ between groups and was not significantly associated with overall cognition, suggesting that psychosis-related alterations in executive networks only emerged during states of goal-oriented behavior. Conclusions These results provide novel evidence indicating while frontoparietal connectivity at rest appears intact in psychosis, when engaged during a cognitive state, it is impaired possibly undermining cognitive control capacities in FEP.

Information-based decoding of the coupling among human brain activity and movement paths

2021 ◽  
pp. 1-10
Author(s):  
Shahul Mujib Kamal ◽  
Norazryana Mat Dawi ◽  
Hamidreza Namazi
Keyword(s):  
Brain Activity ◽  
Point Of View ◽  
Physiological Signals ◽  
Human Walking ◽  
Eeg Signals ◽  
Rehabilitation Science ◽  
Information Contents ◽  
First Time ◽  
The Brain ◽  
Brain Reaction

BACKGROUND: Walking like many other actions of a human is controlled by the brain through the nervous system. In fact, if a problem occurs in our brain, we cannot walk correctly. Therefore, the analysis of the coupling of brain activity and walking is very important especially in rehabilitation science. The complexity of movement paths is one of the factors that affect human walking. For instance, if we walk on a path that is more complex, our brain activity increases to adjust our movements. OBJECTIVE: This study for the first time analyzed the coupling of walking paths and brain reaction from the information point of view. METHODS: We analyzed the Shannon entropy for electroencephalography (EEG) signals versus the walking paths in order to relate their information contents. RESULTS: According to the results, walking on a path that contains more information causes more information in EEG signals. A strong correlation (p= 0.9999) was observed between the information contents of EEG signals and walking paths. Our method of analysis can also be used to investigate the relation among other physiological signals of a human and walking paths, which has great benefits in rehabilitation science.

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