scholarly journals β-Band Analysis from Simulated Flight Experiments

Aerospace ◽  
2021 ◽  
Vol 8 (5) ◽  
pp. 120
Author(s):  
Válber César Cavalcanti Roza ◽  
Octavian Postolache
Keyword(s):  
Brain Activity ◽  
Flight Simulation ◽  
Civil Aviation ◽  
Human Errors ◽  
Aviation Accidents ◽  
Final Approach ◽  
Electronic Games ◽  
Cruise Flight ◽  
The Brain ◽  
Different Levels

Several safety-related improvements are applied every year to try to minimize the total number of civil aviation accidents. Fortunately, these improvements work well, reducing the number of accident occurrences. However, while the number of accidents due to mechanical failures has decreased, the number of accidents due to human errors seems to grow. On that basis, this work presents a contribution regarding the brain’s β-band activities for different levels of volunteers’ expertise on flight simulator, i.e., experienced, mid-level and beginner, in which they acted as pilots in command during several simulated flights. Spectrogram analysis and statistical measurements of each volunteer’s brain’s β-band were carried out. These were based on seven flight tasks: takeoff, climb, cruise flight, descent, approach, final approach and landing. The results of the proposed experiment showed that the takeoff, approach and landing corresponded to the highest brain activities, i.e., close to 37.06–67.33% more than the brain activity of the other flight tasks: when some accidents were about to occur, the intensities of the brain activity were similar to those of the final approach task. When the volunteers’ expertise and confidence on flight simulation were considered, it was shown that the highest brain magnitudes and oscillations observed of more experienced and confident volunteers were on average close to 68.44% less, compared to less experienced and less confident volunteers. Moreover, more experienced and confident volunteers in general presented different patterns of brain activities compared to volunteers with less expertise or less familiarity with fight simulations and/or electronic games.

scholarly journals Making the brain-activity-to-information leap using a novel framework: Stimulus Information Representation (SIR)

2019 ◽  
Author(s):  
Philippe G. Schyns ◽  
Robin A.A. Ince
Keyword(s):  
Brain Imaging ◽  
Brain Activity ◽  
Behavioral Responses ◽  
Data Driven ◽  
Information Representation ◽  
Stimulus Information ◽  
Fundamental Challenge ◽  
Per Se ◽  
The Brain ◽  
Different Levels

AbstractA fundamental challenge in neuroscience is to understand how the brain processes information. Neuroscientists have approached this question partly by measuring brain activity in space, time and at different levels of granularity. However, our aim is not to discover brain activity per se, but to understand the processing of information that this activity reflects. To make this brain-activity-to-information leap, we believe that we should reconsider brain imaging from the methodological foundations of psychology. With this goal in mind, we have developed a new data-driven framework, called Stimulus Information Representation (SIR), that enables us to better understand how the brain processes information from measures of brain activity and behavioral responses. In this article, we explain this approach, its strengths and limitations, and how it can be applied to understand how the brain processes information to perform behavior in a task.“It is no good poking around in the brain without some idea of what one is looking for. That would be like trying to find a needle in a haystack without having any idea what needles look like. The theorist is the [person] who might reasonably be asked for [their] opinion about the appearance of needles.” HC Longuet-Higgins, 1969.

From Latent Failure to Active Failure: The Investigation of Human Errors in Aviation Operation

2007 ◽  
Vol 51 (20) ◽  
pp. 1425-1429
Author(s):  
Wen-Chin Li ◽  
Don Harris
Keyword(s):  
Human Factors ◽  
Human Error ◽  
Analytical Framework ◽  
Civil Aviation ◽  
Human Errors ◽  
Aviation Accidents ◽  
Republic Of China ◽  
Long Time ◽  
The Republic ◽  
Supervisory Practices

The Human Factors Analysis and Classification System (HFACS, Wiegmann & Shappell, 2003) was developed as an analytical framework for the investigation of the role of human factors in aviation accidents. HFACS is based upon Reason's model (1990) of human error in which active failures are associated with the performance of front -line operators in complex systems and latent failures are characterized as inadequacies which lie dormant within a system for a long time, and are only trigge red when combined with other factors to breach the system's defenses. In this research HFACS was used to analyze accidents occurring in civil aviation aircraft in the Republic of China (ROC). Forty-one accident reports from the Aviation Safety Council (A SC) were analyzed. Relationships in the HFACS framework were identified linking fallible decisions at higher (organizational) levels with supervisory practices, thereby creating the preconditions for unsafe acts and hence indirectly impairing the performance of pilots.

scholarly journals Modulatory Effects of Prediction Accuracy on Electroencephalographic Brain Activity During Prediction

2021 ◽  
Vol 15 ◽  
Author(s):  
Kentaro Ono ◽  
Junya Hashimoto ◽  
Ryosuke Hiramoto ◽  
Takafumi Sasaoka ◽  
Shigeto Yamawaki
Keyword(s):  
Cognitive Processes ◽  
Prediction Error ◽  
Coding Theory ◽  
Prediction Accuracy ◽  
Brain Activity ◽  
Subjective Evaluation ◽  
Predictive Coding ◽  
Upcoming Event ◽  
The Brain ◽  
Different Levels

Prediction is essential for the efficiency of many cognitive processes; however, this process is not always perfect. Predictive coding theory suggests that the brain generates and updates a prediction to respond to an upcoming event. Although an electrophysiological index of prediction, the stimulus preceding negativity (SPN), has been reported, it remains unknown whether the SPN reflects the prediction accuracy, or whether it is associated with the prediction error, which corresponds to a mismatch between a prediction and an actual input. Thus, the present study aimed to investigate this question using electroencephalography (EEG). Participants were asked to predict the original pictures from pictures that had undergone different levels of pixelation. The SPN amplitude was affected by the level of pixelation and correlated with the subjective evaluation of the prediction accuracy. Furthermore, late positive components (LPC) were negatively correlated with SPN. These results suggest that the amplitude of SPN reflects the prediction accuracy; more accurate prediction increases the SPN and reduces the prediction error, resulting in reduced LPC amplitudes.

Complexity-based decoding of the relation between human voice and brain activity

2020 ◽  
Vol 28 (6) ◽  
pp. 665-674 ◽  
Author(s):  
Mohamed Rasmi Ashfaq Ahamed ◽  
Mohammad Hossein Babini ◽  
Hamidreza Namazi
Keyword(s):  
Brain Activity ◽  
Fractal Theory ◽  
Eeg Signal ◽  
Human Communication ◽  
Eeg Signals ◽  
Human Voice ◽  
Voice Signal ◽  
Molecular Complexity ◽  
The Brain ◽  
Different Levels

BACKGROUND: The human voice is the main feature of human communication. It is known that the brain controls the human voice. Therefore, there should be a relation between the characteristics of voice and brain activity. OBJECTIVE: In this research, electroencephalography (EEG) as the feature of brain activity and voice signals were simultaneously analyzed. METHOD: For this purpose, we changed the activity of the human brain by applying different odours and simultaneously recorded their voices and EEG signals while they read a text. For the analysis, we used the fractal theory that deals with the complexity of objects. The fractal dimension of EEG signal versus voice signal in different levels of brain activity were computed and analyzed. RESULTS: The results indicate that the activity of human voice is related to brain activity, where the variations of the complexity of EEG signal are linked to the variations of the complexity of voice signal. In addition, the EEG and voice signal complexities are related to the molecular complexity of applied odours. CONCLUSION: The employed method of analysis in this research can be widely applied to other physiological signals in order to relate the activities of different organs of human such as the heart to the activity of his brain.

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.

Relation Between Level of Prefrontal Activity and Subject's Performance

1999 ◽  
Vol 13 (2) ◽  
pp. 117-125 ◽  
Author(s):  
Laurence Casini ◽  
Françoise Macar ◽  
Marie-Hélène Giard
Keyword(s):  
Brain Activity ◽  
Sensory Information ◽  
Practice Phase ◽  
Trained Subjects ◽  
Anagram Task ◽  
Economic Information ◽  
Long Duration ◽  
Level Of Activity ◽  
The Brain ◽  
Processing Mechanism

Abstract The experiment reported here was aimed at determining whether the level of brain activity can be related to performance in trained subjects. Two tasks were compared: a temporal and a linguistic task. An array of four letters appeared on a screen. In the temporal task, subjects had to decide whether the letters remained on the screen for a short or a long duration as learned in a practice phase. In the linguistic task, they had to determine whether the four letters could form a word or not (anagram task). These tasks allowed us to compare the level of brain activity obtained in correct and incorrect responses. The current density measures recorded over prefrontal areas showed a relationship between the performance and the level of activity in the temporal task only. The level of activity obtained with correct responses was lower than that obtained with incorrect responses. This suggests that a good temporal performance could be the result of an efficacious, but economic, information-processing mechanism in the brain. In addition, the absence of this relation in the anagram task results in the question of whether this relation is specific to the processing of sensory information only.

Memory Processes, Aging, Cognitive Decline, and Neurodegenerative Diseases

2006 ◽  
Vol 11 (4) ◽  
pp. 304-311 ◽  
Author(s):  
Lars-Göran Nilsson
Keyword(s):  
Cognitive Impairment ◽  
Cognitive Decline ◽  
Neurodegenerative Disease ◽  
Genetic Markers ◽  
Brain Activity ◽  
Memory Performance ◽  
Modeling And Simulations ◽  
Aging And Health ◽  
Explained Variance ◽  
Different Levels

This paper presents four domains of markers that have been found to predict later cognitive impairment and neurodegenerative disease. These four domains are (1) data patterns of memory performance, (2) cardiovascular factors, (3) genetic markers, and (4) brain activity. The critical features of each domain are illustrated with data from the longitudinal Betula Study on memory, aging, and health ( Nilsson et al., 1997 ; Nilsson et al., 2004 ). Up to now, early signs regarding these domains have been examined one by one and it has been found that they are associated with later cognitive impairment and neurodegenerative disease. However, it was also found that each marker accounts for only a very small part of the total variance, implying that single markers should not be used as predictors for cognitive decline or neurodegenerative disease. It is discussed whether modeling and simulations should be used as tools to combine markers at different levels to increase the amount of explained variance.

Automated System for Epileptic Seizures Prediction based on Multi-Channel Recordings of Electrical Brain Activity

2018 ◽  
pp. 115-122 ◽  
Author(s):  
V. A. Maksimenko ◽  
A. A. Harchenko ◽  
A. Lüttjohann
Keyword(s):  
Epileptic Seizure ◽  
Brain Activity ◽  
Epileptic Seizures ◽  
Automated System ◽  
Brain Computer Interfaces ◽  
Electrical Brain Activity ◽  
Computer Interfaces ◽  
Prediction And Prevention ◽  
The Brain

Introduction: Now the great interest in studying the brain activity based on detection of oscillatory patterns on the recorded data of electrical neuronal activity (electroencephalograms) is associated with the possibility of developing brain-computer interfaces. Braincomputer interfaces are based on the real-time detection of characteristic patterns on electroencephalograms and their transformation  into commands for controlling external devices. One of the important areas of the brain-computer interfaces application is the control of the pathological activity of the brain. This is in demand for epilepsy patients, who do not respond to drug treatment.Purpose: A technique for detecting the characteristic patterns of neural activity preceding the occurrence of epileptic seizures.Results:Using multi-channel electroencephalograms, we consider the dynamics of thalamo-cortical brain network, preceded the occurrence of an epileptic seizure. We have developed technique which allows to predict the occurrence of an epileptic seizure. The technique has been implemented in a brain-computer interface, which has been tested in-vivo on the animal model of absence epilepsy.Practical relevance:The results of our study demonstrate the possibility of epileptic seizures prediction based on multichannel electroencephalograms. The obtained results can be used in the development of neurointerfaces for the prediction and prevention of seizures of various types of epilepsy in humans. 

scholarly journals Conditional Entropy: A Potential Digital Marker for Stress

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 286
Author(s):  
Soheil Keshmiri
Keyword(s):  
Resting State ◽  
Nearest Neighbor ◽  
Brain Activity ◽  
Solution Concept ◽  
Conditional Entropy ◽  
K Nearest Neighbor ◽  
Brain Response ◽  
Substantial Progress ◽  
Eeg Recordings ◽  
The Brain

Recent decades have witnessed a substantial progress in the utilization of brain activity for the identification of stress digital markers. In particular, the success of entropic measures for this purpose is very appealing, considering (1) their suitability for capturing both linear and non-linear characteristics of brain activity recordings and (2) their direct association with the brain signal variability. These findings rely on external stimuli to induce the brain stress response. On the other hand, research suggests that the use of different types of experimentally induced psychological and physical stressors could potentially yield differential impacts on the brain response to stress and therefore should be dissociated from more general patterns. The present study takes a step toward addressing this issue by introducing conditional entropy (CE) as a potential electroencephalography (EEG)-based resting-state digital marker of stress. For this purpose, we use the resting-state multi-channel EEG recordings of 20 individuals whose responses to stress-related questionnaires show significantly higher and lower level of stress. Through the application of representational similarity analysis (RSA) and K-nearest-neighbor (KNN) classification, we verify the potential that the use of CE can offer to the solution concept of finding an effective digital marker for stress.

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