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Understanding the secrets underlying the brain functioning would be the noble achievement of this era. Learning how brain learns would be the milestone to guide the researchers of artificial intelligence, neurology and psychology. With the advent of “Integrate and Fire” model of neuron proposed about a hundred years ago, the brain research has pick...
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... wish to undertake black box modeling approach to observe the behaviour of the system and the external influence (input to the system) without going into the deep details of the internal processes within the system [33]. Figure 2 shows the schematic representation of the general system identification approach. Here, EEG is represented in the system as a sensor box, which will respond to cognitive perturbations to produce the observable signals. ...
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Citations
... Different sorts of measures have contributed to deciding the driver's Cognitive state [74][75][76]. Employments of EEG signals for weakness identification have been broadly examined [21,22], while [77] have talked about the usage of the outstanding task at hand list towards evaluating the driver's Cognitive state. A few models for recognizing diverted drivers have been inspected in [78]. ...
Cognitive informatics is a transdisciplinary inquiry of data sciences, intellectual science, and knowledge science that explores the inward data preparing systems. It also explores the procedures of the brain and normal insight, just as their building applications in intellectual registering. Cognitive processing is a developing worldview of clever figuring strategies and frameworks dependent on intellectual informatics that executes computational insight through self-sufficient inductions. This article presents a lot of aggregate points of view on Cognitive informatics and intellectual figuring, just as their applications in unique insight, computational insight, computational phonetics, information portrayal, cooperative processing, granular registering, semantic registering, AI, and social processing. In this assessment paper, an endeavor has been made towards decoding an intellectual movement (conscious eye squint) of human subjects caught utilizing electroencephalography (EEG) energetically. A noteworthy ascent in occasion associated prospective remains seen through frontal flaps of the cerebral cortex. The created ideal has remained conveyed in Arduino utilizing Simulink towards control yield gadgets freely. The outcomes exhibit the achievability of the Cognitive mechanism system towards wards interpreting purposeful goals into orders through EEG centered BCI for the rest towards the ration of truly tested patients.
... Driver drowsiness affects the mental capabilities of drivers (Dahal et al., 2011). These effects can be observed in the driver's brain waves. ...
Driver drowsiness leads to fatal road traffic accidents. The effects of drowsy driving on electroencephalogram (EEG) signal are well visible. Accordingly, classifying EEG signal with machine learning (ML) is known as a reliable and accurate drowsiness detection technique. The type of ML algorithm, data preprocessing, and tuning the hyperparameters influence classification results greatly. In this paper, the drowsy EEG signal of 20 participants was measured in simulator tests. Six ML algorithms of decision tree, extra trees, K-nearest neighbors, multi-layer perceptron, random forest, and support vector classification were trained and cross-validated with different approaches to understand how data preprocessing and tuning the ML hyperparameters can improve drowsy EEG signal modeling. Results indicated that the type of ML algorithm has a more notable effect on modeling accuracy and modeling error than the modeling approach. Data preprocessing generally improved modeling results. But tuning the hyperparameters with the random search method wasn't helpful. Comparison of algorithms showed that tree-based ensemble algorithms (extra trees and random forest) were the most accurate models. They are more practical for real-time applications of drowsy EEG signal modeling.
... By monitoring the cognitive state of drivers, BCIs have proved to be a revolutionary step in the arena of smart and intelligent transportation. Owing to the inattentiveness of drivers (due to distraction or fatigue) being the cause of most traffic accidents [90], various approaches have been proposed to determine the cognitive state of drivers [91][92][93]. Fatigue detection [94,95], cognitive state assessment via workload index [96], concentration and stress evaluation using multimodal context recognition [97], and evaluation of drunk drivers [98] have been effectively explored. For the assessment of driving responses, an audio-visual environment has also been established [99]. ...
This article provides a comprehensive review of the recent trends and applications of BCIs. This review also provides future directions towards the acceleration and maturation of BCI technology. Based on a methodical search strategy, major technical databases were searched in quest of research papers of average and outstanding interest. A total of 188 research works were included within this review due to their suitability and state-of-the-art achievements. This review identifies various eminent applications of BCIs in medical and non-medical domains. The findings of this review reveal the need for further exploration of BCI devices outside the laboratory-based settings for their development and seamless integration. In addition, applications of BCIs, including neuromarketing, neurorehabilitation, and neuroergonomics, require additional investigations for further validation and fruition of BCI technology. Based on this review, it is concluded that BCIs are in their embryonic stage and seek further research and investigation for their maturation.
... They can analyze the impact of workload mental fatigue as well as the task time on EEG features 38 . Different measures have helped for finding the driver's cognitive state [39][40][41] . Alcoholic drivers could be characterized using EEG signals of brain [42][43][44] . ...
... It has been found that distraction and fatigue are two main sources for driver's inattention, which is considered as a strong cause for most traffic accidents [63]. Various types of measures have contributed in determining the driver's cognitive state [64][65][66]. Uses of EEG signals for fatigue detection have been widely studied in [67], while [68] has discussed the utilization of workload index to assess the driver's mental state. Several models for distinguishing distracted drivers have been examined in [69]. ...
Brain computer interface technology represents a highly growing field of research with application systems. Its contributions in medical fields range from prevention to neuronal rehabilitation for serious injuries. Mind reading and remote communication have their unique fingerprint in numerous fields such as educational, self-regulation, production, marketing, security as well as games and entertainment. It creates a mutual understanding between users and the surrounding systems. This paper shows the application areas that could benefit from brain waves in facilitating or achieving their goals. We also discuss major usability and technical challenges that face brain signals utilization in various components of BCI system. Different solutions that aim to limit and decrease their effects have also been reviewed.
... Finally, as the pole analysis of EEG involved the averaging of all the selected epochs of the participant, the data from twelve participants was not sufficient to perform classification of patterns during normal and distracted driving. In future work, we intend to take more participants and classify the normal driving and audio distracted EEG epochs and use the pole displacement features of ERD/ERS phenomenon in different brain sites in the modeling of various other cognitive functions and processes [55]. ...
Objective:
The objective of our current study was to look for the EEG correlates that can reveal the engaged state of the brain while undertaking cognitive tasks. Specifically, we aimed to identify EEG features that could detect audio distraction during simulated driving.
Approach:
Time varying autoregressive (TVAR) analysis using Kalman smoother was carried out on short time epochs of EEG data collected from participants as they undertook two simulated driving tasks. TVAR coefficients were then used to construct all pole model enabling the identification of EEG features that could differentiate normal driving from audio distracted driving.
Main results:
Pole analysis of the TVAR model led to the visualization of event related synchronization/desynchronization (ERS/ERD) patterns in the form of pole displacements in pole plots of the temporal EEG channels in the z plane enabling the differentiation of the two driving conditions. ERS in the EEG data has been demonstrated during audio distraction as an associated phenomenon.
Significance:
Visualizing the ERD/ERS phenomenon in terms of pole displacement is a novel approach. Although ERS/ERD has previously been demonstrated as reliable when applied to motor related tasks, it is believed to be the first time that it has been applied to investigate human cognitive phenomena such as attention and distraction. Results confirmed that distracted/non-distracted driving states can be identified using this approach supporting its applicability to cognition research.
Human cognition is the essential building block of human intelligence, and it is what makes us who we are. Cognition is defined as the capacity to recognize and respond appropriately to external stimuli based on one’s beliefs, actions, experiences, and senses. It is one of the fundamental reasons for human existence and is one of the most important aspects of the brain. In childhood, adolescence, and maturity, the cognitive processes of humans are always evolving and developing. Although some of these abilities begin to diminish as one grows older and approaches older maturity, others begin to deteriorate when neurons die and the systems that replace them become insufficient. Understanding cognition is essential not just for healthy cognitive growth and survival but also for the treatment of a variety of neuropsychological conditions, such as Alzheimer’s disease. It is necessary to examine the cognitive functions of the human brain before one can comprehend cognition. fNIRS and electroencephalography (EEG) are low-cost methods of assessing and evaluating cognitive function. The principles of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), as well as a number of methods for preprocessing and interpreting EEG and fNIRS data, are therefore covered in this chapter. Lastly, the use of simultaneous EEG-fNIRS is discussed along with its limitations and advantages.
A coordination game is one in which two players are rewarded for making the same choice from the same set of alternatives. The ability of humans to tacitly coordinate effectively is based on the identification of pronounced solutions associated with salient features attracting the player’s attention. These prominent solutions are referred to as focal points. Game theory fails to account for how people make decisions in tacit coordination games, and human behavior in these games cannot be explained by a single theory. One of the accepted theories for explaining human behavior is level-k theory. This theory assumes that each player has a different level of reasoning by which she assesses the behavior of other players in the game and makes strategic decisions based on that assessment. In Previous studies, we have found an association between the players’ cognitive load as reflected by EEG power and the level-k during the coordination game. The goal of the current study was to examine the relationship between alpha frequency and its sub-bands and level-k during a tacit coordination game in the context of semantic processing.KeywordsEEGTacit coordination gamesFocal pointsAlpha band
The purpose of this study is to determine the brainwave or electroencephalogram (EEG) distribution of Normal and Dyslexia children during cognitive activity. EEG is a method for monitoring electrical activity in the brain and has four main signals in the brain: Delta Wave, Theta Wave, Alpha Wave and Beta Wave. Delta wave is a slow wave generated in the deepest meditation, Theta Wave usually occurs in sleep, Alpha Wave is dominant in calming, relaxing condition, and Beta Wave is dominant in waking condition. The raw data collected are analysed using SPSS and Microsoft Excel to analyse the accuracy and pattern of brain waves between normal children and children with dyslexia. For normal versus dyslexia during cognitive activity, the average and correlation are used. Subjects are asked to remember words in a minute. There were 20 students who were tested, 10 of them normal and 10 of them had dyslexia. All subjects are from special needs school under PPD Batu Pahat. Data is focused on Alpha and Beta wave when people are awake. Brain waves tend to vary through cognitive function. The result indicates a more powerful Theta signal compared to other signals for normal kids. Then Beta signal more dominant than other signals in cognitive function for children with dyslexia.KeywordsElectroencephalogram (EEG)Alpha WaveBeta WaveCognitiveNormalDyslexia
