What Techniques Are Used To View The Frontal Lobe

Neuroimaging Technique

Tensors for neuroimaging

Aybüke Erol , Borbála Hunyadi , in Tensors for Data Processing, 2022

Abstract

Neuroimaging techniques are used to image the structure and function of the nervous system for medicine, psychology, and neuroscience research. Brain information are inherently multidimensional and complex, and the recent advances in neuroimaging allow the acquisition of brain signals at an increasing spatiotemporal resolution. Being able to process the resulting large-scale data and capturing the multiway structure of the brain, tensor-based analyses are well suited for a variety of neuroimaging applications. In this review, we provide a comprehensive overview of successful tensor-based solutions used in the field of neuroimaging discuss practical challenges and the future of tensors in medical technology.

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Toward improving the accuracy in the diagnosis of schizophrenia using functional magnetic resonance imaging (fMRI)

M. Kaviya Elakkiya , Dejey , in Cerebral Systems and Signal Processing in Epitome Processing, 2022

3.ane.ii fMRI and conquering of fMRI

fMRI, a functional neuroimaging technique, has become a predominant tool for clinical research to furnish information nearly psychiatric disorders, such as schizophrenia, major depressive disorder, bipolar disorder, obsessive-compulsive disorder, posttraumatic stress disorder, and Alzheimer's illness. fMRI measures neural activation through changes in oxidation and blood flow noninvasively. Blood oxygenation level-dependent (Bold) and arterial spin labeling are the most commonly used fMRI types in psychiatric neuroimaging. BOLD fMRI is preferred due to greater sensitivity and loftier temporal resolution [14].

R-fMRI is a supplementary echo planar imaging BOLD (EPI BOLD) sequence that needs Brain Wave software, available only with viii-channel coil. It is a x-minute long sequence. Information technology contains 200 repetitions of a high resolution EPI scan. All the subjects are instructed to close their eyes and residue during fMRI acquisition [4, 13].

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Sparse models for imaging genetics

J. Wang , ... J. Ye , in Machine Learning and Medical Imaging, 2016

5.i Introduction

Imaging genetics studies neuroimaging-related genetic variation. In the past decade, neuroimaging techniques—for instance, computed tomography (CT), magnetic resonance imaging (MRI), functional MRI (fMRI), and positron emission tomography (PET)—provide both anatomical and functional visualizations of the nervous system, which profoundly accelerate modern medicine, neuroscience, and psychology. As an emerging promising technique, imaging genetics research has attracted extensive attention. With the integration of molecular genetics and disorder-related neuroimaging phenotypes, imaging genetics provides a unique opportunity to reveal the impact of genetic variation in neuroimaging, that is, how individual differences in single nucleotide polymorphisms (SNPs) bear upon brain development, construction, and role (Hariri et al., 2006; Thompson et al., 2013). Molecular geneticists believe that some common genetic variants in SNPs may lead to common disorders (Cirulli and Goldstein, 2010). Moreover, as another do good of exploiting neuroimaging in genetics, imaging phenotypes are closer to the biology of genetic function (Meyer-Lindenberg, 2012) than disease or cerebral phenotypes.

Previous studies evidence the great hope of imaging genetics. For instance, the ϵiv allele of apolipoprotein E (ApoE4) is i of the well-known genetic risk factors for Alzheimer'due south affliction (AD). From a neuroimaging perspective, the degeneration of encephalon tissue of ApoE4 carriers is faster as they age; immature adult ApoE4 carriers often exhibit thinner cortical gray matter than noncarriers (Shaw et al., 2007). It has been verified in a series of genome-wide association (GWA) studies of Advertizing that ApoE4 is strongly associated with the volumes of key brain regions, such as the hippocampus and entorhinal cortex (Potkin et al., 2009; Stein et al., 2012; Yang et al., 2015). Recent worldwide consortium efforts, such as ENIGMA (Enhancing Neuroimaging Genetics through Meta-Analysis (Stein et al., 2012)) and CHARGE (Cohorts for Center and Aging Research in Genomic Epidemiology (Bis et al., 2012; Psaty et al., 2009)), enable u.s.a. to notice robust common neuroimaging-genetic associations (Medland et al., 2014).

Imaging genetic studies are challenging in do due to the relatively small number of subjects and extremely high dimensionality of imaging as well as genetic information. Neuroimaging information, for instance, contains hundreds of thousands of voxels. Advances in modern sequencing techniques lead to huge scale (whole) genome sequencing information with tens of millions of SNPs. However, most traditional statistical methods are intended for low-dimensional data sets (James et al., 2013), in which the number of subjects is much larger than the number of features. This significantly limits the practical usage of traditional methods to the high-dimensional imaging data sets, every bit they are prone to overfitting.

The loftier-dimensional information sets involved in many imaging genetics studies face up researchers and scientists with an urgent need for novel methods that tin can effectively uncover the predictive patterns from these types of data. A useful ascertainment from many real-world applications is that data with complex structures oft has sparse underlying representations. More specifically, although the data may accept millions of features, it may be well interpreted by a few of the nigh relevant explanatory features. For instance, the neural representation of natural scenes in the visual cortex is thin, every bit only a modest number of neurons are active at a given instant (Vinje and Gallant, 2000); images have very sparse representations with respect to an overcomplete dictionary because they lie on or close to low-dimensional subspaces or submanifolds (Wright et al., 2010); although humans have millions of SNPs, only a pocket-sized number of them are relevant to sure diseases such as leukemia and Alzheimer's illness (Golub et al., 1999; Guyon et al., 2002; Mu and Gage, 2011). Moreover, sparsity has been shown to be an constructive approach to alleviate overfitting, from which nigh traditional statistical methods suffer. Therefore finding sparse representations is particularly important in discovering the underlying mechanisms of many circuitous systems.

As an emerging and powerful technique, thin models have attracted increasing research interest in paradigm genetics in the past decade. As well as their robustness to overfitting, sparse models are besides promising in enhancing the interpretability of the model by automatically identifying a small subset of features that tin best explicate the upshot. Indeed, we can categorize existing methodological approaches for imaging genetics into three classes (Thompson et al., 2013).

The first ane is the so-called univariate-imaging univariate-genetic association analysis that performs a univariate statistical exam on each SNP-voxel pair individually. This type of arroyo has been widely used in previous GWA studies. However, these approaches fail to reveal scenarios such as SNP-SNP interactions and the joint effects of multiple SNPs, which occur commonly in factor expression (Dinu et al., 2012; Cornelis et al., 2009; Singh et al., 2011; Yang et al., 2012). In add-on, it is worth mentioning that this kind of analysis is computationally inefficient.

The second class is the univariate-imaging multivariate-genetic clan method. Based on a candidate imaging phenotype, a common multivariate approach utilizes sparse models, for example, Lasso (to the lowest degree accented shrinkage and selection operator (Tibshirani, 1996; Yang et al., 2015)), to perform simultaneous model plumbing equipment and variable (causal SNPs) selection. Moreover, by incorporating biological prior noesis such as linkage disequilibrium (LD) information, we can employ group Lasso to locate groups of candidate SNPs (Wang et al., 2012; Yuan and Lin, 2006). In the sequel, tree-structured group Lasso tin also be applied if the hierarchical structure of SNPs is farther available (Liu and Ye, 2010).

The third class of methodology in imaging genetics is joint multivariate clan assay, for example, canonical correlation analysis (CCA) and partial least squares (PLS) regression. However, a clear drawback of this kind of approach is that the detected genetic variants and imaging features may not be immediately related to a disorder (Batmanghelich et al., 2013).

In this affiliate, we focus on univariate-imaging multivariate-genetic clan studies in imaging genetics. We commencement introduce 2 uncomplicated sparse models, that is, Lasso and thin logistic regression, in Section v.ii. Then, in Section 5.3, we introduce a series of popular structured thin methods, which incorporate some prior knowledge. We will also review some popular optimization algorithms in Section 5.iv. In Section 5.5, we pay particular attention to a suite of novel techniques, that is, screening rules, for sparse models (Hastie et al., 2015; Wang et al., 2015b), which can better the computational efficiency by several orders of magnitude.

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Autism, Neural Basis of

R.T. Schultz , in International Encyclopedia of the Social & Behavioral Sciences, 2001

3.4 Frontal Lobe Interest

Aspects of frontal lobe integrity and role have been implicated in the pathogenesis of autism. Older studies using lower resolution neuroimaging techniques reported general hypoactivation of the frontal lobes. Functional neuroimaging information collected in the last decade are converging to bear witness that subregions of the prefrontal cortices with especially strong connectivity to limbic areas are critical for 'social knowledge,' that is, thinking about other's thoughts, feelings and intentions. Deficits in such 'theory of heed' abilities are common in autism (see Autistic Disorder: Psychological ). Theory of mind ability has been linked to functional activeness in the medial aspect of the superior frontal gyrus (primarily Brodmann surface area nine) and to prefrontal cortex immediately above the orbits of the eyes (i.e., orbital frontal cortex). The orbital and medial prefrontal cortices have dense reciprocal connections with the amygdala providing the compages for a arrangement that could regulate social-emotional processes. Bilateral lesions to orbital and medial prefrontal cortex lesions cause deficits on theory of mind tasks. Moreover, nonhuman primate studies have documented abnormal social responsivity and loss of social position within the group post-obit lesions to orbital and medial prefrontal cortex.

Preliminary functional imaging show in autism spectrum conditions suggests altered functional representation in prefrontal cortex regions during theory of mind tasks. Moreover, medial prefrontal dopaminergic activity as measured by flourine-eighteen-labeled fluorodopa PET has been institute to be significantly reduced in autism. Reduced glucose metabolism during memory activities has too been reported in a subdivision of the anterior cingulate gyrus, a region that lies along the medial surface of the frontal lobe.

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Cognitive Neuroscience

Craig Weiss , John F. Disterhoft , in Encyclopedia of Social Measurement, 2005

Imaging Cognition

A "fishing trip" through the brain in search of knowledge could be avoided if some sort of "radar" was available to narrow downward the target area. This so-called radar is now available in the form of unlike neuroimaging techniques. The technique that shortly has the greatest spatial and temporal resolution is functional magnetic resonance imaging (fMRI), which relies on differences in the magnetic susceptibility of oxygenated and deoxygenated claret. This claret oxygen level-dependent (Assuming) response tin can be followed in fourth dimension while a subject learns or performs a task. The response tin can then be superimposed on a detailed image of the brain so that functional activity (increases or decreases in the BOLD response) tin be localized. The technique is almost often used with man written report participants considering they can go along their caput very still, follow directions, give exact feedback, and indicate responses with some sort of detector (e.one thousand., a keystroke). Some experiments accept been done recently with monkeys, but they are rather precious and require lots of training, some sedation, and extreme care to maintain in adept health.

A simple brute model for neuroimaging of cognition is over again based on the rabbit. Alice Wyrwicz, John Disterhoft, Craig Weiss, and colleagues took reward of the rabbit's natural tolerance for restraint and adjusted the eyeblink conditioning prototype for the MRI environs. This paradigm allows the detection of functionally active regions throughout about of the encephalon while the rabbit is awake, drug free, and learning a new task. The results so far take confirmed the interest of the hippocampus and cerebellum in unproblematic delay workout, and take revealed specific regions that should be explored more carefully with electrophysiological techniques to empathise fully the neural mechanisms that mediate cognitive processes. An example of activation in the visual cortex and hippocampus can be seen in Fig. 5.

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Face Recognition: Psychological and Neural Aspects

A. Puce , in International Encyclopedia of the Social & Behavioral Sciences, 2001

2 Neuroimaging and Neurophysiological Studies

In the latter function of the twentieth century many human being physiological studies were dedicated to investigating the neural mechanisms underlying facial recognition (recently reviewed by Haxby et al. 2000 ). This was prompted, in role, past the evolution of neuroimaging techniques such as positron emission tomography (PET) and, more recently, functional magnetic resonance imaging (fMRI). Both methods effectively measure focal changes in brain blood flow during perception and cognition. I of the first investigations of face perception and recognition was performed by Justine Sergent working at the Montreal Neurological Institute in 1992 (Sergent et al. 1992). Sergent and her colleagues performed a PET report examining differences in cerebral claret menstruum when normal developed subjects viewed pictures of faces and discriminated between various facial attributes. For example, subjects made gender discriminations (deciding whether a confront was male or female), remembering if a particular face up had been shown to the subject previously, so on. The blood flow patterns seen in these conditions were assorted relative to weather where subjects viewed visual material such as gratings (grids of black and white lines). These studies identified regions of the occipital and temporal lobe on the underside of the brain as being selectively active when subjects viewed and discriminated between faces. Since and then many investigators accept followed suit and studied other aspects of facial processing (reviewed by Haxby et al. 2000), and the studies show concordance with this initial investigation. Additionally, it is now thought that while 'face-selective' regions in both hemispheres possess the capability to procedure faces, information technology is the correct hemisphere that is more of import for this process. In prosopagnosia, for case, if the lesion occurs on ane side of the encephalon information technology is unremarkably on the right side (De Renzi et al. 1994).

Claret menstruation studies show what is active in the brain; however, these methods cannot examine these changes over a fine time window. Recording the electric activeness of the brain (EEG) tin resolve when this activity occurs to thousandths of a 2d (millisecond). If the EEG is recorded from the scalp it may be difficult to identify where these active structures are in the encephalon. Ane potential way effectually this problem is to perform recordings of the electric activeness directly from the surface of the encephalon. This occurs in the routine assessment of patients who are existence considered for epilepsy surgery. This method has allowed the 'what' and 'when' of the face recognition process to be mapped accurately in both infinite and time. Face-selective regions of brain on the underside (Fig. 2(a)), and side of the brain (Fig. 2(b)) accept been mapped using this method. Face-specific areas in these studies overlap those seen in neuroimaging studies in good for you subjects, and the sites of injury in prosopagnosia. After a face is presented, the brain generates a big wave (N200) at around 200 milliseconds, which is negative in voltage and is around 2×10⁻⁴ of a volt, or 200 microvolts, in size (Fig. 2(c)). The N200 result-related potential (ERP) occurs irrespective of whether the observer attempts to recognize the face or non, and does non depend on the lighting conditions, size, orientation of the face, gender, or familiarity of the confront (Puce et al. 1999).

The robustness of the N200 in the large number of perceptual manipulations and the seemingly automatic mode in which the response is generated suggests that this may exist a neural correlate of the structural encoder of Bruce and Young's (1986) model. These information are consequent with behavioral studies of confront perception, where healthy subjects tin can readily detect faces relative to other object categories, despite stimulus degradation, fragmentation, rotation, inversion, manipulations of light and shade, and so on (Bruce and Young 1998).

Under these same perceptual manipulations, facial recognition tin be dumb. Individuating 1 person'south face up from some other requires that the features that are unique to that detail (familiar) individual are extracted and matched to a pre-existing 'template.' Manipulations that impair our ability to excerpt subtle spatial differences will affect successful facial recognition. For example, inverted familiar faces are difficult to recognize (compare Fig. iii(a) with Fig. 3(b)). Similarly, a negative epitome may brand the face up unrecognizable (Fig. three(c)). We are forced to rely on idiosyncratic, incidental details like the cigar and moustache so that we can infer that we are looking at Groucho Marx's face in Fig. 3(c). Similarly, manipulations of spatial frequency content or amount of detail of the confront tin can also impair facial recognition (Fig. three(d), (e)).

The ability to discriminate between individual faces is based on detecting changes in subtle spatial configurations in a homogeneous object category, different any other object category dealt with on a daily basis. Our-specialized facial recognition skills are and so honed that behavioral studies accept repeatedly demonstrated an ain-race advantage for facial recognition across different indigenous groups, i.e., Caucasian, Asian (Brigham 1986). These data suggest that in that location actually might exist a ground for the often-heard comment from travelers that the faces of people of other races look alike. Different indigenous groups have idiosyncrasies in their facial features that an individual member of that particular group learns to differentiate. The expertise that develops with the individuals own indigenous group may hence not necessarily be generalizable to another ethnic group.

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Electroencephalogram-based cerebral performance evaluation for mental arithmetic task

Debatri Chatterjee , ... Sanjoy Kumar Saha , in Cognitive Computing for Human-Robot Interaction, 2021

Previous works

Written report of brain activations associated with various cerebral processes are gaining lot of involvement both for clinical also as nonclinical applications. Cognitive neuroscience is the field which analyzes brain functions underlying various cognitive processes. In social club to practise so, researchers use various neuroimaging techniques like functional magnetic resonance imaging ( Poldrack, 2008), positron emission tomography (Frith et al., 1992), magnetoencephalography (Bialystok et al., 2005), etc. These techniques are mostly expensive and require expert intervention. EEG is another technique which is used for recording electrical activities of brain. This technique is widely available, cheap and has excellent temporal resolution. Moreover, recent invention of low resolution commercial form EEG devices has resulted in increased usage of this engineering for non-clinical applications and research.

Researchers take used EEG signals for studying the relationships betwixt various cognitive phenomena and associated activeness of brain (Sarter, Berntson, & Cacioppo, 1996). Cognition basically results from dynamic interaction of various brain areas operating as networks (Bressler & Menon, 2010). Prior works suggest that different EEG subbands reflects different activities. For instance, alpha band reflects attentional demands whereas beta band reflects emotional state and cognitive processes (Ray & Harry, 1985). Researchers have investigated the human relationship between power in solving problems and brain activations recorded with 28-lead EEG device (Jaušovec, 2000). They have used alpha power and asymmetry in alpha band as the features. In some works, authors have reported increased beta ring activity for increased attention bridge and alertness (Engel & Fries, 2010; Kamiński et al., 2012). It has too been reported that hippocampal theta activity is associated with cognitive performance (Kahana, Seelig, & Madsen, 2001). In a nutshell, all EEG sub-bands together control the man knowledge and cognitive abilities. Apart from these band powers, other approaches similar detrended fluctuation analysis (Márton et al., 2014), wavelet transform (Murata, 2005), principal component assay (Chaouachi, ImèneJraidi, & Frasson, 2011), etc. accept also been explored for studying the cognitive performances and abilities. An increased prefrontal encephalon activity for a controlling job has too been observed (Fleming et al., 2012). Another (EEG) based written report (Boldt & Yeung, 2015) shows a well characterized neural correlates of error sensation which is indicative of determination confidence. Most of these studies have been performed using loftier-resolution EEG devices and hence different brain regions were tapped for cess. However, researches are being conducted using low resolution EEG devices also. In Aniruddha et al. (2015) and Sinha et al. (2016), authors used single lead EEG devices for recording brain activations for assessment of cognitive flow country and performance evaluation. Wong, Chan, and Mak (2014) analyzed activities recorded using single aqueduct EEG device from frontal brain region for motor conquering task. In Papakostas et al. (2017), researchers used another low-cost EEG device from Muse for predicting the sequential learning job performance, before the user completes the chore.

Few researchers have come up up with some novel metrics which they have used for quantification of a cerebral performance and mental workload. Berka, Levendowski, and Cvetinovic (2004) take defined a workload index for measuring mental workload for diverse tasks similar arithmetic calculation, retentiveness task, etc. Later they have applied aforementioned index for analyzing educatee operation in a problem-solving job (Stevens R., Galloway, & Berka, 2007; Stevens R.H., Galloway, & Berka, 2007). Multiple metrics like, EEG-lark, EEG-workload and EEG-engagement accept been defined for analyzing student performance (Stevens, Galloway, & Berka, 2006). Feature like, EEG band-power based engagement index for estimating mental workload and task engagement for mathematical problem solving are also taken upwards (Galán & Beal, 2012). In Stikic et al. (2011), authors used the metric proposed by Berka et al. (2004) in association with reaction time and accuracy and derived a measure for predicting present and hereafter chore functioning for various cognitive tasks. Recently, researchers are also using neural network-based approaches for cess and classification of mental states of an individual during execution of a cognitive task (Baldwin & Penaranda, 2012). Thus, there are multiple approaches and several EEG features which can be used for assessing cognitive abilities and functioning.

These findings motivated us to study brain activations for assessing cognitive performance associated with a task. In the present study, we take used a dataset for studying the brain activations during arithmetic task. Instead of directly using traditional EEG domain-based feature, we have summarized the distribution of those feature values computed over several time-windows. This summarization helps to capture the overall trend of the characteristic. The proposed descriptor is a generic 1 and can be applied to other physiological sensor data (like center rate, galvanic peel conductance, respiratory signal, etc.) also.

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Biological Underpinnings of Anatomic Consistency and Variability in the Homo Brain

N. Tzourio Mazoyer , ... B Mazoyer , in Handbook of Medical Imaging, 2000

ane Introduction

One of the major goals of modernistic human neuroscience research is to establish the relationships betwixt brain structures and functions. Although such a goal was considered unrealistic a decade ago, it now seems attainable, thanks to the advent of anatomical and functional three-dimensional (3D) neuroimaging techniques. There remain, however, a number of key questions that volition demand to be resolved earlier a complete man brain map tin be realized.

Offset of all, brain structures and functions are characterized by considerable between-subject area variability, which motivates the topic of spatial registration and normalization of brain images taken from different individuals. Secondly, the man encephalon exhibits several levels of structural and functional organization, both in time and space, ranging from synapses to large-scale distributed networks. Integrating these various levels is both a hard theoretical neuroscience trouble and a major technical claiming for neuroimagers. In this chapter, we will thus effort to reply the post-obit questions: Why is anatomy a concern for functional imaging of the brain? How tin can anatomical landmarks be accurately identified and used as tools for researchers in the domain of functional brain imaging? What are the relationships between gross anatomy, microanatomy, and role?

In the kickoff department, we summarize the basic knowledge about the sulcal and gyral cortical anatomy that we think is a prerequisite for anyone who wants to develop registration and normalization tools based on encephalon anatomical landmarks. In the 2d section, we investigate the issue of encephalon anatomical variability. Interestingly, this issue has emerged from population studies in functional imaging where the low signal-to-noise ratio of positron emission tomography (PET) images made it necessary to average functional images of dissimilar subjects. In the tertiary department, we focus on the question of structure/function relationships in the man brain, both at the macroscopic and microscopic levels, to underscore the importance of the link betwixt anatomy and function at the individual level. The development of database projects integrating several sources of information such as cytoarchitectony, electrical stimulation, and electrical recordings, too as data from functional imaging methods, including PET, functional magnetic resonance imaging (fMRI), event-related potentials, magnetoencephalography, and electroencephalography, renders always more critical the need for a mutual neuroanatomical reference frame.

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Alzheimer's Disease, Neural Basis of

H. Braak , in International Encyclopedia of the Social & Behavioral Sciences, 2001

4.two Functional Imaging

In vivo neuroimaging with MRI makes possible visualization of activated cerebral regions during the performance of specific motor tasks or in response to external sensory stimuli. Functional MRI, nonetheless, differs from structural MRI in that the information obtained goes beyond the limits of encephalon morphology and topography alone. The operative principle behind the technology is that, in response to increased energy demands, blood flow in stimulated cortical regions of healthy persons may exist elevated by as much as 20–xl percent and oxygen consumption by 5 percent and so that activated brain tissue displays greater MRI signal intensity than nonactivated or insufficiently activated areas. Two additional in vivo diagnostic parameters which are measurable by means of a second functional neuroimaging technique, positron emission tomography (PET), are cognitive glucose metabolism and cholinergic neurotransmission: In individuals with balmy Advertisement, cortical hypoperfusion (decreased blood catamenia) can exist seen in the regions involved using functional MRI. PET-scanned glucose metabolic rates are lowered in both temporoparietal lobes, and reduced cholinergic action tin exist traced bilaterally likewise as symmetrically in the cerebral cortex, including the hippocampus, during PET.

The practical aim of functional MRI and PET neuroimaging is the determination of the degree to which neocortical hypoperfusion and hypometabolism correlate with the severity of early AD-associated deficits. Nonetheless, fifty-fifty proponents of functional neuroimaging caution that, insofar equally Advertizing involves multiple neuronal systems, it is imperative for clinicians to know where in the brain and at which stages the neurotransmitter-specific cortical pathologies (e.one thousand., cholinergic, serotonergic, GABA-ergic, noradrenergic) develop. Also, and perhaps even more importantly, clinicians demand to understand mutual intersystemic implications for the patient's overall prognosis earlier intervening therapeutically (i.eastward., pharmacologically) (Francis et al. 1999). Moreover, the same dilemma mentioned in connection with structural imaging applies hither as well, namely: The actual extent and severity of nerve cell damage or loss only can be surmised, not necessarily deduced or inferred, based on in vivo MRI or PET-detectable regional hypometabolism. Whereas the neurofibrillary pathology in Advert very probably correlates with synapse loss, neuronal loss, and the clinical course of the illness, the circuitous causal interrelationships between the selective vulnerability of specific subsets of nerve cells, neurotransmitter-induced deficits, neurofibrillary tangle and/or beta-amyloid plaque formation, and the clinical picture of AD are only only outset to come up to light.

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Time-Frequency Methodologies in Neurosciences

In Time-Frequency Signal Assay and Processing (2d Edition), 2016

16.half dozen.5 Measuring Cognitive Workload past Assessing EEG Signals

16.vi.v.1 The problem

There is an ongoing interest for cognitive monitoring in the fields of cognitive neuroscience, biomedical engineering, human-computer-interactions, brain-computer interfaces and psychology. The objective is to get together information nigh the user's cognitive state, such as the mental or cognitive activities, retentiveness workload and task engagement. These states indicate how hard a user's cognitive system is working to solve a trouble or use an interface, and tin can be employed to support the user's goals or adapt an interface [60]. In cognitive monitoring, measuring the imposed load on the working memory during a cognitive process, i.eastward., cerebral load, is of high importance. This is to avoid the user's mental overload and maintain efficiency and productivity in disquisitional or high mental load workplaces, such as medical or emergency departments, air traffic control and military operations [60]. Amidst different techniques available to measure cerebral load, monitoring the brain activity using neuro-imaging techniques such equally EEG has been recognized as the most sensitive and consequent reflector of working memoryload [61].

16.half dozen.five.2 Principle

The wavelet transform (WT) provides a (t,f) representation of the given bespeak with a good time-resolution at high frequencies and good frequency resolution at low frequencies, so-called multiresolution belongings [57,59] (see Section two.7.six). The characteristic feature suitable for classifying cognitive load levels can be extracted from the wavelet coefficients. One such feature is the entropy, which is a measure of regularity or guild, and in the case of EEG signals, indicates the degree of synchrony of the neural groups participating in different neural responses.

16.half-dozen.five.3 Methods

Permit v = (v ₁ v ₂ ⋯ v _M)^T denote the wavelet coefficients of an EEG segment in a particular calibration, and p _i is defined as $p_{i} = \frac{| v_{i} |}{\sum_{i = ane}^{Grand} | {five}_{i} |}$ [60]. Note that $\sum_{i = 1}^{One thousand} p_{i} = 1$ . The following entropies can exist calculated as features for capturing the EEG signal's regularity and order variations [threescore]:

i.: Shannon entropy: $H_{South} = - \sum_{i = 1}^{M} p_{i} log (p_{i})$ ,
ii.: Tsallis entropy: $H_{T} = \frac{1}{q - 1} \sum_{i = 1}^{M} (p_{i} - p_{i}^{q}); 0 < q < 1$ ,
three.: Escort-Tsallis entropy: $H_{ET} = \frac{1}{q - 1} [ane - {(\sum_{i = 1}^{Yard} {(p_{i})}^{q^{- 1}})}^{- q}]$ .

16.half-dozen.5.4 Results

In an experiment, an "addition" task with seven levels of difficulty was designed. The task levels of difficulty varied from one-digit addition (very easy) to multidigit addition (extremely difficult). The EEG recordings were conducted nether controlled conditions in an electrically isolated lab, at the ATP Laboratory of National ICT Australia in Sydney, for 12 participants. The higher up-mentioned entropy-basedfeatures were extracted from the artifact-free EEG segments (of 5-s length) from selected EEG channels (i.e., Fp1, AF3, F7, F3, FC1, FC5 FC6, FC2, F4, F8, AF4, Fp2, and Fz) of each participant. The extracted features were then fed into a multilayer neural network classifier, and a leave-one-out cross-validation technique was used for preparation and testing data. The results showed a consistent decline in the medians of the entropy-based features as the task load increased, indicating that the degree of the indicate disorder decreases as the working memory load imposed increases. Classification accuracies of 98.35%, 98.44%, and 92.21% were obtained using H _S, H _T, and H _ET, respectively. The assessment of the strength of phase synchrony among the EEG channels revealed that as the load level increases, the level of synchrony of the cell groups involved in neural responses increases [60]. Meet Section sixteen.4 for another application of stage synchrony.

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