Quantitative EEG Analysis Methods and Clinical Applications (Engineering in Medicine & Biology)

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Kortelainen, S. Chua, J. Astrid, A. DOI: Al-Nashash, H. Mir, H. Al-Nashash, D. Kerr, A. All and N. Al-Nashash, A. Kerr, N. Thakor and A. Al-Nashash, N. Fatoo, N. In parallel, over the last two decades, significant growth was noted in the research interest on EEG, as the full investigation of neurodynamic time-sensitive biomarker that helps in detecting cortical abnormalities associated with cognitive decline and dementia [ 4 — 7 ]. An EEG marker would be a noninvasive method that may have the sensitivity to detect dementia early and even classify the degree of its severity at a lower cost for mass screening.

EEG is also widely available and faster to use than other imaging devices [ 8 , 9 ]. This review has focused on using EEG as an investigating tool and physiological biomarker to identify dementia in early stages and classify the degree of its severity by signal processing and analysis. The review aims to reveal subtle changes that might define indicators for the early detection of dementia that will help medical doctors and clinicians in planning and providing a more reliable prediction of the course of the disease in addition to the optimal therapeutic program to provide dementia patients additional years of a higher quality of life.

Dementia occurs when the brain has been affected by a specific disease or condition that causes cognitive impairment [ 10 ]. The diagnosis of dementia is usually based on several criteria, such as the medical history of patients with clinical, neurological, and psychological examination, laboratory studies, and neuroimaging [ 3 ]. Dementia is associated with neurodegenerative disorder diversity, as well as neuronal dysfunction and death. Dementia has different types based on its cause; these types include Alzheimer's disease AD , vascular dementia VaD , Lewy body, frontotemporal dementia FTD , and Parkinson's disease, among others [ 2 , 11 ].

AD and VaD are considered the two most common types of dementia in the world, and thus the present review deals with the effect of AD and VaD on the brain [ 12 ]. Half of people aged 85 years or older have AD, and this number will roughly double every 20 years due to the aging population [ 14 , 15 ]. Several neuropathological changes act together to develop AD. These changes include loss of neuronal cell and development of neurofibrillary tangles and amyloid plaques in the hippocampus, entorhinal cortex, neocortex, and other regions of the brain.

These changes can also occur in a nondemented individual, and they are associated with AD development even before typical cognitive symptoms are evident [ 16 , 17 ]. The reduction in cholinergic tone caused by neural damage results in an increase in cognitive difficulties [ 18 ]. VaD is another type of dementia. VaD is the loss of cognitive function caused by ischemic, ischemic-hypoxic, or hemorrhagic brain lesions as a result of cerebrovascular disease and cardiovascular pathologic changes, such as ischemic heart disease and stroke [ 21 — 23 ]. Cognitive impairment introduces individuals to the dementia spectrum that is illustrated in Figure 1.

The dementia spectrum can be viewed as a sequence in the cognitive domain that starts from mild cognitive impairment MCI and ends with severe dementia, and the period beyond dementia in which the brain is at risk is called cognitive impairment no dementia CIND [ 24 ].

MCI refers to the decline in cognitive function that is greater than expected with respect to the age and education level of an individual, but the reduced cognitive function does not interfere with daily activities. Clinically, MCI is the transitional stage between early normal cognition and late severe dementia and is considered heterogeneous because some MCI patients develop dementia, whereas others stay as MCI patients for many years.

However, patients who were diagnosed with MCI have a high risk to develop dementia, that is, threefold that of people without a cognitive dysfunction. The most commonly observed symptoms of MCI are limited to memory, whereas daily activities of patients remain the same [ 25 ]. As dementia diagnosis is not easily performed due to the heterogeneity of the symptoms within the cognitive impairment spectrum, it may be advisable to integrate the neuropsychological testing with biomarkers. The latest diagnosis criteria for AD and MCI support this idea as they highlight the importance that several biomarkers structural MRI, FDG-PET, and biochemical analyses of the cerebrospinal fluid have to confirm that a pathological process of AD is, indeed, the cause of the cognitive symptoms [ 26 — 29 ].

The diagnosis criteria usually focus of assessing diverse dementia signs, particularly memory disturbance. Several validate clinical neuropsychological assessments are used to assess cognitive domain including but not limited to Trail Making Test TMT [ 40 ] and Clock Drawing Test CDT [ 41 ] for attention and executive function, Rey Osterrieth Figure Copy [ 42 ] for construction praxis test, and Phonological and Semantic fluency Token test for language test [ 43 ].

Ideally, the biomarker should detect the neuropathological processes even before a clinical diagnosis and should help in identifying people who are at risk of developing dementia.


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The biomarkers for the early detection of dementia may include numerous studies in multiple fields and may be divided into four main categories, namely, biochemical, genetic, neuroimaging, and neurophysiology [ 2 , 3 , 11 , 46 ]. Two main types of biochemical markers were identified to reflect the pathological events, particularly detection of dementia, cerebrospinal fluid CSF , and serum [ 2 , 47 ]. However, both CSF and serum are used as markers to identify dementia, but the sensitivity and the specificity of these tests are limited [ 11 ].

Gene expression profile is considered a promising approach for the early detection of dementia. Several studies have been conducted through the genetic analysis of related disorders, such as AD, to evaluate the genetic risk factor that may lead to dementia. Moreover, blood-based gene expression profiling has been described as capable of diagnosing brain disorders by several independent groups. Numerous advantages are offered by the expression profiling of whole blood RNA in deciphering aberrant patterns of gene regulation in neurogeneration.

Therefore, the genetic biomarker provides an indication to develop dementia but also needs other biomarkers, such as neuroimaging and chemical biomarkers [ 2 , 3 , 11 ]. Neuroimaging has been available for a few decades. This technique can be classified into structural and functional based on the principal information that it provides. Both magnetic resonance imaging MRI and computed tomography CT are structural imaging techniques; they help clarify the brain diagnosis by detecting the affected area and the type of atrophy or vascular damage.

The role of CT is to distinguish two structures and separate them from each other, as CT has good spatial resolution. By contrast, MRI distinguishes the differences between two arbitrarily similar but not identical tissues. MRI provides a good contrast resolution. Positron emission tomography and single photon emission computed tomography are considered functional imaging techniques that can measure brain metabolism parameters, such as regional cerebral blood flow and regional cerebral glucose metabolism. These parameters provide good indication for AD and VaD before morphological changes occur.

Moreover, functional MRI is used to measure the brain function over time based on blood oxygen level at rest. It indirectly reflects neuronal activity and identifies the brain activities that are associated with cognitive tasks. Functional imaging techniques are suitable in early dementia detection and diagnosis [ 2 , 3 , 11 ]. These techniques have high spatial resolution for anatomical details but limited temporal resolution.

Thus, these neuroimaging techniques are incapable of differentiating the stages within the brain distribution network in series or in parallel activation [ 51 ]. Additionally, CT and MRI may be affected by fluid imbibition after brain injury in some cases, thus becoming incapable of detecting the best risk changes or becoming inadequately sensitive to detect dementia in its early stages [ 52 ].

Neural changes associated with dementia can also be detected with clinical biomarkers, such as EEG, quantitative electroencephalography, event related potential, transcranial magnetic stimulation, and Vagus nerve stimulation [ 2 , 18 ]. EEG is a neurosignal that tracks information processing with milliseconds precision. It has been subjected to interpretation by clinician visual inspection that results in acceptable and successful diagnosis results.

However, EEGs are characterized by spatial resolution that is lower than that of other neuroimaging techniques, although these techniques do not provide functional information about the brain in addition to their limitation in temporal resolution; EEG provides high temporal resolution and it is thus crucial for studying brain activity [ 53 , 54 ].

Thus, the interpretation of the degree of EEG abnormality and severity of dementia are the benefits of signal processing and analysis of EEG. EEG signal analysis provides a relatively precise localization of electrical activity sources by tracking the hierarchical connectivity of neurons in the recording place.

EEG may provide useful indication of the patterns of brain activity if it is integrated with other biomarkers, such as structural and functional neuroimaging [ 51 ]. With the dramatic progress in EEG devices, sensors, and electrodes, this review has been focused solely on the function of EEG as a subtle and suitable biomarker in explicitly identifying the neuronal dynamics and cognitive manifestation in most dementia cases, such as AD and VaD, through techniques of EEG signal analysis and processing. As a neurophysiological biomarker, EEG can characterize different physiological and pathological conditions, such as dementia effects on cortical function distribution.

EEG could be used not only as a clinical diagnosis tool, but also as a tool for predicting the stages of dementia [ 7 ]. Numerous studies have been conducted to deal with EEG changes associated with dementia and to identify the degree of severity of dementia, and some studies support the possibility for EEG to detect dementia in early stages [ 55 — 59 ].

For instance, Henderson et al.

EEG may play an important role in detecting and classifying dementia because of its significant influence on dementia abnormalities in terms of rhythm activity. EEG is useful for clinical evaluation because of its ease of use, noninvasiveness, and capability to differentiate types and severity of dementia at a cost lower than that of other neuroimaging techniques [ 8 , 9 ]. To deal with EEG signals and to extract useful information and features that help in early dementia diagnosis, an EEG signal should be illustrated in terms of its rhythmic activity [ 9 ].

EEG can be classified into the following five rhythms according to their frequency bands as shown in Figure 2. This waveform is prominent during sleep, arousal in older children and adults, emotional stress, and idling. This wave has been linked to activities such as focusing, attention, mental effort, and stimulation processing [ 66 ]. This waveform can be recorded frontally in adults and posteriorly in children [ 62 ]. EEG frequency waveform.

EEG has been used as a benchmark for the detection and diagnosis of dementia for two decades. EEG can diagnose the two most common types of dementia i. The first EEG clinical observation was illustrated by Berger in the beginning of the last century [ 74 , 75 ]. The interpretation of the conventional visual characteristics related to AD can be summarized by slowing the EEG dominant posterior rhythm frequency, increasing the diffused slow frequency, and reducing both alpha and beta activities, whereas the occipital alpha activity is preserved and theta power is increased in the case of VaD.

The delta power is increased in both AD and VaD patients [ 4 , 76 ]. The computerized EEG signal analysis provides quantitative data, including reduced mean frequency, increased delta and theta power along with decreased alpha and beta power, reduced coherence in the cortical area, and reduced EEG complexity in dementia patients [ 4 ].

Numerous studies by Moretti et al. However, EEG may exhibit normal frequency and may appear similar to normal aged control subjects during the earliest stages of dementia [ 4 ]. Nonetheless, EEG signal analysis may contribute to the deeper understanding of dementia because such computerized analysis provides quantitative data instead of mere visual inspection. The recorded EEG needs successive stages of signal processing to extract meaningful markers from the EEG signal of dementia patients, and these markers reflect brain pathological changes.

The main stages of EEG signal processing are denoising, feature extraction, and classification. Figure 3 illustrates the stages of EEG signal processing.

EEG/ERP Analysis: Methods and Applications

EEG is a medical device that reflects the electrical activity of the neurons of the brain and records from the scalp with metal electrode and conductive media [ 80 ]. For dementia patients, several procedures have been proposed to record the EEG signal; for instance, the gold plate cup electrodes shown in Figure 5 have been used to record EEGs. The skin should be swabbed with alcohol and gel or paste should be applied before placing the electrode on the scalp to reduce the movement of the device and improve the electrode conductivity; the EEG electrode-scalp contact impedance should be below five kilo-ohms to record good quality signal [ 81 ].

Referential montage is the most popular montage used for EEG recording for dementia that is employed to record the voltage difference between the active electrode on the scalp and the reference electrode on the earlobe, for example, as shown in Figure 6 [ 82 , 83 ]. For fruitful clinical application, the EEG of dementia patients has been recorded in a specialized clinical unit state with the 10—20 system of the international federation, which is adopted by the American EEG Society, while resting with eyes comfortably closed, as shown in Figure 7.

Hamadicharef et al. The 10—20 EEG electrodes placement system. An example of the most popular EEG device contains a low pass, high pass, and notch filters. Typical frequency values for low pass filter LPF i. Typically, the frequency for EEG recording for dementia range is from 0. Finally, the EEG signal will be printed on papers, displayed on the computer screen, and stored for further examination in the next stage [ 81 ].

The reliability of the recorded EEG signal is heavily affected by its noise factors. Most artifacts overlap with the frequencies of EEG signals. The artifacts that contaminated the EEG signal are divided into physiological e.

Introduction

The noise has a direct effect on EEG signal properties, and thus different signal processing techniques have been applied to overcome this problem and to extract relevant information from the recorded EEG signal. In order to focus on the role of EEG in the diagnosis of dementia, the mathematical details have been simplified in the text.

This section discusses the most popular and effective methods used for EEG denoising. Independent component analysis ICA is a blind source separation higher order statistical method used to split a set of recorded EEG signal i. Langlois et al. Wavelet transform WT is an effective denoising procedure that was introduced to process nonstationary signals, such as EEG.

Zikov et al. The continuous wavelet transform CWT can be used as a set of decomposition functions called mother wavelet; the most popular mother wavelets used in biomedical signal denoising are Daubechies, coiflets, and dyme, as shown in Figure 8.

ARTECH HOUSE USA : Quantitative EEG Analysis Methods and Clinical Applications

WT is considered a method for multiresolution analysis that provides varying resolutions at different time and frequency [ 99 ], as shown in Figure 9. Nazareth et al. This merge assists ICA in distinguishing the signal and noise even if both nearly have the same or higher amplitude and removes overlapping noise signal.

Furthermore, the WT can decompose EEG signals into different subbands based on the decomposition levels [ — ]. The ICA-WT technique has illustrated successful results in removing the electrooculography and muscle activity artifacts [ ]. Accordingly, this technique is useful in revealing hidden EEG characteristics by the next stage.

Thus, the signal is ready for the next stage i. The denoised EEG signal from the previous stage undergoes feature extraction to detect dementia and develop a useful diagnostic index using EEG. This stage aims to extract the useful information from the EEG of dementia patients by linear and nonlinear techniques. Linear techniques have been used to extract meaningful features from the EEG of dementia patients that are useful as early dementia indices. Jeong used linear techniques based on coherence and spectral calculations that were used to find EEG abnormalities [ 4 ]. A slowdown in EEG signals in dementia is illustrated by the shifting of power to the lower frequency and the decrease in interaction among the cortical area i.

Spectral analysis has intensively been used to gain insight into dementia, for instance, Escudero et al. Both spectral features provide information about the relative power of low and high frequencies that reflect the local synchronization of the neural assemblies [ ].

Thereafter, the electrical brain activity for dementia patients is characterized by the slowing of brain frequency, and this property can be performed using MF and SpecEn [ ]. AD and VaD patients share spectral analysis properties, such as the slowing of alpha power and increase in delta power, but theta power is higher in VaD patients than in AD patients [ 86 ]. Generally, the severity of cognitive impairment and the degree of EEG abnormalities are correlated [ 4 ]. EEG coherence is used to evaluate the cortical connection functionality and quantify cortico-cortico or cortico-subcortical connection.

[NEUROSCIENCE INTRO] EEG preprocessing in Brain Vision Analyzer - Part 3

Moreover, the coherence function can be used to quantify the linear correlation and detect the linear synchronization between two channels; however, this function does not distinguish the directionality of the coupling [ , ]. A decrease in coherence is interpreted as a reduction in linear function connection and function uncoupling in the cortical area.

By contrast, an increase in coherence is interpreted as augmented linear function connection and function coupling in the cortical area [ 51 ]. Nonlinear dynamic techniques have been used intensively to analyze the EEG signal, particularly the EEGs of dementia patients, for decades. Researchers have used EEG to investigate the complex dynamic information that is reflected from the brain cortex and recorded by EEG devices [ , ]. The hypothesis that the brain is stochastic may be rejected based on the capability of the brain to perform sophisticated cognitive tasks thanks to its complicated structure.

Moreover, brain neurons are controlled by nonlinear phenomena, such as threshold and saturation processes, such that brain behavior can be classified as nonlinear. The nonlinear dynamic analysis may be considered a complementary approach in detecting mental diseases, because it provides additional information to that of traditional linear methods [ , ]. Moreover, numerous methods have been introduced to study time series EEG data from human brain activity to understand and detect EEG abnormalities. The first nonlinear methods that were used to analyze EEG are the correlation dimension D 2 and the first Lyapunov exponents L 1.

D 2 was applied by Grassberger and Procaccia in to quantify the number of independent variables that are necessary to describe the dynamic system. It was used to provide the statistical characteristic of the system.

tutoxubywawa.tk By contrast, L 1 was applied by Wolf in as a dynamic measure to gauge the flexibility of the system [ , ]. Early detection of dementia can be predicted using fractal dimension FD , zero-crossing interval ZCI , entropy, such as sample entropy SampEn and Kolmogorov entropy, central tendency measure, and Hojorth-Index.

Henderson et al. The derivation of FD of the autocorrelation function can be found in [ ]. Lempel-Ziv-Welch LZW is a metric that has been applied to evaluate the signal complexity by measuring the number of distinct substring and their rate of recurrence along the time series; Ferenets et al. Several methods have dealt with the complexity or irregularity in the ability of the system to create information by entropy methods, such as Tsallis entropy TsEn , approximation entropy, SampEn, and multiscale entropy MSE [ 55 , 61 , , — ]. To sum up, linear spectral methods have been used traditionally in the field.

Their interpretation may be more straightforward for clinicians, as they are closely related to the power associated with different brain rhythms alpha, beta, delta, and theta , whereas nonlinear techniques may provide complementary information. Nonlinear methods are motivated by the nonlinear behavior of the neurons in the brain. Both approaches have been used to inspect the EEG activity in dementia, but most studies have focused on only one of those families of methods and there are few comprehensive comparative studies [ ]. Despite potentially promising findings, the sizes of the analyzed datasets limit the results.

These features are applied to the next stage to estimate the degree of the severity of dementia. The classification staging is necessary to predict the qualitative properties of the mental state of dementia patients. In this stage, the feature vectors extracted from the previous stage were classified into three categories, namely, CIND, MCI, and dementia. Feature vectors must be analyzed further before being applied to the classifier to avoid overloading the classifier and reduce the computational time, increasing the accuracy of classification.

These feature vectors can be processed using dimensionality reduction techniques as shown in Figure These methods are well-established methods for dimensionality reduction. PCA is a widely used method to avoid the redundancy because of high-dimensional data [ — ]. The dimensionality-reduced features were used as an input to the classifiers to improve the accuracy of the classification of the severity of dementia by EEG signal analysis. Five EEG features were used to quantify temporal and spatial signal variations.

Journal Update

Two distance measures for the single-channel and multi-channel variations of these features were defined. The global thresholds were determined by three-fold cross-validation and Youden's J statistic in conjunction with receiver operating characteristics ROC curves. We observed high sensitivity of The method has thus shown great potential and is promising as a possible tool for both EEG-based clinical applications and EEG-related research.

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