EEG background activity described by a large computerized database

Aurlien, Harald; Gjerde, Ivar; Aarseth, Jan Harald; Eldøen, G; Karlsen, Bjørn; Skeidsvoll, H; Gilhus, Nils Erik

doi:10.1016/j.clinph.2003.10.019

Cited by 146 publications

(121 citation statements)

References 20 publications

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“…The average of alpha frequency is around 10 Hz, with a range of approximately 8-12 Hz in healthy adults (cf. Aurlien et al, 2004;Chiang et al, 2011;Niedermeyer and Lopes da Silva, 1999). One of the earliest observations relating the alpha frequency (AF) to individual differences in cognitive functioning was the finding of 'mentally retarded' patients (Berger, 1933) exhibiting systematically slower alpha waves.…”

Section: Introductionmentioning

confidence: 99%

Individual alpha peak frequency is related to latent factors of general cognitive abilities

et al. 2013

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Some eighty years after the discovery of the human electroencephalogram (EEG) and its dominant rhythm, alpha (~10 Hz), the neurophysiological functions and behavioral correlates of alpha oscillations are still under debate. Similarly, the biological mechanisms contributing to the general factor of intelligence, or g, have been under scrutiny for decades. Individual alpha frequency (IAF), a trait-like parameter of the EEG, has been found to correlate with individual differences in cognitive performance and cognitive abilities. Informed by large-scale theories of neural organization emphasizing the general functional significance of oscillatory activity, the present study replicates and extends these findings by testing the hypothesis that IAF is related to intelligence at the level of g, rather than at the level of specific cognitive abilities. Structural equation modeling allowed us to statistically control for measurement error when estimating the association between IAF and intellectual functioning. In line with our hypothesis, we found a statistically reliable and substantial correlation between IAF and g (r = .40). The magnitude of this correlation did not differ significantly between younger and older adults, and captured all of the covariation between IAF and the cognitive abilities of reasoning, memory, and perceptual speed. The observed association between IAF and g provides a parsimonious explanation for the commonly observed diffuse pattern of correlations between IAF and cognitive performance. We conclude that IAF is a marker of global architectural and functional properties of the human brain.

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Section: Introductionmentioning

confidence: 99%

Individual alpha peak frequency is related to latent factors of general cognitive abilities

et al. 2013

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“…The results shown the best main-lobe is for rectangular window, the best side-lobe is for Kaiser β (12) and the best SNR is for Hanning. Also the best window according to main-lobe, side-lobe and SNR is Kaiser β (12).…”

Section: Resultsmentioning

confidence: 99%

“…Also the unit impulse response is finite. In this work different finite impulse response filter (FIR) windows methods were used to extract the EEG signal to its basic components (Delta wave, Theta wave, Alpha wave and Beta wave) [12][13][14][15]. …”

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confidence: 99%

A Comparison between Windowing FIR Filters for Extracting the EEG Components

Hassan¹,

Mahmoud²

2015

J Biosens Bioelectron

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IntroductionElectroencephalogram (EEG) used to measure abnormalities in the brain, which related to electrical activity (Figure 1). This measurement tracks and records brain wave patterns by using electrodes are placed on the scalp, and then signals go to a computer to record the results. Abnormal patterns in EEG indicate seizures and other problems [1][2][3][4][5][6]. The important reason for using EEG is to diagnose and show seizure disorders. Also EEGs help to help to identify causes of other problems like sleep disorders and changes in behaviour. To evaluate the brain activity after head injury or before heart or liver transplantation the EEG is a good choice [3]. The electrodes in the conventional scalp EEG are placed on the scalp with a conductive gel or paste which is led to reduce impedance due to dead skin cells. When used on highdensity arrays of electrodes are needed many systems use caps or nets which electrodes are embedded in it. The Electrodes locations and names depend on an international 10-20 system for most clinical and research applications (except when high-density arrays are used). About 19 recording electrodes (plus ground and system reference) are used in most clinical applications. If the spatial resolution is the aim of interest further electrodes can be added for a particular area of the brain and this in clinical or research application. Up to 256 electrodes can be used in High-density arrays, it may become more-or-less evenly spaced around the scalp. Differential amplifier can be used to connect the electrode (one amplifier per pair of electrodes). The other input of each differential amplifier is connected by a common reference electrode. The job of these amplifiers is to amplify the voltage between the reference and active electrode and (1,000-100,000 times, or 60-100 dB voltage gain). Most EEGs systems nowadays are digital, the signal is amplified and pass through ADC and anti-aliasing filter. The sampling rate between 256-512 Hz in the clinical scalp EEG; in some research applications, it may use sampling rates of up to 20 kHz. For adult EEG signal is about 10 μV to 100 μV when the measuring from the scalp and is about 10-20 mV when measuring from subdural electrodes [3,4]. The fundamental components of the EEG system are shown in Figure 1 One of the most basic elements in a digital signal processing system is the finite impulse response (FIR) filter. It can guarantee a linear phase frequency characteristic with frequency characteristic. Also the unit impulse response is finite. In this work different finite impulse response filter (FIR) windows methods were used to extract the EEG signal to its basic components (Delta wave, Theta wave, Alpha wave and Beta wave) [12][13][14][15]. MethodsIn this work comparison between different FIR windowing filters to extract EEG signal to its basic components (delta, theta, alpha and beta). The steps are: 1)EEG signals (Delta, theta, alpha and beta) were extracted by band-pass filter using Rectangular, Hamming, Hanning, Kaiser and Blackman wind...

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“…The EEG amplitude of a normal subject in the awake state recorded with the scalp electrodes is 10 µV to 100 µV. Higher background amplitudes are associated with lower frequencies in normal EEGs for all age groups [2]. Symmetrical low voltage and even visually unrecognizable alpha rhythm was not regarded as pathological.…”

Section: Introductionmentioning

confidence: 99%

Symbolic Entropy of the Amplitude rather than the Instantaneous Frequency of EEG Varies in Dementia

Lin

Tsao

et al. 2015

Entropy

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Abstract:The dynamics of human electroencephalography (EEG) have been proved to be related to cognitive activities. This study separately assessed the two EEG components, amplitude and rhythm, aiming to capture their individual contributions to cognitive functions. We extracted the local peaks of EEGs under rest or photic stimulation and calculated the symbolic dynamics of their voltages (amplitude) and interpeak intervals (instantaneous frequency), individually. The sample consisted of 89 geriatric outpatients in three patient groups: 38 fresh cases of vascular dementia (VD), 22 fresh cases of Alzheimer's disease (AD) and 29 controls. Both sample entropy and number of forbidden words revealed significantly less regular symbolic dynamics in the whole EEG tracings of the VD than the AD and control groups. We found consistent results between groups with the symbolic dynamics in the local-peak voltage sequence rather than the interpeak interval sequence. Photic stimulation amplified the differences between groups. These results suggest that the EEG dynamics which relates to either cognitive functions or the underlying pathologies of dementia are embedded within the dynamics of the amount of but not the interval between each synchronized firing of adjacent cerebral neurons.

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EEG background activity described by a large computerized database

Cited by 146 publications

References 20 publications

Individual alpha peak frequency is related to latent factors of general cognitive abilities

Individual alpha peak frequency is related to latent factors of general cognitive abilities

A Comparison between Windowing FIR Filters for Extracting the EEG Components

Symbolic Entropy of the Amplitude rather than the Instantaneous Frequency of EEG Varies in Dementia

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