Task Difficulty and EEG Alpha Asymmetry: An Amplitude and Frequency Analysis

Earle, Jonathan B.B.

doi:10.1159/000118482

Cited by 32 publications

(11 citation statements)

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“…The present study shows that the right hemisphere of the brain has much variation in HE for the MCI-AD patients than for the left half. The damage in the right part causes the reduction of nonverbal thinking and also a reason for the inability of motor skills for control (Earle 1988). The lobe vice analysis of HE shows that they are the highest at the temporal lobe.…”

Section: Discussionmentioning

confidence: 99%

Analysis of long range dependence in the EEG signals of Alzheimer patients

2018

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Alzheimer's disease (AD), a cognitive disability is analysed using a long range dependence parameter, hurst exponent (HE), calculated based on the time domain analysis of the measured electrical activity of brain. The electroencephalogram (EEG) signals of controls and mild cognitive impairment (MCI)-AD patients are evaluated under normal resting and mental arithmetic conditions. Simultaneous low pass filtering and total variation denoising algorithm is employed for preprocessing. Larger values of HE observed in the right hemisphere of the brain for AD patients indicated a decrease in irregularity of the EEG signal under cognitive task conditions. Correlations between HE and the neuropsychological indices are analysed using bivariate correlation analysis. The observed reduction in the values of Auto mutual information and cross mutual information in the local antero-frontal and distant regions in the brain hemisphere indicates the loss of information transmission in MCI-AD patients.

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Section: Discussionmentioning

confidence: 99%

Analysis of long range dependence in the EEG signals of Alzheimer patients

2018

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“…Very interesting is the work of Blackhart and colleagues (Blackhart, 2006) reporting that the relative right frontal EEG activity may even predict future development of anxiety symptoms. The reports of Earle J.B (Earle, 1998) showed that task activity during the event related potentials appeared to play a significant role in the determination of parietal and temporal-lobe asymmetry. So in our further work we must analyze these regionsparietal and temporal, for alpha asymmetry either.…”

Section: Discussionmentioning

confidence: 99%

Relation Between Frontal Alpha Asymmetry and Anxiety in Young Patients with Generalized Anxiety Disorder

Demerdzieva

Pop‐Jordanova

2015

Prilozi

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Frontal alpha asymmetry (the relative difference in power between two signals in different hemispheres) has been suggested as biomarker for anxiety. The goal of this study was to evaluate alpha asymmetry in the frontal region for young people (7-18 years) with generalized anxiety disorder, diagnosed according to two statistic manuals (DMS-IV-R and ICD-10), the medical history and the neuropsychological assessment. The QEEG recording and analysis of the obtained results from alpha spectra power and log of alpha spectra power are made in four conditions (eyes open, eyes closed, VCPT and ACPT). The obtained results for alpha power in general showed higher cortical activity in the right hemisphere, associated with negative emotions. The calculated alpha asymmetry separate for eyes open, eyes closed, VCPT and ACPT conditions showed the right activation in all four conditions. In addition, the right frontal asymmetry was specific for the Fp(1)-Fp(2) region, while a greater left frontal activation was recorded for the F(7)-F(8) region. The log of alpha power in general was additionally analyzed. The calculated asymmetry score in general (in a way that the left log transformed score was subtracted from the right) confirmed a greater right activation. Testing the power of the whole alpha band (μV(2)) in general, for all four conditions and for frontal region confirmed the right alpha asymmetries in all participants. The right alpha asymmetry in the frontal region was specific only for the Fp(1)-Fp(2) region (frontopolar region). The only greater left frontal activation was registered between the F(7)-F(8) region. Our findings are supported by many other studies using specific localization methods like fMRI or LORETA source localization.

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“…Since brain waves are dynamic activities which are contingent upon variations in a subject’s performance and state, linked to fluctuations in expectation, attention, arousal, and task strategy [47-52], the generation of command output from an EEG interval involving SSVEP-unrelated information may sometimes cause unexpected pitfalls in BCI operation. To prevent the outputs of trained multiclass SVM from oversensitivity to input data, the present study generated command outputs from effective epochs.…”

Section: Discussionmentioning

confidence: 99%

Improvement of classification accuracy in a phase-tagged steady-state visual evoked potential-based brain computer interface using multiclass support vector machine

et al. 2013

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BackgroundBrain computer interface (BCI) is an emerging technology for paralyzed patients to communicate with external environments. Among current BCIs, the steady-state visual evoked potential (SSVEP)-based BCI has drawn great attention due to its characteristics of easy preparation, high information transfer rate (ITR), high accuracy, and low cost. However, electroencephalogram (EEG) signals are electrophysiological responses reflecting the underlying neural activities which are dependent upon subject’s physiological states (e.g., emotion, attention, etc.) and usually variant among different individuals. The development of classification approaches to account for each individual’s difference in SSVEP is needed but was seldom reported.MethodsThis paper presents a multiclass support vector machine (SVM)-based classification approach for gaze-target detections in a phase-tagged SSVEP-based BCI. In the training steps, the amplitude and phase features of SSVEP from off-line recordings were used to train a multiclass SVM for each subject. In the on-line application study, effective epochs which contained sufficient SSVEP information of gaze targets were first determined using Kolmogorov-Smirnov (K-S) test, and the amplitude and phase features of effective epochs were subsequently inputted to the multiclass SVM to recognize user’s gaze targets.ResultsThe on-line performance using the proposed approach has achieved high accuracy (89.88 ± 4.76%), fast responding time (effective epoch length = 1.13 ± 0.02 s), and the information transfer rate (ITR) was 50.91 ± 8.70 bits/min.ConclusionsThe multiclass SVM-based classification approach has been successfully implemented to improve the classification accuracy in a phase-tagged SSVEP-based BCI. The present study has shown the multiclass SVM can be effectively adapted to each subject’s SSVEPs to discriminate SSVEP phase information from gazing at different gazed targets.

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Task Difficulty and EEG Alpha Asymmetry: An Amplitude and Frequency Analysis

Cited by 32 publications

References 0 publications

Analysis of long range dependence in the EEG signals of Alzheimer patients

Analysis of long range dependence in the EEG signals of Alzheimer patients

Relation Between Frontal Alpha Asymmetry and Anxiety in Young Patients with Generalized Anxiety Disorder

Improvement of classification accuracy in a phase-tagged steady-state visual evoked potential-based brain computer interface using multiclass support vector machine

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