The EEG Split Alpha Peak: Phenomenological Origins and Methodological Aspects of Detection and Evaluation

Olejarczyk, Elżbieta; Bogucki, Piotr; Sobieszek, Aleksander

doi:10.3389/fnins.2017.00506

Cited by 24 publications

(36 citation statements)

References 41 publications

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“…The main sources were located in the occipital lobe for each electrode reference, excluding the REST, where they are located mainly in the parietal and central areas (see adjacency matrices in Fig 6 and graphs for DTF in Fig 8 ). A similar consequence of EEG data re-referencing using REST, i.e., the sources of alpha activity being located at parietal rather than occipital derivations, was observed in our previous study where we investigate the generation of alpha rhythm [ 42 ].…”

Section: Discussionsupporting

confidence: 52%

“…The statistical significance of the above connectivity measures was evaluated using indices based on graph theory. The choice of reference electrode may have an impact on the connectivity analysis [ 37 – 42 ]. Thus, we compared our results obtained using the scalp EEG data with results obtained using data that was re-referenced using the average reference electrode (AVERAGE) [ 43 ], scalp surface Laplacian or current source density (CSD) analysis [ 44 – 46 ] and reference electrode standardization techniques (REST) [ 37 , 47 – 48 ].…”

Section: Introductionmentioning

confidence: 99%

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Graph-based analysis of brain connectivity in schizophrenia

2017

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The present study evaluated brain connectivity using electroencephalography (EEG) data from 14 patients with schizophrenia and 14 healthy controls. Phase-Locking Value (PLV), Phase-Lag Index (PLI) and Directed Transfer Function (DTF) were calculated for the original EEG data and following current source density (CSD) transformation, re-referencing using the average reference electrode (AVERAGE) and reference electrode standardization techniques (REST). The statistical analysis of adjacency matrices was carried out using indices based on graph theory. Both CSD and REST reduced the influence of volume conducted currents. The largest group differences in connectivity were observed for the alpha band. Schizophrenic patients showed reduced connectivity strength, as well as a lower clustering coefficient and shorter characteristic path length for both measures of phase synchronization following CSD transformation or REST re-referencing. Reduced synchronization was accompanied by increased directional flow from the occipital region for the alpha band. Following the REST re-referencing, the sources of alpha activity were located at parietal rather than occipital derivations. The results of PLV and DTF demonstrated group differences in fronto-posterior asymmetry following CSD transformation, while for PLI the differences were significant only using REST. The only analysis that identified group differences in inter-hemispheric asymmetry was DTF calculated for REST. Our results suggest that a comparison of different connectivity measures using graph-based indices for each frequency band, separately, may be a useful tool in the study of disconnectivity disorders such as schizophrenia.

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Section: Discussionsupporting

confidence: 52%

Section: Introductionmentioning

confidence: 99%

Graph-based analysis of brain connectivity in schizophrenia

2017

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“…The complexity can be infer by studying of interactions between signals. The interactions between the EEG signals can be evaluated by different connectivity measures and indices based on graph theory ( Olejarczyk et al, 2017a , b ; Olejarczyk and Jernajczyk, 2017 ). Recently, a new field of Network Physiology has been developed ( Bartsch et al, 2015 ; Liu et al, 2015 ; Ivanov et al, 2016 ).…”

Section: Discussionmentioning

confidence: 99%

Complexity Analysis of EEG Data in Persons With Depression Subjected to Transcranial Magnetic Stimulation

et al. 2018

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Aim: The aim of this work was to study the neurophysiological effect of repetitive transcranial magnetic stimulation (rTMS) applied to the left dorsolateral prefrontal cortex (DLPFC) in 8 patients with major depression disorder (MDD) and 10 patients with bipolar disorder (BP), considering separately responders and non-responders to rTMS therapy in each of both groups.Methods: The Higuchi’s Fractal Dimension (FD) was analyzed from 64-channels EEG signals in five physiological frequency bands and every channel separately. Changes of FD were analyzed before and after 1st, 10th, and 20th session of rTMS.Results: Some differences in response to the rTMS therapy was found across individual groups. In MDD responders, FD decreased in all bands after longer stimulation (20th session). Whereas, in BP non-responders, FD decreased after 1st session in all bands as well as after 10th session in lower frequencies (delta and theta). In MDD non-responders and BP responders FD increased at the beginning of the therapy (1st and 10th session, respectively), but the final FD value did not changed in comparison to the initial FD value, except the FD decrease for theta band in BP responders. Comparison between groups showed a higher FD in MDD responders than in MDD non-responders in every band before as well as after stimulation. In contrast to MDD patients, FD was lower in BP responders than in BP non-responders in higher frequency bands (alpha, beta, and gamma) in both conditions as well as in lower frequency bands (delta and theta) after stimulation. Comparing both groups of responders, FD was lower in MDD than in BP in every band, except alpha. In case of non-responders, FD was higher in BP than in MDD in all bands in both conditions.Conclusion: The results showed that FD may be useful marker for evaluation of the rTMS effectiveness and the therapy progress as well as for group differentiation between MDD and BP or between responders and non-responders. The changes of FD under the influence of rTMS allow to unambiguously conclude whether the effect of stimulation is positive or negative as well as allow to evaluate an optimal time of rTMS.

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“…Gold-plated electrodes were placed on the scalp of each one of the subjects that participated in the experimental procedure, according to the 10-20 system (displayed in Figure 2) at positions O1 and O2. The specific positions were chosen because, although alpha rhythms can be also generated in other parts of the brain, they are considered to exhibit greater amplitude in the posterior part of the brain, specifically at derivations O1 and O2 [26]. The reference electrode was placed on the left earlobe (A1), while the ground electrode was placed on the right earlobe (A2).…”

Section: Signal Acquisitionmentioning

confidence: 99%

“…Brainwaves are detected in the frequency domain, having signal intensity measured in microvolts (µV) and signal frequency usually ranging from 1 to 100 Hz. According to their frequency, there are specific bands classified as delta (δ) (1-4 Hz), theta (θ) (4-7 Hz), alpha (α) (8)(9)(10)(11)(12)(13), beta (β) (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (γ) (>30 Hz) [4]. evaluation and require extensive training.…”

Section: Introductionmentioning

confidence: 99%

Robot Motion Control via an EEG-Based Brain–Computer Interface by Using Neural Networks and Alpha Brainwaves

et al. 2019

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Modern achievements accomplished in both cognitive neuroscience and human-machine interaction technologies have enhanced the ability to control devices with the human brain by using Brain-Computer Interface systems. Particularly, the development of brain-controlled mobile robots is very important because systems of this kind can assist people, suffering from devastating neuromuscular disorders, move and thus improve their quality of life. The research work presented in this paper, concerns the development of a system which performs motion control in a mobile robot in accordance to the eyes' blinking of a human operator via a synchronous and endogenous Electroencephalography-based Brain-Computer Interface, which uses alpha brain waveforms. The received signals are filtered in order to extract suitable features. These features are fed as inputs to a neural network, which is properly trained in order to properly guide the robotic vehicle. Experimental tests executed on 12 healthy subjects of various gender and age, proved that the system developed is able to perform movements of the robotic vehicle, under control, in forward, left, backward, and right direction according to the alpha brainwaves of its operator, with an overall accuracy equal to 92.1%.A Brain-Computer Interface (BCI) is a system that enables communication between brain and machines. A BCI, in order to perform its purposes, records brain signals, interprets them, and produces corresponding commands to a connected machine [5]. BCI technology is used in various applications, such as security and authentication, education, neuromarketing and advertisement, games and entertainment, and several medical applications, such as cognitive neuroscience, brain-related prevention and diagnosis of health problems, rehabilitation, and restoration [6][7][8][9].This article presents the development of a BCI-based system that performs the motion control of a robotic vehicle by using brainwaves of a human operator. After capturing the brainwaves via EEG, a set of features is extracted and given as input to a neural network, which is trained to predict the desired movement of the robotic vehicle. The rest of this paper is organized as follows: In Section 2, the theoretical background of the research carried out is set up. In Section 3, the structure and operation of the proposed system are explained. In Section 4, the performance of the system is evaluated through the description of the experimental tests made, and the presentation of the corresponding results and discussion on them. Finally, Section 5 concludes the article and proposes future research work. Theoretical Background BCI TypesA BCI provides an interconnection platform that supports the full duplex communication between the brain and an external device. According to the way that BCIs use to set up the brain-device interconnection, they are classified as non-invasive or invasive. Non-invasive BCIs use electrodes placed on the scalp. They are easy and safe to use, low-cost, portable, and offer a relatively hig...

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The EEG Split Alpha Peak: Phenomenological Origins and Methodological Aspects of Detection and Evaluation

Cited by 24 publications

References 41 publications

Graph-based analysis of brain connectivity in schizophrenia

Graph-based analysis of brain connectivity in schizophrenia

Complexity Analysis of EEG Data in Persons With Depression Subjected to Transcranial Magnetic Stimulation

Robot Motion Control via an EEG-Based Brain–Computer Interface by Using Neural Networks and Alpha Brainwaves

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