Individual Alpha Peak Frequency’s Dataset Through Neurofeedback’s Protocol

Peralta-Malváez, Lizbeth; Etcheverry, Gibran

doi:10.1007/978-3-030-01845-0_138

Cited by 1 publication

(4 citation statements)

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“…In both, the activities performed were related to cognitive skills. For future references, we will refer the dataset from [ 16 ] as LGR and the one from [ 17 ] as DM (related to the authors’ names of each dataset). In Table 1 , different characteristics of each dataset are described, such as the headset used for acquisition, its sampling rate, and the number of recordings.…”

Section: Methodsmentioning

confidence: 99%

“…All statistical tests were carried out with a 5% significance level. We were particularly interested in looking for significant differences in the PSD values at alpha, low- and high-beta, or gamma bands, given the previously reported findings in [ 16 , 17 ], respectively. Please note that a change in the frequency band was considered to be relevant when the majority of its PSD values (with 1 Hz resolution) turned out to have significant differences between the groups.…”

Section: Methodsmentioning

confidence: 99%

“…Please note that the main goal of this dataset was to study the spectrum behavior in different frequency bands during the process of learning a new skill. They reported a significant decrease in the PSD of beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29) and gamma (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40) bands. With this information, the corresponding statistical analysis for beta (low and high) and gamma bands was performed on selected channels and significant changes found are presented in Figure 7.…”

Section: Dataset Dmmentioning

confidence: 99%

“…As described previously, this approach can be helpful to the experts of the area of neurosciences, like neurologists and academics, to make diagnosis and medical decisions related to patient performance. In this research, we focus our attention in showing the applicability of our proposed technique through the analysis of cognitive abilities that have been previously measured in two different datasets previously analyzed in [ 16 , 17 ]. Both datasets acquire QEEG data related to the development of a cognitive activity.…”

Section: Introductionmentioning

confidence: 99%

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Using Data Assimilation for Quantitative Electroencephalography Analysis

et al. 2020

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We propose a method based on the ensemble Kalman filter (EnKF) together with quantitative electroencephalogram (QEEG) coherence and power spectrum analysis for evaluating changes in brain activity associated with cognitive processes. Such analysis framework has been widely used in the context of data assimilation (DA) in areas such as geosciences, meteorology, and aerospace. However, the use of this approach is less common in neurosciences. In our case, EnKF highlights the spectral contribution of brain signals that are more likely (according to their coherence analysis) to be related to the cognitive process of interest. The power enhancement, due to the cognitive activity, is later validated in the power spectrum analysis by comparing through statistical tests relevant frequency content in two datasets in which assessing the development of cognitive abilities is of interest: the process of getting concentrated and of learning a new skill. Our results show that our DA-based methodology can highlight important frequency characteristics of the electroencephalogram (EEG) data that have been related to different cognitive processes. Hence, our proposal has the potential to understand of neurocognitive phenomena that is tracked through QEEG.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%