2021
DOI: 10.3390/e23050592
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EEG Fractal Analysis Reflects Brain Impairment after Stroke

Abstract: Stroke is the commonest cause of disability. Novel treatments require an improved understanding of the underlying mechanisms of recovery. Fractal approaches have demonstrated that a single metric can describe the complexity of seemingly random fluctuations of physiological signals. We hypothesize that fractal algorithms applied to electroencephalographic (EEG) signals may track brain impairment after stroke. Sixteen stroke survivors were studied in the hyperacute (<48 h) and in the acute phase (∼1 week afte… Show more

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Cited by 12 publications
(7 citation statements)
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“…EEG recordings (exploratory) of study participants reflected a pattern of brain activity indicative of recovery exclusively in the ENTF group (49). More specifically, the EEG results are consistent with improvement in movement inhibition or motor learning (58) as well as increased signal complexity, a characteristic of healthy brain activity (59). In effect, the EEG data provide evidence for a biomarker of recovery putatively linked to plasticity (59) in the ENTF group but not in the sham group.…”
Section: Discussionsupporting
confidence: 62%
“…EEG recordings (exploratory) of study participants reflected a pattern of brain activity indicative of recovery exclusively in the ENTF group (49). More specifically, the EEG results are consistent with improvement in movement inhibition or motor learning (58) as well as increased signal complexity, a characteristic of healthy brain activity (59). In effect, the EEG data provide evidence for a biomarker of recovery putatively linked to plasticity (59) in the ENTF group but not in the sham group.…”
Section: Discussionsupporting
confidence: 62%
“…A number of earlier studies investigated the relation between fractal-derived brain measures and pathological conditions [27,28,30,32,[60][61][62][63][64]. However, the studies targeting pDoC patients focused on either discriminating healthy individuals from UWS/MCS patients or on performing a differential diagnosis of UWS/MCS [28,32,33].…”
Section: Discussionmentioning
confidence: 99%
“…We will investigate both linear (i.e., power spectral density [ 37 ], time-varying connectivity [ 38 ]) and nonlinear EEG features (i.e., entropy and fractal properties [ 39 , 40 ] in the scalp and inverse domains (Electrical Source Imaging [ 41 ]) from the data recorded at rest and during the execution of target reaching. The laterality index, which describes the contrast in amount of activation (i.e., relative power in the alpha band) between the right and left hemispheres, will be calculated during the target reaching.…”
Section: Methodsmentioning
confidence: 99%