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OBJECTIVE The complexity of the EEG time series during stages of neonatal sleep states is investigated. The relationship between these sleep states, birth status (i.e. preterm and full-term), and the complexity of the EEG is assessed. METHODS Dimensional complexity, an estimate of the correlation dimension (D(2)) of the EEG time series, is used as a novel(More)
Abnormal synaptic maturation and connectivity are possible etiologies of autism. Previous studies showed significantly less alpha activity in autism than normal children. Therefore, we studied the effects of anodal tDCS on peak alpha frequency (PAF) related to autism treatment evaluation checklist (ATEC). Twenty male children with autism were randomly(More)
OBJECTIVE To investigate the relationship between the complexity of sleep EEG time series and neurodevelopment for premature or full-term neonates. METHODS Nonlinear dynamical analysis of neonatal sleep EEG time series is used to compute the correlation dimension D2 which is an index of the complexity of the dynamics of the developing brain. The(More)
The wavelet transform is a natural tool for characterizing self-similar signals. In this work, the spectral exponent γ derived from the wavelet-based representation for 1/f processes is used to investigate the self-similarity of electrocorticography (intracranial EEG) signals from an epilepsy patient. An increase in γ leads to sample signals(More)
Self-similarity or scale-invariance is a fascinating characteristic found in various signals including electroencephalogram (EEG) signals. A common measure used for characterizing self-similarity or scale-invariance is the spectral exponent. In this study, a computational method for estimating the spectral exponent based on wavelet transform was examined. A(More)
Temporal characteristics of the EEG have been extensively studied for their relationship to both sleep and epilepsy. In this work we introduce a computational algorithm that quantifies temporal variability of the EEG signal based on the local minima and the local maxima of the signal. The proposed computational algorithm is applied to examine the temporal(More)
OBJECTIVE Transcranial direct current stimulation (tDCS) has demonstrated efficacy for reducing neuropathic pain, but the respective mechanisms remain largely unknown. The current study tested the hypothesis that pain reduction with tDCS is associated with an increase in the peak frequency spectrum density in the theta-alpha range. METHODS Twenty patients(More)
Temporal characteristics of the EEG have been extensively studied for their relationship to both sleep and epilepsy. In this work, a new computational algorithm for quantifying the temporal variability of signal is proposed. The proposed computational algorithm is based on local minima and local maxima. The temporal variability of ECoG data obtained from(More)