Suparerk Janjarasjitt

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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)
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)
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)
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)
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)
—In this study, the wavelet-based fractal analysis is applied to analyze epileptic ECoG data obtained during non-seizure period and epileptic seizure events. The spectral exponents of the epileptic ECoG data obtained using the wavelet-based fractal analysis from various intervals of levels are examined. The computational results show that the estimated(More)
In this study, wavelet-based features of single-channel scalp EEGs recorded from subjects with intractable seizure are examined for epileptic seizure classification. The wavelet-based features extracted from scalp EEGs are simply based on detail and approximation coefficients obtained from the discrete wavelet transform. Support vector machine (SVM), one of(More)