![]() ![]() With advent of relatively cheap and high performance personal computers sophisticated and computation demanding time series analysis methods became available to a broad brain research community. Keywordsįractal analysis Power spectral analysis Topography Cross-correlation Sleep stage classification Best sleep stage classifications were achieved by estimating measureD in temporal EEG channels both at group and individual levels, suggesting that assessing multifractality might be an adequate approach for compact modeling of brain activities. In addition wetested sleep stage classification capability of these measures according to different channels.We found that cross-correlations between fractal and spectral measures as well as between H and D exhibit specific topographic and sleep stage-related characteristics. Therefore, in the present study we investigated the relationship of monofractal and multifractal EEG measures (H and D) with relative band powers and spectral edge frequency across different sleep stages and topographic locations. However, a comprehensive assessment of the relationship between fractal and power spectral measures is still missing. In the literature there are some attempts to relate fractal features to spectral properties. Received in revised form: 30 November 2010įractal nature of the human sleep EEG was revealed recently. Building, III/311, 1111 Budapest, Hungary Corresponding author: Tel.: +36 fax: +36. 50/a, 1083 Budapest, Hungaryī National Institute of Neuroscience, Amerikai út 57, 1145 Budapest, HungaryĬ Institute of Behavioural Sciences, Semmelweis University, Nagyvárad Budapest, HungaryĭHAS–BME Cognitive Science Research Group, Stoczek u. Béla Weiss a, Zsófia Clemens b, Róbert Bódizs c,d, Péter Halász aĪFaculty of Information Technology, Pázmány Péter Catholic University, Práter u. ![]()
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