Browsing by Subject "Brain Signal Analysis"

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    A new time-frequency analysis technique for neuroelectric signals
    (2002) Tüfekçi, D. İlhan
    In the presence of external stimuli, the functioning brain emits neuroelectrical signals which can be recorded as the Event Related Potential (ERP) signals. To understand the brain cognitive functions, ERP signals have been the subject matter of many applications in the field of cognitive psychophysiology. Due to the non–stationary nature of the ERP signals, commonly used time or frequency analysis techniques fail to capture the time–frequency domain localized nature of the ERP signal components. In this study, the newly developed Time–Frequency Component Analyzer (TFCA) approach is adapted to the ERP signal analysis. The results obtained on the actual ERP signals show that the TFCA does not have a precedent in resolution and extraction of uncontaminated individual ERP signal components. Furthermore, unlike the existing ERP analysis techniques, the TFCA based analysis technique can reliably measures the subject dependent variations in the ERP signals, which iiiopens up new possibilities in the clinical studies. Thus, TFCA serves as an ideal tool for studying the intricate machinery of the human brain.
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    Time-frequency component analyzer and its application to brain oscillatory activity
    (Elsevier, 2005-06-30) Özdemir, A. K.; Karakaş S.; Çakmak, E. D.; Tüfekçi, D. İ.; Arıkan, Orhan
    Currently, event-related potential (ERP) signals are analysed in the time domain (ERP technique) or in the frequency domain (Fourier analysis and variants). In techniques of time-domain and frequency-domain analysis (short-time Fourier transform, wavelet transform) assumptions concerning linearity, stationarity, and templates are made about the brain signals. In the time–frequency component analyser (TFCA), the assumption is that the signal has one or more components with non-overlapping supports in the time–frequency plane. In this study, the TFCA technique was applied to ERPs. TFCA determined and extracted the oscillatory components from the signal and, simultaneously, localized them in the time–frequency plane with high resolution and negligible cross-term contamination. The results obtained by means of TFCA were compared with those obtained by means of other commonly used techniques of ERP analysis, such as bilinear time–frequency distributions and wavelet analysis. It is suggested that TFCA may serve as an appropriate tool for capturing the localized ERP components in the time–frequency domain and for studying the intricate, frequency-based dynamics of the human brain. © 2004 Elsevier B.V. All rights reserved