کد مقاله کد نشریه سال انتشار مقاله انگلیسی ترجمه فارسی نسخه تمام متن
404765 677448 2007 16 صفحه PDF سفارش دهید دانلود رایگان
عنوان انگلیسی مقاله ISI
A machine learning approach to the analysis of time–frequency maps, and its application to neural dynamics
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موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
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A machine learning approach to the analysis of time–frequency maps, and its application to neural dynamics
چکیده انگلیسی

The statistical analysis of experimentally recorded brain activity patterns may require comparisons between large sets of complex signals in order to find meaningful similarities and differences between signals with large variability. High-level representations such as time–frequency maps convey a wealth of useful information, but they involve a large number of parameters that make statistical investigations of many signals difficult at present. In this paper, we describe a method that performs drastic reduction in the complexity of time–frequency representations through a modelling of the maps by elementary functions. The method is validated on artificial signals and subsequently applied to electrophysiological brain signals (local field potential) recorded from the olfactory bulb of rats while they are trained to recognize odours. From hundreds of experimental recordings, reproducible time–frequency events are detected, and relevant features are extracted, which allow further information processing, such as automatic classification.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neural Networks - Volume 20, Issue 2, March 2007, Pages 194–209
نویسندگان
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