| Project/Area Number |
17560381
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Measurement engineering
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| Research Institution | Kinki University |
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Principal Investigator |
YOSHIDA Hisashi Kinki University, School of Biology-Oriented Science and Technology, Associate Professor (50278735)
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| Project Period (FY) |
2005 – 2007
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| Project Status |
Completed (Fiscal Year 2007)
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| Budget Amount *help |
¥2,950,000 (Direct Cost: ¥2,800,000、Indirect Cost: ¥150,000)
Fiscal Year 2007: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2006: ¥1,000,000 (Direct Cost: ¥1,000,000)
Fiscal Year 2005: ¥1,300,000 (Direct Cost: ¥1,300,000)
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| Keywords | Equivalent Bandwidths / Stochastic Processes / Renyi Divergence / Positive Time-Frequency Distributions / Copula Theory / EEG Analysis / Power Spectral Estimation / Time and Frequency Marginal Estimation / 時間周辺分布推定 / 定常確率過程 / 非定常確率過程 / ダイバージェンス / Spectral Flatness Measure / 生体信号 |
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| Research Abstract |
The objectives of the project are :
1. To present a new class of generalized equivalent bandwidths▲(EBWs) of stationary random signals by using Renyi divergence including a spectral flatness measure,
2. To define a new class of instantaneous equivalent bandwidths▲(IEBWs) for non-stationary random signals and obtain its estimation method,
3. Application of the proposed methods, i.e. EBWs and IEBWs, to bio-signals.
The results of the project are listed as follows.
1. We have obtained a unified presentation of EBWs of random signals including spectral flatness measure which are well known in the field of speech processing introducing Renyi divergence
2. We have proposed a new class of IEBWs for non-stationary random signals. IEBWs are defined on a positive time-frequency distribution based on Copula theory which is developed in statistics.
3. We have developed the method for estimating frequency and time marginal of time-frequency distributions of non-stationary random signals.
4. We have applied to the IEBW to EEG analysis. As a result, the proposed method can track the change of the bandwidth of the EEG signals in the periods between wakefulness state and wakefulness maintenance state against sleepiness. The EEG signal in the period of wakefulness maintenance state against sleepiness has wide bandwidth since it has high frequency components. The result is very interesting to understand a relationship between the prefrontal cortex which is a main area of activity and hypothalamus which controls sleep and wakefulness.
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