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EEG based on compressed sensing compressive sampling

Author: ZhouWei
Tutor: WeiZhiHui
School: Nanjing University of Technology and Engineering
Course: Pattern Recognition and Intelligent Systems
Keywords: Compressed sensing Multi-channel EEG Joint Sparse Compressive sampling
CLC: TN911.7
Type: Master's thesis
Year: 2011
Downloads: 281
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In this paper, based on compressive sensing EEG compressive sampling , first studied the single-channel EEG compression based on compressive sensing sampling , and then based on the improvement in the single-channel multi-channel joint compression algorithm sampling . In medical practice , usually a long period of several multi-channel EEG measurement repeatability , it will produce large amounts of data . How to effectively deal with these data is a serious problem . Compressed sensing theory emerged in recent years as an effective solution to this problem proposed a new solution ideas . In view of this , the paper first introduces the basics of EEG signals and compressed sensing priori theoretical framework . The next study based on compressed sensing theory of compressive sampling single-channel EEG signals , including EEG best sparse decomposition , experimental comparison, the EEG signals , a Gaussian function , Gaussian wavelet function, Mexican hat function as the atomic generating function constructed redundant dictionary , you can achieve better EEG signal sparse decomposition effect ; measurement matrix choice experiments comparing the commonly used measurement matrix for reconstruction errors, such as Gaussian random matrices , Toeplitz matrix , etc., next, use the measurement matrix for sparse decomposition coefficients were observed vector values ??are measured compressive sampling completed , and finally by these measurements using an orthogonal matching pursuit algorithm recovers the coefficient vector , and then complete the reconstruction of the original EEG signals . In the single-channel EEG signals compressive sampling , based on the EEG signals of each channel in view of the link between the proposed multi-channel EEG signals joint compressive sampling , saving the sparse decomposition used in atomic number and the number of observations , to achieve a more efficient compression sampling .

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