Compression of long-term EEG using power spectral density

Madan, T. N.; Agarwal, Rajeev; Swamy, M.N.S.

doi:10.1109/iembs.2004.1403121

Cited by 17 publications

(7 citation statements)

References 8 publications

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“…It is not useful to visually observe original brainwave patterns. Tarun Mandan proposed using features based on power spectral density as a descriptor of the compression of a long-term EEG in an intensive care setting to obtain the temporal evolution of recurrent patterns [15]. We used power spectrum analysis which classified the signals according to frequency, and shows the magnitude or power of the decomposed frequency.…”

Section: Eeg Signal Analysis Methodsmentioning

confidence: 99%

Emotion recognition using EEG signals with relative power values and Bayesian network

Yang

Sim

2009

Int. J. Control Autom. Syst.

114

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Many researchers use electroencephalograms (EEGs) to study brain activity in the context of seizures, epilepsy, and lie detection. It is desirable to eliminate EEG artifacts to improve signal collection. In this paper, we propose an emotion recognition system for human brain signals using EEG signals. We measure EEG signals relating to emotion, divide them into five frequency ranges on the basis of power spectrum density, and eliminate low frequencies from 0 to 4 Hz to eliminate EEG artifacts. The resulting calculations of the frequency ranges are based on the percentage of the selected range relative to the total range. The calculated values are then compared to standard values from a Bayesian network, calculated from databases. Finally, we show the emotion results as a human face avatar.

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Section: Eeg Signal Analysis Methodsmentioning

confidence: 99%

Emotion recognition using EEG signals with relative power values and Bayesian network

Yang

Sim

2009

Int. J. Control Autom. Syst.

114

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“…• Madan et al, 2004, have presented a method that mainly makes use of the process called Power Spectral Density for the EEG data compression [53].…”

Section: Electroencephalography (Eeg) Data Compressionmentioning

confidence: 99%

Brain Image Compression Techniques

Abo‐Zahhad¹,

Gharieb²,

Ahmed³

et al. 2015

IJETT

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This paper presents a survey of different methods in the prospect of medical and brain image compression. Compressing an image is basically a process of encoding the image to reduce the size of image as a number of bytes for storage and transmission purposes. This should be done while preserving as much as possible the quality of the image. Hence, image compression has been founded necessary in different medical imaging systems, where there are too much number of bytes of the reconstructed images. There are basically two categories of compression techniques; namely lossless and lossy compression techniques. As the name indicates, in the lossless technique the image is encoded without any loss of information.But in the lossy one, it is allowed that some information is missed. The lossy compression techniques are commonly applied to multimedia data such as audio, video, and still images. Several lossless and lossy compression approaches have been applied to medical images. Different compression approaches belonging to the two categories are discussed and Brain images compression techniques are highlighted. Furthermore, quantitative comparisons between different compression methods are given.as well as advantages and disadvantages of each method.

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“…Various methods for lossy or lossless EEG compression have been proposed in the literature. Some of these EEG compression methods are discussed in [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

EEG signal compression based on Classified Signature and Envelope Vector Sets

Gürkan

Güz

Yarman

2007

2007 18th European Conference on Circuit Theory and Design

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SUMMARYIn this paper, a novel method to compress electroencephalogram (EEG) signal is proposed. The proposed method is based on the generation process of the classified signature and envelope vector sets (CSEVS), which employs an effective k-means clustering algorithm. It is assumed that both the transmitter and the receiver units have the same CSEVS. In this work, on a frame basis, EEG signals are modeled by multiplying only three factors called as classified signature vector, classified envelope vector, and gain coefficient (GC), respectively. In other words, every frame of an EEG signal is represented by two indices R and K of CSEVS and the GC. EEG signals are reconstructed frame by frame using these numbers in the receiver unit by employing the CSEVS. The proposed method is evaluated by using some evaluation metrics that are commonly used in this area such as root-mean-square error, percentage root-mean-square difference, and measuring with visual inspection. The performance of the proposed method is also compared with the other methods. It is observed that the proposed method achieves high compression ratios with low-level reconstruction error while preserving diagnostic information in the reconstructed EEG signal.

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Compression of long-term EEG using power spectral density

Cited by 17 publications

References 8 publications

Emotion recognition using EEG signals with relative power values and Bayesian network

Emotion recognition using EEG signals with relative power values and Bayesian network

Brain Image Compression Techniques

EEG signal compression based on Classified Signature and Envelope Vector Sets

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