Biomedical subjects

Ahmed H Tewfik

Publications and source records attributed to Ahmed H Tewfik.

4 recordsLinked to original sources

PubMed2006

Extraction subject-specific motor imagery time-frequency patterns for single trial EEG classification.

We introduce a new adaptive time-frequency plane feature extraction strategy for the segmentation and classification of electroencephalogram (EEG) corresponding to left and right hand motor imagery of a brain-computer interface task. The proposed algorithm adaptively segments the time axis by dividing the EEG data into non-uniform time segments over a dyadic tree. This is followed by grouping the expansion coefficients in the frequency axis in each segment. The most discriminative features are selected from the segmented time-frequency plane and fed to a linear discriminant for classification. The proposed algorithm achieved an average classification accuracy of 84.3% on six subjects by selecting the most discriminant subspaces for each one. For comparison, classification results based on an autoregressive model are also presented where the mean accuracy of the same subjects turned out to be 79.5%. Interestingly the subjects and two hemispheres of each subject are represented by distinct segmentations and features. This indicates that the proposed method can handle inter-subject variability when constructing brain-computer interfaces.

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PubMed2006

Progressive quantized projection approach to data hiding.

A new image data-hiding technique is proposed. The proposed approach modifies blocks of the image after projecting them onto certain directions. By quantizing the projected blocks to even and odd values, one can represent the hidden information properly. The proposed algorithm performs the modification progressively to ensure successful data extraction without the need for the original image at the receiver side. Two techniques are also presented for correcting scaling and rotation attacks. The first approach is an exhaustive search in nature, which is based on a training sequence that is inserted as part of the hidden information. The second approach uses wavelet maxima as image semantics for rotation and scaling estimation. Both algorithms have proved to be effective in correcting rotation and scaling distortion.

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PubMed2006

A novel high-capacity data-embedding system.

In this paper, we present a novel data-embedding system with high embedding capacity. The embedding algorithm is based on the quantized projection embedding method with some enhancement to achieve high embedding rates. In particular, our system uses a random permutation of the columns of a Hadamard matrix as projection vectors and a fixed perceptual mask based on the JPEG default quantization table for the quantization step design. As a result, the data-embedding system achieves 1/167 (1 bit out of 167 raw image bits) to 1/84 hiding ratios with a BER of around 0.1% in the presence of JPEG compression attacks, while maintaining visual distortion at a minimum.

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PubMed2004

Geometric invariance in image watermarking.

Surviving geometric attacks in image watermarking is considered to be of great importance. In this paper, the watermark is used in an authentication context. Two solutions are being proposed for such a problem. Both geometric and invariant moments are used in the proposed techniques. An invariant watermark is designed and tested against attacks performed by StirMark using the invariant moments. On the other hand, an image normalization technique is also proposed which creates a normalized environment for watermark embedding and detection. The proposed algorithms have the advantage of being robust, computationally efficient, and no overhead needs to be transmitted to the decoder side. The proposed techniques have proven to be highly robust to all geometric manipulations, filtering, compression and slight cropping which are performed as part of StirMark attacks as well as noise addition, both Gaussian and salt & pepper.

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