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A Critical Analysis on EEG signal processing for BCI applications

A Critical Analysis on EEG Signal Processing for BCI Applica...

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Innovations in High Speed Communication and signal processing (IHCSP), International conference on

作者： Shashikant P. Patole Mamta Patankar Vijayshri Chaurasia Madhu Shandilya Rajesh Mahadeva Praveen Sharma Department of Physics Khalifa University of Science and Technology Abu Dhabi UAE Department of Electronics & Communication Engineering Maulana Azad National Institute of Technology Bhopal India Department of CSE IES Institute of Technology and Management IES University Bhopal India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

An overview of the brain-computer interface is given in this publication and the variety of deep learning architectures for the acquisition of brain signals we have discussed the EEG signals that are used in BCI applications. Time-frequency localization is frequently poor in EEG signals. As a result, BCI systems frequently have low task detection accuracy and high error rates. It also exhibits extremely quasi qualities and has a lot of trans variances. Additionally, we have discussed noise removal techniques given by different researchers from EEG signals and BCI applications, and the comparison is given further than we have discussed regarding the EEG signal's image retrieval and different deep learning techniques for EEG learning commons. From the analysis of the results of different approaches, it’s been noticed that non-stationary EEG signals are more contributing to BCI applications. Whereas, in pre-processing steps, SWT-ICA, DWT, and CSP algorithms are most efficient for noise removal. For feature extraction sliding window spatial and temporal methods and deep learning methods contributed the most. Finally, for feature learning and classification, transfer learning and fine-tuned model performance were analyzed. It was observed from the analytical review that the fine-tuned transfer learning model outperforms better.

关键词： Deep learning Representation learning Reviews Transfer learning Noise Brain modeling Feature extraction Electroencephalography Brain-computer interfaces Discrete wavelet transforms

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TCIE-ResNeXtGAN: infrared and water vapor satellite image fusion model using dual discriminator generative adversarial networks and ResNeXt for improving tropical cyclone intensity estimation accuracy

TCIE-ResNeXtGAN: infrared and water vapor satellite image fu...

引用

2022 conference on Infrared, Millimeter, Terahertz Waves and applications, IMT 2022

作者： Guo, Jia-Xu Zhang, Chang-Jiang College of Physics and Electronic Information Engineering Zhejiang Normal University Jinhua321004 China Taizhou University Taizhou318000 China

ISBN: (纸本)9781510662476

Different characteristics of satellite images are reflected in different channels, so the monitoring and early warning of meteorological disasters based on satellite image data of a single channel may not achieve satisfactory results. The infrared channel satellite image reflects the ground and cloud top infrared radiation or the temperature distribution. The water vapor channel satellite image reflects the spatial distribution of water vapor in the upper atmosphere. The two channel satellite images reflect atmospheric characteristics from different wavebands. This paper proposes an infrared and water vapor channel satellite image fusion model (TCIE-ResNeXtGAN) based on dual discriminator generative adversarial network and ResNeXt to improve the accuracy of tropical cyclone (Tropical Cyclone, TC) intensity estimation. For this reason, we define the factors affecting TC intensity estimation, namely the brightness temperature gradient in satellite images, based on our previous work, and introduce it to the loss function in the proposed deep learning model to guide the training process of the model. In this way, the purpose of improving the estimation accuracy of TC intensity using fused satellite images is achieved. To demonstrate the effectiveness of the fusion results in this paper, we compare performance between the existing three models and our model. The experimental results show that the proposed image fusion method in this paper can preserve the infrared and water vapor dual-channel information to the greatest extent, while improving the estimation accuracy of TC intensity using fused satellite image. © 2023 SPIE.

关键词： Generative adversarial networks

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A multi-scale energy detector for anomaly detection in dynamic networks

A multi-scale energy detector for anomaly detection in dynam...

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Asilomar conference on signals, Systems & Computers

作者： Arash Golibagh Mahyari Selin Aviyente Department of Electrical and Computer Engineering Michigan State University East Lansing MI USA

ISBN: (纸本)9781479923915

Complex networks have attracted a lot of attention for representing relational data, where the weights of the edges, where edges' weights show the strength of relationships. Complex networks have found numerous applications in social and biological sciences. Studying the topology of these networks is important for a better understanding of the underlying systems and data. Currently, most of the network analysis tools are limited to static networks. However, most networks of interest have edges or relationships that vary across time. Therefore, there is a need to develop methods for the study of these dynamic networks. In this paper, we introduce another aspect of threat detection, which is identifying abrupt changes in edges' weights over time. wavelet decomposition method is used to separate the transient activity from the stationary activity in the edges. A hypothesis testing is proposed for the wavelet coefficients to detect any anomalous edges. Finally, the time points where the anomalous activity occurs are identified through the ratio of the energy of the anomalous to normal edges.

关键词： image edge detection wavelet packets Testing Media Data mining signal processing Social network services

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An FPGA Based Facial Component Extractor using wavelet-Based Salient Points

An FPGA Based Facial Component Extractor using Wavelet-Based...

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International conference on Information and Communication Technologies: From Theory to applications, ICTTA

作者： I. Hosseini M. Rahmaty M.S. Sadri N. Shams R.A. Fattahi ECE Department I.U.T. Iran

Facial component extraction is an active area of research and numerous methods are available for it. Some of these methods are slightly inaccurate because of vast dynamic characteristics of human face and its shape variations. However, these methods are widely used in real-time applications due to their low computational cost. In this paper we propose a new method that uses wavelet decomposition to extract salient points of the given image. Statistical analysis of the obtained salient points can exactly locate facial components. The proposed method tries to achieve computational efficiency and accuracy simultaneously. Simulation results present a detection rate of 95.6% for facial components. A complete FPGA implementation of the system is also proposed. It is shown that the logic resource usage for entire system is so low that the entire system can fit into a 300K gates Sparatan-IIE FPGA. The designed hardware is capable of extracting facial components for up to 5 frames per second

关键词： Field programmable gate arrays Principal component analysis Face detection Discrete wavelet transforms Computational efficiency Statistical analysis Hardware Face recognition Pattern recognition signal processing algorithms

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Overview of Artifacts Detection and Elimination Methods for BCI Using EEG

Overview of Artifacts Detection and Elimination Methods for ...

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International conference on image, Vision and Computing (ICIVC)

作者： Clifford Maswanganyi Chunling Tu Pius Owolawi Shengzhi Du Department of Computer Systems Engineering Tshwane University of Technology Pretoria Gauteng RSA Department of Electrical Engineering Tshwane University of Technology Pretoria Gauteng RSA

ISBN: (纸本)9781538649923

Electroencephalogram (EEG) is the most commonly utilized method of mapping brain wave patterns from the scalp. It is a process of extracting electrical activities of the brain in the form of EEG signals using non-invasive methods. In EEG based techniques, a challenge exists in the form of signal contamination caused by internal and external factors in the recorded EEG signals., such as the artifacts, interferences among multiple EEG sources, and noise. signal pre-processing is the most significant phase for EEG based applications, for instance, the brain computer interface (BCI) systems. This is because the quality of data acquired from the brain through EEG measurements is dependent on the effectiveness of artifact detection and removal methods. During EEG recording there are several factors that influence the quality of data. One of the factors of signal contamination occurs as the result of physiological and non-physiological artifacts. To detect and remove such contaminations from EEG signals, there are several efficient artifact detection and elimination techniques based on certain pre-processing methods. The main objective of this study is to demonstrate various types of artifacts in EEG recordings as results of external and internal factors. Furthermore, the study will also present an extensive review of existing artifacts identification and elimination methods applied on EEG signals.

关键词： Electroencephalography Physiology Discrete wavelet transforms Electrodes Contamination Electric potential

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An efficient VLSI architecture for fingerprint recognition using O2D-DWT architecture and modified CORDIC-FFT

An efficient VLSI architecture for fingerprint recognition u...

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IEEE International conference on signal processing, Informatics, Communication and Energy Systems (SPICES)

作者： Satish S Bhairannawar Sayantam Sarkar K B Raja K R Venugopal Department of Electronics and Communication Engineering Dayananda Sagar College of Engineering Bangalore India Department of Electronics and Communication Engineering UVCE Banglore India Visvesvaraya College of Engineering Bangalore University Bangalore India

The real time biometric systems are used to authenticate persons for various applications. In this paper, we propose an efficient VLSI architecture for fingerprint recognition using O2D-DWT architecture and modified CORDIC-FFT. The O2D-DWT architecture is designed using only adders and shifters for high speed operation and is applied on fingerprint image to generate four sub-bands. The optimized Fast Fourier Transform (OFFT) architecture is designed by computing different twiddle factor angles using modified CORDIC processor and is applied on LL sub-band coefficients to generate final fingerprint features. The test fingerprint features are compared with features of fingerprint images in database using Euclidean Distance. It is observed that the performance of proposed architecture is better compared to existing architectures.

关键词： Computer architecture Fingerprint recognition Discrete wavelet transforms Finite impulse response filters Databases Very large scale integration

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Digital image watermarking using uncorrelated color space

Digital image watermarking using uncorrelated color space

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International conference on Computer applications and Industrial Electronics (ICCAIE)

作者： Manish Gupta Rajeev Gupta Girish Parmar Mukesh Saraswat University College of Engineering Rajasthan Technical University Kota India University College of Engineering Rajasthan Technical University Sambalpur Odisha IN Motilal Nehru National Institute of Technology Allahabad India

The exponential growths in electronic data over internet have increase the demand of a robust and high quality watermarking method for authentication and copyright protection. The available digital image watermarking methods generally use the correlated color spaces which impose the limitations to researchers for using only one color component at a time for embedding the watermark. Therefore, in this paper, a novel DWT based digital image watermarking method has been proposed which embeds the gray scale watermark into color host image using uncorrelated color space (UCS) and genetic algorithm (GA) method. The result shows that the proposed method outperforms other existing methods against the various signal processing attacks. The proposed method is a first inline report which uses uncorrelated color space for digital image watermarking.

关键词： Watermarking image color analysis Robustness Discrete wavelet transforms Digital images Educational institutions Color

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Projected Improved Fista And Application To image Deblurring

Projected Improved Fista And Application To Image Deblurring

引用

IEEE International conference on image processing

作者： Praveen Kumar Pokala Chandra Sekhar Seelamantula Department of Electrical Engineering Indian Institute of Science Bangalore India

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

The analysis-sparse model has been shown to be efficient as compared to the synthesis-sparse model when the sparsifying transform is redundant particularly for image restoration applications. We pose the image deblurring problem as an optimization problem based on the analysis-sparse model considering the sparsifying basis to be a tight frame, more specifically, the shift-invariant discrete wavelet transform (SIDWT). We propose two algorithms, namely, projected improved fast iterative soft-thresholding algorithm (piFISTA) and projected improved fast iterative soft-thresholding algorithm beyond Nesterov's momentum (piFISTA-BN). The proposed algorithms are the analysis counterparts of the improved fast iterative soft-thresholding algorithm (iFISTA) and improved fast iterative soft-thresholding algorithm beyond Nesterov's momentum (iFISTA-BN), respectively, both of which consider the synthesis-sparse model. We demonstrate that piFISTA and piFISTA-BN significantly outperform FISTA, pFISTA, iFISTA, and iFISTA-BN considering standard objective metrics such as peak signal-to-noise ratio (PSNR) and structural similarity index metric (SSIM). Further, we demonstrate empirically that the proposed algorithms converge faster than the state-of-the-art techniques.

关键词： Analytical models image restoration Transforms image reconstruction Iterative algorithms Convergence Standards

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Face Detection using DT-CWT on SHPCA Space

Face Detection using DT-CWT on SHPCA Space

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International conference on Intelligent Systems Design and applications (ISDA)

作者： Yuehui Sun Minghui Du College of Electronic and Information Engineering South China University of Technology Guangzhou China

A novel face detection algorithm is presented by applying dual tree complex wavelets transform (DT-CWT) on spectral histogram PCA space (SHPCA) and support vector machine (SVM). DT-CWT is a transform recently studied, which provides good directional selectivity in six different fixed orientations at different scales. It has limited redundancy for images and is much faster than Gabor transform to compute. Hence, DT-CWT is a good choice to replace Gabor transform in some image signals processing fields especially for face images representation. In the face detection algorithm presented in this paper, images are first projected to SHPCA space after convolved with different filters including DT-CWT filters to achieve features subtraction based on frequency. Then on SHPCA space SVM classification is applied to detect whether faces exist in images or not. The experimental results show that DT-CWT performs much better than Gabor transform on SHPCA space. Furthermore, during preliminary experiments, SVM based on SHPCA space has been trained on a training set of 4000 faces aligned and 6000 non-face images, and a robust classifying function for face and non-face pattern is obtained, which gives the satisfying performance. Several questions about computation time saving and performance improvement are discussed

关键词： Face detection Support vector machines Gabor filters Support vector machine classification wavelet transforms Histograms Principal component analysis signal processing algorithms image representation Frequency

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image Segmentation Based Hybrid Watermarking Algorithm for Copyright Protection

Image Segmentation Based Hybrid Watermarking Algorithm for C...

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International conference on Computing and Networking Technology (ICCNT)

作者： Y. Bhavani Sai Srikar Puppala Spoorthy Shivani Pabba Kavya Sri Kasarla Kadipaka Anvitha Kakatiya Institute of Technology & Science Warangal India

ISBN: (数字)9781728168517

ISBN: (纸本)9781728168524

Today, most of the information shared across the globe is in multimedia form like audio, video and image. With increased globalization, any type of such information can be broadcasted throughout the world easily and is accessible to multiple users for varied applications. This may lead to fraudulent claiming of data ownership which makes copyright protection a major priority in today's world. Digital Watermarking serves as a prime solution to copyright protection where we embed watermark into digital image which serves as an authenticator. In this paper, we proposed a hybrid algorithm using image segmentation, SVD (Singular value decomposition), DWT (Discrete wavelet transformation), Homomorphic filtering and RSA encryption technique that helps to overcome various threats. Here, we proposed a new approach of digital watermarking by embedding two segmented watermarks which are obtained using two distinct threshold values on the original watermark. These threshold values are sent as secret keys to the receiver using Diffie-Hellman key exchange algorithm and can be used at the time of copyright verification. This approach of hybrid technique is being tested by applying Gaussian Noise and it gave the best results when compared with the other digital watermarking techniques.

关键词： Watermarking Discrete wavelet transforms image segmentation signal processing algorithms Discrete cosine transforms Hafnium Copyright protection

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