Статті в журналах: "EEG DENOISING"

1

An, Yang, Hak Keung Lam, and Sai Ho Ling. "Auto-Denoising for EEG Signals Using Generative Adversarial Network." Sensors 22, no. 5 (February 23, 2022): 1750. http://dx.doi.org/10.3390/s22051750.

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The brain–computer interface (BCI) has many applications in various fields. In EEG-based research, an essential step is signal denoising. In this paper, a generative adversarial network (GAN)-based denoising method is proposed to denoise the multichannel EEG signal automatically. A new loss function is defined to ensure that the filtered signal can retain as much effective original information and energy as possible. This model can imitate and integrate artificial denoising methods, which reduces processing time; hence it can be used for a large amount of data processing. Compared to other neural network denoising models, the proposed model has one more discriminator, which always judges whether the noise is filtered out. The generator is constantly changing the denoising way. To ensure the GAN model generates EEG signals stably, a new normalization method called sample entropy threshold and energy threshold-based (SETET) normalization is proposed to check the abnormal signals and limit the range of EEG signals. After the denoising system is established, although the denoising model uses the different subjects’ data for training, it can still apply to the new subjects’ data denoising. The experiments discussed in this paper employ the HaLT public dataset. Correlation and root mean square error (RMSE) are used as evaluation criteria. Results reveal that the proposed automatic GAN denoising network achieves the same performance as the manual hybrid artificial denoising method. Moreover, the GAN network makes the denoising process automatic, representing a significant reduction in time.

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Elsherbieny, Zeinab, Nagy Messiha, Adel S. El-Fisawy, Mohamed Rihan, and Fathi E. Abd El-Samie. "Efficient Denoising Schemes of EEG Signals." Menoufia Journal of Electronic Engineering Research 28, no. 1 (December 1, 2019): 209–13. http://dx.doi.org/10.21608/mjeer.2019.77020.

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3

Grobbelaar, Maximilian, Souvik Phadikar, Ebrahim Ghaderpour, Aaron F. Struck, Nidul Sinha, Rajdeep Ghosh, and Md Zaved Iqubal Ahmed. "A Survey on Denoising Techniques of Electroencephalogram Signals Using Wavelet Transform." Signals 3, no. 3 (August 17, 2022): 577–86. http://dx.doi.org/10.3390/signals3030035.

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Electroencephalogram (EEG) artifacts such as eyeblink, eye movement, and muscle movements widely contaminate the EEG signals. Those unwanted artifacts corrupt the information contained in the EEG signals and degrade the performance of qualitative analysis of clinical applications and as well as EEG-based brain–computer interfaces (BCIs). The applications of wavelet transform in denoising EEG signals are increasing day by day due to its capability of handling non-stationary signals. All the reported wavelet denoising techniques for EEG signals are surveyed in this paper in terms of the quality of noise removal and retrieving important information. In order to evaluate the performance of wavelet denoising techniques for EEG signals and to express the quality of reconstruction, the techniques were evaluated based on the results shown in the respective literature. We also compare certain features in the evaluation of the wavelet denoising techniques, such as the requirement of reference channel, automation, online, and performance on a single channel.

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4

Zhao, Haoyan, and Bin Guo. "EEG Signal Denoising Based on Deep Residual Shrinkage Network." Journal of Physics: Conference Series 2395, no. 1 (December 1, 2022): 012076. http://dx.doi.org/10.1088/1742-6596/2395/1/012076.

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Abstract In recent years, EEG signals are usually denoised by traditional algorithms, which suffer from large amounts of computation, a limited number of channels, or modal aliasing. To achieve better denoising, a deep residual contraction network is proposed to denoise EEG signals. At the same time, on the basis of the original residual shrinkage building units, a new soft threshold module is used to replace the ReLU function to construct a new network model. Through different groups of denoising experiments, the effectiveness of this denoising algorithm is verified.

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5

PERDHANA, HASBIAN FAUZY, and HASBALLAH ZAKARIA. "Pembersihan Artefak EOG dari Sinyal EEG menggunakan Denoising Autoencoder." ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika 10, no. 3 (July 19, 2022): 639. http://dx.doi.org/10.26760/elkomika.v10i3.639.

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ABSTRAKElektroensefalografi (EEG) adalah teknik perekaman yang merekam aktivitas elektrik pada otak menggunakan elektroda yang ditempelkan pada kulit kepala. Artefak elektrookulografi (EOG) adalah salah satu artefak yang kerap muncul pada perekaman EEG dikarenakan pergerakan mata dan menyebabkan sinyal EEG berubah bentuk. Untuk membersihkan EEG, artefak perlu dibuang dengan tetap menjaga informasi penting dari EEG. Pada penelitian ini kami mendeteksi artefak EOG menggunakan Independent Component Analysis (ICA) dan deteksi puncak, dan untuk rekonstruksi sinyal EEG kami menggunakan Denoising Autoencoder (DAE). Pada penelitian ini kami meneliti model DAE apakah dapat merekonstruksi sinyal EEG dari artefak EOG. Metode pendeteksian artefak mendapatkan 85% sensitivitas dan 83% Positive Predictive Value (PPV) pada dataset sekunder dan 82% sensitivitas pada dataset primer. Model DAE dilatih dengan validasi silang 10 lipat dan mendapatkan rerata mean squared error (MSE) 0,007±0,008. Penelitian ini membuktikan kemampuan DAE untuk merekonstruksi sinyal EEG denganmasukan segmen sinyal EEG terkontaminasi artefak EOG.Kata kunci: EEG, Artefak EOG, Denoising Autoencoder ABSTRACTThe Electroencephalography (EEG) is a recording technique to record electrical activity on the brain using electrodes attached to the head scalp. Electrooculography (EOG) is one of the artifacts that are prone to appear on EEG due to eye movement and cause EEG signals to deform. To fix the EEG signal, we need to remove artifacts while conserving EEG information. In this research, we detect EOG artifactual signal using Independent Component Analysis (ICA) and peak detection and used a generative model Denoising Autoencoder (DAE) to reconstruct clean EEG by using EEG artifact-corrupted signal. Our artifact detection method scores 85% sensitivity and 83% Positive Predictive Value on the secondary dataset and 82% sensitivity on the primary dataset. We train the DAE model with 10-fold cross-validation and got 0.007 ± 0.008 Mean Squared Error (MSE). We demonstrated DAE on its ability to generate a clean EEG segment by feeding it contaminated EEG segment.Keywords: EEG, Eye movement artifact, Denoising Autoencoder

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Yan, Wenqiang, Chenghang Du, Yongcheng Wu, Xiaowei Zheng, and Guanghua Xu. "SSVEP-EEG Denoising via Image Filtering Methods." IEEE Transactions on Neural Systems and Rehabilitation Engineering 29 (2021): 1634–43. http://dx.doi.org/10.1109/tnsre.2021.3104825.

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7

UTHAYAKUMAR, R., and D. EASWARAMOORTHY. "MULTIFRACTAL-WAVELET BASED DENOISING IN THE CLASSIFICATION OF HEALTHY AND EPILEPTIC EEG SIGNALS." Fluctuation and Noise Letters 11, no. 04 (December 2012): 1250034. http://dx.doi.org/10.1142/s0219477512500344.

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Identification of abnormality in Electroencephalogram (EEG) signals is the vast area of research in the neuroscience. Especially, the classification of healthy and epileptic subjects through EEG signals is the crucial problem in the biomedical sciences. Denoising of EEG signals is another important task in signal processing. The noises must be corrected or reduced before the subsequent decision analysis. This paper presents a wavelet-based denoising method for the recovery of EEG signal contaminated by nonstationary noises and investigates the recognition of healthy and epileptic EEG signals by using multifractal measures such as Generalized Fractal Dimensions. The multifractal measures show the significant differences among normal, interictal and epileptic ictal EEGs with denoising by wavelet transform as the pre-processing step. The denoised artifact-free EEG presents a very good improvement in the identification rate of epileptic seizure. The proposed scheme illustrates with high accuracy through the suitable graphical and statistical tools and performs an important role in the epileptic seizure detection.

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8

Zhang, Zhen, Xiaoyan Yu, Xianwei Rong, and Makoto Iwata. "A Novel Multimodule Neural Network for EEG Denoising." IEEE Access 10 (2022): 49528–41. http://dx.doi.org/10.1109/access.2022.3173261.

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9

Turnip, Arjon, and Jasman Pardede. "Artefacts Removal of EEG Signals with Wavelet Denoising." MATEC Web of Conferences 135 (2017): 00058. http://dx.doi.org/10.1051/matecconf/201713500058.

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10

Li, Junhua, Zbigniew Struzik, Liqing Zhang, and Andrzej Cichocki. "Feature learning from incomplete EEG with denoising autoencoder." Neurocomputing 165 (October 2015): 23–31. http://dx.doi.org/10.1016/j.neucom.2014.08.092.

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11

JAYALAXMI, ANEM, and KUMAR G. SATEESH. "DENOISING OF EEG SIGNAL USING FrFT BASED BARLETT WINDOW." i-manager's Journal on Digital Signal Processing 5, no. 1 (2017): 18. http://dx.doi.org/10.26634/jdp.5.1.13528.

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12

Geetha, G., and S. N. Geethalakshmi. "EEG Denoising using SURE thresholding based on Wavelet Transforms." International Journal of Computer Applications 24, no. 6 (June 30, 2011): 29–33. http://dx.doi.org/10.5120/2948-3935.

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13

Chu, Ruibo, Jian Wang, Qian Zhang, and Huanhuan Chen. "An adaptive noise removal method for EEG signals." Journal of Physics: Conference Series 2414, no. 1 (December 1, 2022): 012007. http://dx.doi.org/10.1088/1742-6596/2414/1/012007.

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Abstract EEG signals are the response to the electrophysiological activity of brain nerve cells in the cerebral cortex, but the collected EEG signal generally contains noise. In order to effectively remove the noise and retain useful information, after research and analysis, this paper proposes an improved EEG noise-removing algorithm based on the threshold method where wavelet contraction is used. The improved adaptive threshold selection algorithm makes it possible that the threshold changes with the number of layers of decomposed signal, so it can be applied flexibly in practice. The wavelet transform is used to decompose the EEG signal into multiple layers of high- and low-frequency coefficients. Adaptive threshold processing is then applied to the wavelet coefficients in accordance with the various decomposition levels, and the scaled wavelet coefficients are then reconstituted to produce the denoised EEG signal. Using the Root Mean Square Error (RMSE) and Signal Noise Ratio (SNR) as quantitative measures of the denoising effect Through trials, the improved threshold method, hard and soft threshold method, and adaptive threshold method were contrasted. The experimental findings demonstrate that the wavelet shrinkage-based improved threshold approach has a better denoising effect than the other three threshold methods.

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14

Phadikar, Souvik, Nidul Sinha, Rajdeep Ghosh, and Ebrahim Ghaderpour. "Automatic Muscle Artifacts Identification and Removal from Single-Channel EEG Using Wavelet Transform with Meta-Heuristically Optimized Non-Local Means Filter." Sensors 22, no. 8 (April 12, 2022): 2948. http://dx.doi.org/10.3390/s22082948.

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Electroencephalogram (EEG) signals may get easily contaminated by muscle artifacts, which may lead to wrong interpretation in the brain–computer interface (BCI) system as well as in various medical diagnoses. The main objective of this paper is to remove muscle artifacts without distorting the information contained in the EEG. A novel multi-stage EEG denoising method is proposed for the first time in which wavelet packet decomposition (WPD) is combined with a modified non-local means (NLM) algorithm. At first, the artifact EEG signal is identified through a pre-trained classifier. Next, the identified EEG signal is decomposed into wavelet coefficients and corrected through a modified NLM filter. Finally, the artifact-free EEG is reconstructed from corrected wavelet coefficients through inverse WPD. To optimize the filter parameters, two meta-heuristic algorithms are used in this paper for the first time. The proposed system is first validated on simulated EEG data and then tested on real EEG data. The proposed approach achieved average mutual information (MI) as 2.9684 ± 0.7045 on real EEG data. The result reveals that the proposed system outperforms recently developed denoising techniques with higher average MI, which indicates that the proposed approach is better in terms of quality of reconstruction and is fully automatic.

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15

Sohaib, Muhammad, Ayesha Ghaffar, Jungpil Shin, Md Junayed Hasan, and Muhammad Taseer Suleman. "Automated Analysis of Sleep Study Parameters Using Signal Processing and Artificial Intelligence." International Journal of Environmental Research and Public Health 19, no. 20 (October 14, 2022): 13256. http://dx.doi.org/10.3390/ijerph192013256.

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An automated sleep stage categorization can readily face noise-contaminated EEG recordings, just as other signal processing applications. Therefore, the denoising of the contaminated signals is inevitable to ensure a reliable analysis of the EEG signals. In this research work, an empirical mode decomposition is used in combination with stacked autoencoders to conduct automatic sleep stage classification with reliable analytical performance. Due to the decomposition of the composite signal into several intrinsic mode functions, empirical mode decomposition offers an effective solution for denoising non-stationary signals such as EEG. Preliminary results showed that through these intrinsic modes, a signal with a high signal-to-noise ratio can be obtained, which can be used for further analysis with confidence. Therefore, later, when statistical features were extracted from the denoised signals and were classified using stacked autoencoders, improved results were obtained for Stage 1, Stage 2, Stage 3, Stage 4, and REM stage EEG signals using this combination.

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Nagar, Subham, Ahlad Kumar, and M. N. S. Swamy. "Orthogonal features-based EEG signal denoising using fractionally compressed autoencoder." Signal Processing 188 (November 2021): 108225. http://dx.doi.org/10.1016/j.sigpro.2021.108225.

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17

N., PADMAJA, BHARATHI M., and SUJATHA E. "A GUI based EEG Signal Denoising using Hilbert Huang Transform." i-manager’s Journal on Electronics Engineering 7, no. 1 (2016): 25. http://dx.doi.org/10.26634/jele.7.1.8281.

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Albera, L., A. Kachenoura, P. Comon, A. Karfoul, F. Wendling, L. Senhadji, and I. Merlet. "ICA-Based EEG denoising: a comparative analysis of fifteen methods." Bulletin of the Polish Academy of Sciences: Technical Sciences 60, no. 3 (December 1, 2012): 407–18. http://dx.doi.org/10.2478/v10175-012-0052-3.

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Abstract Independent Component Analysis (ICA) plays an important role in biomedical engineering. Indeed, the complexity of processes involved in biomedicine and the lack of reference signals make this blind approach a powerful tool to extract sources of interest. However, in practice, only few ICA algorithms such as SOBI, (extended) InfoMax and FastICA are used nowadays to process biomedical signals. In this paper we raise the question whether other ICA methods could be better suited in terms of performance and computational complexity. We focus on ElectroEncephaloGraphy (EEG) data denoising, and more particularly on removal of muscle artifacts from interictal epileptiform activity. Assumptions required by ICA are discussed in such a context. Then fifteen ICA algorithms, namely JADE, CoM2, SOBI, SOBIrob, (extended) InfoMax, PICA, two different implementations of FastICA, ERICA, SIMBEC, FOBIUMJAD, TFBSS, ICAR3, FOOBI1 and 4- CANDHAPc are briefly described. Next they are studied in terms of performance and numerical complexity. Quantitative results are obtained on simulated epileptic data generated with a physiologically-plausible model. These results are also illustrated on real epileptic recordings.

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Štastný, Jakub, and Pavel Sovka. "High-Resolution Movement EEG Classification." Computational Intelligence and Neuroscience 2007 (2007): 1–12. http://dx.doi.org/10.1155/2007/54925.

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The aim of the contribution is to analyze possibilities of high-resolution movement classification using human EEG. For this purpose, a database of the EEG recorded during right-thumb and little-finger fast flexion movements of the experimental subjects was created. The statistical analysis of the EEG was done on the subject's basis instead of the commonly used grand averaging. Statistically significant differences between the EEG accompanying movements of both fingers were found, extending the results of other so far published works. The classifier based on hidden Markov models was able to distinguish between movement and resting states (classification score of 94–100%), but it was unable to recognize the type of the movement. This is caused by the large fraction of other (nonmovement related) EEG activities in the recorded signals. A classification method based on advanced EEG signal denoising is being currently developed to overcome this problem.

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20

Liang, Shuang, and Lu Li. "Reconstruction of EEG Signal Based on Compressed Sensing and Wavelet Transform." Applied Mechanics and Materials 734 (February 2015): 617–20. http://dx.doi.org/10.4028/www.scientific.net/amm.734.617.

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This paper, both theoretically and numerically, investigates an effective reconstruction of EEG signal. An optimization model is presented, which unifies different sparse signals. The model is solved by employing the proximal algorithm. Based on the theoretical analysis, the simulation of EEG signal is performed. Sparse representation of EEG signal is got by the technique of wavelet transform and the signal denoising is also obtained. Then, by using compressed sensing, the EEG signal is reconstructed. Our results show that the reconstructed signal is in good agreement with the original signal and retains the leading characteristic.

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21

Lu, Junru, and Na Ni. "Application of Wavelet Transform in The Construction of Short-term Memory EEG Information Transmission Model." International Journal of Education and Humanities 7, no. 3 (March 23, 2023): 149–52. http://dx.doi.org/10.54097/ijeh.v7i3.6356.

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Wavelet transform is an analysis method that combines time, frequency (or scale) domains. It has: (1) Multi-resolution; (2) The relative bandwidth is constant; (3) Proper selection of basic wavelet can make wavelet have the ability to represent the local characteristics of signals in both time and frequency domains, and it is known as "the microscope for analyzing signals". An analysis method of EEG signal based on autoregressive model (ARM) and wavelet transform is proposed, and it is used to eliminate noise interference in EEG signal. Wavelet transform is a multi-resolution time scale analysis method, which can divide the signal into sub-band signals of different frequency bands. According to this characteristic of wavelet transform, the EEG signals obtained by sampling are decomposed and denoised at various scales, and the results of decomposition and denoising at various scales are given. Wavelet transform can effectively remove noise interference from EEG signals. Wavelet transform is a multi resolution time scale analysis method that can divide signals into subband signals of different frequency bands. According to this characteristic of wavelet transform, the sampled EEG signal is decomposed and denoised at various scales, and the decomposition and denoising results at each scale are given. Wavelet transform can effectively remove noise interference from EEG signals.

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22

Li, Min, Wuhong Wang, Zhen Liu, Mingjun Qiu, and Dayi Qu. "Driver Behavior and Intention Recognition Based on Wavelet Denoising and Bayesian Theory." Sustainability 14, no. 11 (June 6, 2022): 6901. http://dx.doi.org/10.3390/su14116901.

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Driver behavior and intention recognition affects traffic safety. Many scholars use the steering wheel angle, distance of the brake pedal, distance of the accelerator pedal, and turn signal as input data to identify driver behaviors and intentions. However, in terms of time, the acquisition of these parameters has a relative delay, which lengthens the identification time. Therefore, this study uses drivers’ EEG (electroencephalograph) data as input parameters to identify driver behaviors and intentions. The key to the driving intention recognition of EEG signals is to reduce their noise. Noise interference has a significant influence on EEG driving intention recognition. To substantially denoise EEG signals, this study selects wavelet transform theory and wavelet packet transform technology, collects the EEG signals during driving, uses the threshold noise reduction method on EEG signals to reduce noise, and achieves noise reduction through wavelet packet reconstruction. After the wavelet packet coefficients of EEG signals are obtained, the energy characteristics of the wavelet packet coefficients are extracted as input to the Bayesian theoretical model for driver behavior and intention recognition. Results show that the maximum recognition rate of the Bayesian theoretical model reaches 82.6%. Early driver behavior and intention recognition has important research significance for traffic safety and sustainable traffic development.

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Pratiwi, Nor Kumalasari Caecar, Rita Magdalena, Yunendah Nur Fuadah, Sofia Saidah, Syamsul Rizal, and Muhamad Rokhmat Isnaini. "Denoising Sinyal EEG dengan Algoritma Recursive Least Square dan Least Mean Square." TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol 5, no. 2 (November 27, 2019): 122–29. http://dx.doi.org/10.15575/telka.v5n2.122-129.

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EEG mengukur fluktuasi tegangan yang dihasilkan dari arus ionik yang beredar sepanjang neuron otak. Dalam pengaturan eksperimental, sinyal EEG sering terkontaminasi dengan berbagai noise akibat gerakan otot dan jantung. Noise dengan magnitudo yang lebih tinggi dari sinyal aslinya akan merusak sinyal EEG dan bisa berakibat fatal dalam analisis diagnosa. Sehingga diperlukan sebuah sistem denoising yang mampu secara maksimal mengurangi noise, tanpa menghilangkan komponen informasi penting dari sinyal EEG. Salah satu algoritma yang dapat digunakan dalam mereduksi noise pada sinyal biomedis adalah RLS dan LMS. Keuntungan utama dari penggunaan adaptif filtering termasuk RLS dan LMS adalah dapat digunakan pada lingkungan non-stasioner. Tujuan penelitian adalah melakukan uji perbandingan performansi filtering RLS dan LMS dalam mereduksi noise pada sinyal EEG. Parameter performansi yang diukur adalah waktu komputasi, MSE, SNR, dan PSNR. Dari hasil pengujian, diperoleh bahwa adaptif filtering dengan RLS dan LMS mampu mereduksi noise pada sinyal EEG dengan baik. Filter LMS memiliki kelebihan pada waktu komputasinya yang singkat, rata-rata waktu komputasi filter LMS selama 0.7 detik, jauh berbeda dengan filter RLS yang membutuhkan waktu sampai dengan 113 detik. Tetapi kehandalan sistem dari sisi MSE, SNR dan PSNR untuk filter LMS masih berada dibawah RLS untuk intensitas noise yang rendah. Besarnya parameter SNR dan PSNR pada filter RLS cenderung lebih stabil pada intesitas noise 10 dB, 20 dB, dan 30 db. Hal berbeda terjadi pada denoising dengan menggunakan filter LMS, terjadi perubahan SNR yang signifikan dari 16.14 dB pada noise 10 dB, 21.09 dB untuk noise sebesar 20 dB, dan 25.81 dB untuk intensitas noise sebesar 30 dB.

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Kumar, B. Krishna. "Estimation of Number of Levels of Scaling the Principal Components in Denoising EEG Signals." Biomedical and Pharmacology Journal 14, no. 1 (March 30, 2021): 425–33. http://dx.doi.org/10.13005/bpj/2142.

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Electroencephalogram (EEG) is basically a standard method for investigating the brain’s electrical action in diverse psychological and pathological states. Investigation of Electroencephalogram (EEG) signal is a tough task due to the occurrence of different artifacts such as Ocular Artifacts (OA) and Electromyogram. By and large EEG signals falls in the range of DC to 60 Hz and amplitude of 1-5 µv. Ocular artifacts do have the similar statistical properties of EEG signals, often interfere with EEG signal, thereby making the analysis of EEG signals more complex[1]. In this research paper, Principal Component Analysis is employed in denoising the EEG signals. This paper explains up to what level the scaling of principal components have to be done. This paper explains the number of levels of scaling the principal components to get the high quality EEG signal. The work has been carried out on different data sets and later estimated the SNR.

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Zhang, Haoming, Mingqi Zhao, Chen Wei, Dante Mantini, Zherui Li, and Quanying Liu. "EEGdenoiseNet: a benchmark dataset for deep learning solutions of EEG denoising." Journal of Neural Engineering 18, no. 5 (October 1, 2021): 056057. http://dx.doi.org/10.1088/1741-2552/ac2bf8.

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Hofmanis, Janis, Olivier Caspary, Valerie Louis-Dorr, Radu Ranta, and Louis Maillard. "Denoising Depth EEG Signals During DBS Using Filtering and Subspace Decomposition." IEEE Transactions on Biomedical Engineering 60, no. 10 (October 2013): 2686–95. http://dx.doi.org/10.1109/tbme.2013.2262212.

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Martinez-Murcia, Francisco J., Andres Ortiz, Juan Manuel Gorriz, Javier Ramirez, Pedro Javier Lopez-Abarejo, Miguel Lopez-Zamora, and Juan Luis Luque. "EEG Connectivity Analysis Using Denoising Autoencoders for the Detection of Dyslexia." International Journal of Neural Systems 30, no. 07 (May 28, 2020): 2050037. http://dx.doi.org/10.1142/s0129065720500379.

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The Temporal Sampling Framework (TSF) theorizes that the characteristic phonological difficulties of dyslexia are caused by an atypical oscillatory sampling at one or more temporal rates. The LEEDUCA study conducted a series of Electroencephalography (EEG) experiments on children listening to amplitude modulated (AM) noise with slow-rythmic prosodic (0.5–1[Formula: see text]Hz), syllabic (4–8[Formula: see text]Hz) or the phoneme (12–40[Formula: see text]Hz) rates, aimed at detecting differences in perception of oscillatory sampling that could be associated with dyslexia. The purpose of this work is to check whether these differences exist and how they are related to children’s performance in different language and cognitive tasks commonly used to detect dyslexia. To this purpose, temporal and spectral inter-channel EEG connectivity was estimated, and a denoising autoencoder (DAE) was trained to learn a low-dimensional representation of the connectivity matrices. This representation was studied via correlation and classification analysis, which revealed ability in detecting dyslexic subjects with an accuracy higher than 0.8, and balanced accuracy around 0.7. Some features of the DAE representation were significantly correlated ([Formula: see text]) with children’s performance in language and cognitive tasks of the phonological hypothesis category such as phonological awareness and rapid symbolic naming, as well as reading efficiency and reading comprehension. Finally, a deeper analysis of the adjacency matrix revealed a reduced bilateral connection between electrodes of the temporal lobe (roughly the primary auditory cortex) in DD subjects, as well as an increased connectivity of the F7 electrode, placed roughly on Broca’s area. These results pave the way for a complementary assessment of dyslexia using more objective methodologies such as EEG.

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Sardouie, Sepideh Hajipour, Laurent Albera, Mohammad Bagher Shamsollahi, and Isabelle Merlet. "An Efficient Jacobi-Like Deflationary ICA Algorithm: Application to EEG Denoising." IEEE Signal Processing Letters 22, no. 8 (August 2015): 1198–202. http://dx.doi.org/10.1109/lsp.2014.2385868.

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29

Xu, Peng, and Dezhong Yao. "A novel method based on realistic head model for EEG denoising." Computer Methods and Programs in Biomedicine 83, no. 2 (August 2006): 104–10. http://dx.doi.org/10.1016/j.cmpb.2006.06.002.

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Balamareeswaran, M., and D. Ebenezer. "Denoising of EEG signals using Discrete Wavelet Transform based Scalar Quantization." Biomedical and Pharmacology Journal 8, no. 1 (June 30, 2015): 399–406. http://dx.doi.org/10.13005/bpj/627.

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31

Al-Qazzaz, Noor Kamal, Alaa A. Aldoori, and A. Buniya. "EEG Neuro-markers to Enhance BCI-based Stroke Patients Rehabilitation." International Journal on Engineering, Science and Technology 5, no. 1 (June 15, 2023): 42–53. http://dx.doi.org/10.46328/ijonest.139.

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Анотація:

Stroke is the second largest cause of death worldwide and one of the most common causes of disability. However, several approaches have been proposed to deal with stroke patient rehabilitation like robotic devices and virtual reality systems, researchers have found that the brain-computer interfaces (BCI) approaches can provide better results. In this study, the electroencephalography (EEG) dataset from post-stroke patients were investigated to identify the effects of the motor imagery (MI)-based BCI therapy by investigating sensorimotor areas using frequency and time-domain features and to select particular methods that help in enhancing the MI-based BCI systems for stroke patients using EEG signal processing. Therefore, to detect the imagined movements that are typically required within conventional rehabilitation therapy with good identification accuracies, the conventional filters and wavelet transform (WT) denoising technique was used in the first stage. Next, attributes from frequency and entropy domains were computed. Finally, support vector machine (SVM) classification techniques were utilized to test the motor imagery (MI)-based BCI rehabilitation. The results demonstrate the capability of the WT denoising technique together with the used features and SVM classifier to discriminate the tested classes of the left hand, right hand and foot MI-based BCI rehabilitation. This study will help medical doctors, clinicians, physicians and technicians to introduce a good rehabilitation program for post-stroke patients.

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32

Kumar, R. Suresh, and P. Manimegalai. "Implementation of Neural Network with ALE for the Removal of Artifacts in EEG Signals." Current Signal Transduction Therapy 15, no. 1 (July 31, 2020): 77–83. http://dx.doi.org/10.2174/1574362414666190613142424.

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Objective: The EEG signal extraction offers an opportunity to improve the quality of life in patients, which has lost to control the ability of their body, with impairment of locomotion. Electroencephalogram (EEG) signal is an important information source for underlying brain processes. Materials and Methods: The signal extraction and denoising technique obtained through timedomain was then processed by Adaptive Line Enhancer (ALE) to extract the signal coefficient and classify the EEG signals based on FF network. The adaptive line enhancer is used to update the coefficient during the runtime with the help of adaptive algorithms (LMS, RLS, Kalman Filter). Results: In this work, the least mean square algorithm was employed to obtain the coefficient update with respect to the corresponding input signal. Finally, Mat lab and verilog HDL language are used to simulate the signals and got the classification accuracy rate of 80%. Conclusion: Experiments show that this method can get high and accurate rate of classification. In this paper, it is proposed that a low-cost use of Field Programmable Gate Arrays (FPGAs) can be used to process EEG signals for extracting and denoising. As a preliminary study, this work shows the implementation of a Neural Network, integrated with ALE for EEG signal processing. The preliminary tests through the proposed architecture for the activation function shows to be reasonable both in terms of precision and in processing speed.

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33

Saavedra, Carolina, Rodrigo Salas, and Laurent Bougrain. "Wavelet-Based Semblance Methods to Enhance the Single-Trial Detection of Event-Related Potentials for a BCI Spelling System." Computational Intelligence and Neuroscience 2019 (August 26, 2019): 1–10. http://dx.doi.org/10.1155/2019/8432953.

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Based on similarity measures in the wavelet domain under a multichannel EEG setting, two new methods are developed for single-trial event-related potential (ERP) detection. The first method, named “multichannel EEG thresholding by similarity” (METS), simultaneously denoises all of the information recorded by the channels. The second approach, named “semblance-based ERP window selection” (SEWS), presents two versions to automatically localize the ERP in time for each subject to reduce the time window to be analysed by removing useless features. We empirically show that when these methods are used independently, they are suitable for ERP denoising and feature extraction. Meanwhile, the combination of both methods obtains better results compared to using them independently. The denoising algorithm was compared with classic thresholding methods based on wavelets and was found to obtain better results, which shows its suitability for ERP processing. The combination of the two algorithms for denoising the signals and selecting the time window has been compared to xDAWN, which is an efficient algorithm to enhance ERPs. We conclude that our wavelet-based semblance method performs better than xDAWN for single-trial detection in the presence of artifacts or noise.

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34

Ding, Bin, Fuxiao Tian, and Li Zhao. "Digital Evaluation Algorithm for Upper Limb Motor Function Rehabilitation Based on Micro Sensor." Journal of Medical Imaging and Health Informatics 11, no. 2 (February 1, 2021): 391–401. http://dx.doi.org/10.1166/jmihi.2021.3278.

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The electroencephalogram (EEG) of the cerebral cortex reflects the upper limb motion control information of the human body. The electro myographic signal (EMG) of the body muscle tissue reflects the response of the upper limb muscle to the brain control. The intersection of two physiological electrical signals has become a new hot field in artificial intelligence, medical rehabilitation and neuroscience. Firstly, starting with the analysis of the power consumption characteristics of the micro-sensor system, by studying the working principle and design scheme of the energy self-capture technology, various energy supply methods of the combined vibration energy harvesting system, the thermoelectric energy harvesting system and the RF energy harvesting system are proposed. Combined upper limb exercise rehabilitation energy is self-capture program. Secondly, the upper limb motor EEG and EMG signal acquisition experiments were designed to preprocess the acquired signals. Based on the wavelet threshold denoising method, an improved threshold algorithm is proposed to remove the noise in the EEG signal and improve the signal-to-noise ratio of the EEG signal. On the basis of the wavelet analysis method, the stratified threshold denoising method is applied to the collected EMG signals for denoising processing and digital evaluation of upper limb motor function rehabilitation. Finally, a digital evaluation method for upper limb motor function rehabilitation combined with wavelet low-frequency coefficients and significant information is proposed. The algorithm combines wavelet transform, motion estimation, and significant information to design a video quality evaluation algorithm. It can be seen from the experimental results that the performance of this algorithm is superior and maintains high consistency with the human motion system.

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35

Sedik, Ahmed, Mohamed Marey, and Hala Mostafa. "WFT-Fati-Dec: Enhanced Fatigue Detection AI System Based on Wavelet Denoising and Fourier Transform." Applied Sciences 13, no. 5 (February 21, 2023): 2785. http://dx.doi.org/10.3390/app13052785.

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As the number of road accidents increases, it is critical to avoid making driving mistakes. Driver fatigue detection is a concern that has prompted researchers to develop numerous algorithms to address this issue. The challenge is to identify the sleepy drivers with accurate and speedy alerts. Several datasets were used to develop fatigue detection algorithms such as electroencephalogram (EEG), electrooculogram (EOG), electrocardiogram (ECG), and electromyogram (EMG) recordings of the driver’s activities e.g., DROZY dataset. This study proposes a fatigue detection system based on Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT) with machine learning and deep learning classifiers. The FFT and DWT are used for feature extraction and noise removal tasks. In addition, the classification task is carried out on the combined EEG, EOG, ECG, and EMG signals using machine learning and deep learning algorithms including 1D Convolutional Neural Networks (1D CNNs), Concatenated CNNs (C-CNNs), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), k-Nearest Neighbor (KNN), Quadrature Data Analysis (QDA), Multi-layer Perceptron (MLP), and Logistic Regression (LR). The proposed methods are validated on two scenarios, multi-class and binary-class classification. The simulation results reveal that the proposed models achieved a high performance for fatigue detection from medical signals, with a detection accuracy of 90% and 96% for multiclass and binary-class scenarios, respectively. The works in the literature achieved a maximum accuracy of 95%. Therefore, the proposed methods outperform similar efforts in terms of detection accuracy.

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36

Ranjan, Rakesh, Bikash Chandra Sahana, and Ashish Kumar Bhandari. "Motion Artifacts Suppression From EEG Signals Using an Adaptive Signal Denoising Method." IEEE Transactions on Instrumentation and Measurement 71 (2022): 1–10. http://dx.doi.org/10.1109/tim.2022.3142037.

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37

An Peng. "Research on The EEG Signal Denoising Method Based on Improved Wavelet Transform." International Journal of Digital Content Technology and its Applications 7, no. 4 (February 28, 2013): 154–63. http://dx.doi.org/10.4156/jdcta.vol7.issue4.20.

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38

Zhang, Shuoyue, Jürgen Hennig, and Pierre LeVan. "Direct modelling of gradient artifacts for EEG-fMRI denoising and motion tracking." Journal of Neural Engineering 16, no. 5 (August 6, 2019): 056010. http://dx.doi.org/10.1088/1741-2552/ab2b21.

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39

Saleh, Majd, Ahmad Karfoul, Amar Kachenoura, Isabelle Merlet, and Laurent Albera. "Efficient Stepsize Selection Strategy for Givens Parametrized ICA Applied to EEG Denoising." IEEE Signal Processing Letters 24, no. 6 (June 2017): 882–86. http://dx.doi.org/10.1109/lsp.2017.2696359.

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40

Alyasseri, Zaid Abdi Alkareem, Ahamad Tajudin Khader, Mohammed Azmi Al-Betar, Ammar Kamal Abasi, and Sharif Naser Makhadmeh. "EEG Signals Denoising Using Optimal Wavelet Transform Hybridized With Efficient Metaheuristic Methods." IEEE Access 8 (2020): 10584–605. http://dx.doi.org/10.1109/access.2019.2962658.

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41

Navarro, X., F. Porée, A. Beuchée, and G. Carrault. "Denoising preterm EEG by signal decomposition and adaptive filtering: A comparative study." Medical Engineering & Physics 37, no. 3 (March 2015): 315–20. http://dx.doi.org/10.1016/j.medengphy.2015.01.006.

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42

Rakibul Mowla, Md, Siew-Cheok Ng, Muhammad S. A. Zilany, and Raveendran Paramesran. "Artifacts-matched blind source separation and wavelet transform for multichannel EEG denoising." Biomedical Signal Processing and Control 22 (September 2015): 111–18. http://dx.doi.org/10.1016/j.bspc.2015.06.009.

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43

Upadhyay, R., P. K. Padhy, and P. K. Kankar. "EEG artifact removal and noise suppression by Discrete Orthonormal S-Transform denoising." Computers & Electrical Engineering 53 (July 2016): 125–42. http://dx.doi.org/10.1016/j.compeleceng.2016.05.015.

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44

Al-Qazzaz, Noor Kamal, Alaa A. Aldoori, Sawal Hamid Bin Mohd Ali, Siti Anom Ahmad, Ahmed Kazem Mohammed, and Mustafa Ibrahim Mohyee. "EEG Signal Complexity Measurements to Enhance BCI-Based Stroke Patients’ Rehabilitation." Sensors 23, no. 8 (April 11, 2023): 3889. http://dx.doi.org/10.3390/s23083889.

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Анотація:

The second leading cause of death and one of the most common causes of disability in the world is stroke. Researchers have found that brain–computer interface (BCI) techniques can result in better stroke patient rehabilitation. This study used the proposed motor imagery (MI) framework to analyze the electroencephalogram (EEG) dataset from eight subjects in order to enhance the MI-based BCI systems for stroke patients. The preprocessing portion of the framework comprises the use of conventional filters and the independent component analysis (ICA) denoising approach. Fractal dimension (FD) and Hurst exponent (Hur) were then calculated as complexity features, and Tsallis entropy (TsEn) and dispersion entropy (DispEn) were assessed as irregularity parameters. The MI-based BCI features were then statistically retrieved from each participant using two-way analysis of variance (ANOVA) to demonstrate the individuals’ performances from four classes (left hand, right hand, foot, and tongue). The dimensionality reduction algorithm, Laplacian Eigenmap (LE), was used to enhance the MI-based BCI classification performance. Utilizing k-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF) classifiers, the groups of post-stroke patients were ultimately determined. The findings show that LE with RF and KNN obtained 74.48% and 73.20% accuracy, respectively; therefore, the integrated set of the proposed features along with ICA denoising technique can exactly describe the proposed MI framework, which may be used to explore the four classes of MI-based BCI rehabilitation. This study will help clinicians, doctors, and technicians make a good rehabilitation program for people who have had a stroke.

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45

Sweeney-Reed, Catherine M., Slawomir J. Nasuto, Marcus F. Vieira, and Adriano O. Andrade. "Empirical Mode Decomposition and its Extensions Applied to EEG Analysis: A Review." Advances in Data Science and Adaptive Analysis 10, no. 02 (April 2018): 1840001. http://dx.doi.org/10.1142/s2424922x18400016.

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Empirical mode decomposition (EMD) provides an adaptive, data-driven approach to time–frequency analysis, yielding components from which local amplitude, phase, and frequency content can be derived. Since its initial introduction to electroencephalographic (EEG) data analysis, EMD has been extended to enable phase synchrony analysis and multivariate data processing. EMD has been integrated into a wide range of applications, with emphasis on denoising and classification. We review the methodological developments, providing an overview of the diverse implementations, ranging from artifact removal to seizure detection and brain–computer interfaces. Finally, we discuss limitations, challenges, and opportunities associated with EMD for EEG analysis.

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46

Jukic, Samed, Muzafer Saracevic, Abdulhamit Subasi, and Jasmin Kevric. "Comparison of Ensemble Machine Learning Methods for Automated Classification of Focal and Non-Focal Epileptic EEG Signals." Mathematics 8, no. 9 (September 2, 2020): 1481. http://dx.doi.org/10.3390/math8091481.

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This research presents the epileptic focus region localization during epileptic seizures by applying different signal processing and ensemble machine learning techniques in intracranial recordings of electroencephalogram (EEG). Multi-scale Principal Component Analysis (MSPCA) is used for denoising EEG signals and the autoregressive (AR) algorithm will extract useful features from the EEG signal. The performances of the ensemble machine learning methods are measured with accuracy, F-measure, and the area under the receiver operating characteristic (ROC) curve (AUC). EEG-based focus area localization with the proposed methods reaches 98.9% accuracy using the Rotation Forest classifier. Therefore, our results suggest that ensemble machine learning methods can be applied to differentiate the EEG signals from epileptogenic brain areas and signals recorded from non-epileptogenic brain regions with high accuracy.

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47

Chaddad, Ahmad, Yihang Wu, Reem Kateb, and Ahmed Bouridane. "Electroencephalography Signal Processing: A Comprehensive Review and Analysis of Methods and Techniques." Sensors 23, no. 14 (July 16, 2023): 6434. http://dx.doi.org/10.3390/s23146434.

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The electroencephalography (EEG) signal is a noninvasive and complex signal that has numerous applications in biomedical fields, including sleep and the brain–computer interface. Given its complexity, researchers have proposed several advanced preprocessing and feature extraction methods to analyze EEG signals. In this study, we analyze a comprehensive review of numerous articles related to EEG signal processing. We searched the major scientific and engineering databases and summarized the results of our findings. Our survey encompassed the entire process of EEG signal processing, from acquisition and pretreatment (denoising) to feature extraction, classification, and application. We present a detailed discussion and comparison of various methods and techniques used for EEG signal processing. Additionally, we identify the current limitations of these techniques and analyze their future development trends. We conclude by offering some suggestions for future research in the field of EEG signal processing.

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48

Dalal, Virupaxi, and Satish Bhairannawar. "Efficient de-noising technique for electroencephalogram signal processing." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 2 (June 1, 2022): 603. http://dx.doi.org/10.11591/ijai.v11.i2.pp603-612.

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An electroencephalogram (EEG) is a recording of various frequencies of electrical activity in the brain. EEG signal is very useful for diagnosis of various brain related diseases at early stage to prevent severe issues which may lead to loss of life. The raw EEG signal captured through the leads contain different type of noises which is not susceptible for diagnosis. In this paper, an efficient algorithm is proposed to process the raw EEG signal to combat the noise. To obtain noiseless EEG data, the likelihood test ratio is applied to interference computation block. The likelihood ratio test converts EEG data signal into segmented data with nearly constant noise characteristics. This will aid in detecting the noise present in a tiny segment which ensures proper signal denoising. The processed signal is compared with the database of noiseless EEG of the same person using principal component analysis (PCA) classifier. The proposed algorithm is 99.01% efficient to identify and combat noise in the EEG signal.

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49

Li, Zhiwei, Jun Li, Yousheng Xia, Pingfa Feng, and Feng Feng. "Variation Trends of Fractal Dimension in Epileptic EEG Signals." Algorithms 14, no. 11 (October 29, 2021): 316. http://dx.doi.org/10.3390/a14110316.

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Epileptic diseases take EEG as an important basis for clinical judgment, and fractal algorithms were often used to analyze electroencephalography (EEG) signals. However, the variation trends of fractal dimension (D) were opposite in the literature, i.e., both D decreasing and increasing were reported in previous studies during seizure status relative to the normal status, undermining the feasibility of fractal algorithms for EEG analysis to detect epileptic seizures. In this study, two algorithms with high accuracy in the D calculation, Higuchi and roughness scaling extraction (RSE), were used to study D variation of EEG signals with seizures. It was found that the denoising operation had an important influence on D variation trend. Moreover, the D variation obtained by RSE algorithm was larger than that by Higuchi algorithm, because the non-fractal nature of EEG signals during normal status could be detected and quantified by RSE algorithm. The above findings in this study could be promising to make more understandings of the nonlinear nature and scaling behaviors of EEG signals.

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Yang, Biao, Jinmeng Cao, Tiantong Zhou, Li Dong, Ling Zou, and Jianbo Xiang. "Exploration of Neural Activity under Cognitive Reappraisal Using Simultaneous EEG-fMRI Data and Kernel Canonical Correlation Analysis." Computational and Mathematical Methods in Medicine 2018 (July 2, 2018): 1–11. http://dx.doi.org/10.1155/2018/3018356.

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Background. Neural activity under cognitive reappraisal can be more accurately investigated using simultaneous EEG- (electroencephalography) fMRI (functional magnetic resonance imaging) than using EEG or fMRI only. Complementary spatiotemporal information can be found from simultaneous EEG-fMRI data to study brain function. Method. An effective EEG-fMRI fusion framework is proposed in this work. EEG-fMRI data is simultaneously sampled on fifteen visually stimulated healthy adult participants. Net-station toolbox and empirical mode decomposition are employed for EEG denoising. Sparse spectral clustering is used to construct fMRI masks that are used to constrain fMRI activated regions. A kernel-based canonical correlation analysis is utilized to fuse nonlinear EEG-fMRI data. Results. The experimental results show a distinct late positive potential (LPP, latency 200-700ms) from the correlated EEG components that are reconstructed from nonlinear EEG-fMRI data. Peak value of LPP under reappraisal state is smaller than that under negative state, however, larger than that under neutral state. For correlated fMRI components, obvious activation can be observed in cerebral regions, e.g., the amygdala, temporal lobe, cingulate gyrus, hippocampus, and frontal lobe. Meanwhile, in these regions, activated intensity under reappraisal state is obviously smaller than that under negative state and larger than that under neutral state. Conclusions. The proposed EEG-fMRI fusion approach provides an effective way to study the neural activities of cognitive reappraisal with high spatiotemporal resolution. It is also suitable for other neuroimaging technologies using simultaneous EEG-fMRI data.

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