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Comparison of EEG based epilepsy diagnosis using neural networks and wavelet transform [article]

Mohammad Reza Yousefi, Saina Golnejad, Melika Mohammad Hosseini
2022 arXiv   pre-print
Finally, the evaluation results indicate a relatively uniform impact factor on the use or non-use of wavelet transform on the improvement of epilepsy data functions, but in the end, it was shown that the  ...  Also, the value of using electroencephalogram signal has been evaluated in two ways: using wavelet transform and non-using wavelet transform.  ...  Minireview of Epilepsy Detection Techniques Based on Electroencephalogram Signals. Frontiers in systems neuroscience, 15, p.44. [2]  ... 
arXiv:2204.04488v1 fatcat:vkdleoqqw5d7vbguvlirhyewfu


Zulkifli Mahmoodin, Wahidah Mansor, Lee Yoot Khuan, Noor Bariah Mohamad, Sariah Amirin
2016 Jurnal Teknologi  
This paper describes the feature extraction of (EEG) signal using Daubechies wavelet transform.  ...  Daubechies provide the wavelet function shape that represent the type of features in an EEG signal well, detecting variations in frequencies that corresponds to activation of areas in relation to activities  ...  The power measurement could be set as one of the input feature for the classification system on the neurofeedback design.  ... 
doi:10.11113/jt.v78.9071 fatcat:cd5b6fqgbne6lgp6u4bdzqk6na

Neural Engineering for Rehabilitation

Han-Jeong Hwang, Do-Won Kim, Janne M. Hahne, Jongsang Son
2017 BioMed Research International  
Min et al. investigated four different classifiers to decode imagined speech based on EEG signals, where five vowels, /a/, /e/, /i/, /o/, and /u/, were tested.  ...  , robotaided neurorehabilitation, relation between the features of olfactory stimuli and electroencephalography (EEG), and diagnosis of autism spectrum disorder based on EEG.  ...  The authors used discrete wavelet transform (DWT) and entropy as features and artificial neural network as a classifier.  ... 
doi:10.1155/2017/9638098 pmid:28540305 pmcid:PMC5429912 fatcat:deovjxvdbfd33f3njifsizk3q4

Brain Computer Interface issues on hand movement

Prasant Kumar Pattnaik, Jay Sarraf
2018 Journal of King Saud University: Computer and Information Sciences  
This paper focuses on the Brain Computer Interface (BCI) application and its issues.  ...  One method of feature extraction is by making use of wavelet transform.  ...  Feature extraction using Discrete Wavelet Transform (DWT) The DWT has been adopted in the feature extraction phase where the multiple wavelets (filtered signals as a input) are sampled discretely with  ... 
doi:10.1016/j.jksuci.2016.09.006 fatcat:qytdjkdc4nhbbenlvzqvwoky2q

Accessing and Processing MEG Signals in Real-Time: Emerging Applications and Enabling Technologies [chapter]

Stephen Foldes, Wei Wang, Jennifer Collinger, Xin Li, Jinyin Zhang, Gustavo Sudre, Anto Bagic, Douglas J.
2011 Magnetoencephalography  
Approaches based on statistical learning .  ...  These discrete decoding methods can be implemented in real-time MEG and are valuable for some neurofeedback applications.  ... 
doi:10.5772/27356 fatcat:pa5rkuozrvfttaddpcxhl4gb7q

Integration of stationary wavelet transform on a dynamic partial reconfiguration for recognition of pre-ictal gamma oscillations

N. Jmail, M. Zaghdoud, A. Hadriche, T. Frikha, C. Ben Amar, C. Bénar
2018 Heliyon  
It would be interesting to apply them in real time for instantaneous monitoring, seizure warning or neurofeedback systems. This requires improving execution time.  ...  Embedded systems are a promising venue for real-time applications in clinical systems for epilepsy.  ...  Interestingly, and contrary to the continuous wavelet transform, the SWT is a reversible technique, as the discrete wavelet transform DWT, which allow to reconstruct the inverse function ISWT (from selected  ... 
doi:10.1016/j.heliyon.2018.e00530 pmid:29560450 pmcid:PMC5857637 fatcat:bqewd3qk5fg4hjpxqsmgn4lyma

A physiological signal processing system for optimal engagement and attention detection

A. Belle, R. Hobson, K. Najarian
2011 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW)  
The system is designed to provide real-time analysis and feedback based on its prediction and classification.  ...  Application of Discrete Wavelet Transform In this study, Discrete Wavelet Transform or DWT is applied to the pre-processed ECG signal. FIGURE 19.  ... 
doi:10.1109/bibmw.2011.6112429 dblp:conf/bibm/BelleHN11 fatcat:jprve3btxnc4jd4rvakyoyelve

BrainKilter: A Real-time EEG Analysis Platform for Neurofeedback Design and Training

Guangying Pei, Guoxin Guo, Duanduan Chen, Ruoshui Yang, Zhongyan Shi, Shujie Wang, Jinpu Zhang, Jinglong Wu, Tianyi Yan
2020 IEEE Access  
Neurofeedback targets self-regularized brain activity to normalized brain function based on brain-computer interface (BCI) technology.  ...  Hence, we present BrainKilter, a real-time electroencephalogram (EEG) analysis platform based on a "4-tier layered model".  ...  The time-frequency analysis, which is based on the wavelet transform, can synchronously provide variations of the EEG signals in both the time and frequency domains [31] .  ... 
doi:10.1109/access.2020.2967903 fatcat:3hy5u6j5znht3brojcat47v3g4

Identification of Brain disorders by Sub-band Decomposition of EEG signals and Measurement of Signal to Noise Ratio

Hadaate Ullah, Shahin Mahmud, Rubana Hoque Chowdhury
2016 Indonesian Journal of Electrical Engineering and Computer Science  
The Discrete Wavelet Transform (DWT) has been used to decompose the EEG signal into Sub-band signal.  ...  <p>In the case of medical science, one of the most restless researches is the identification of abnormalities in brain.  ...  In the pre-processing, the discrete wavelet transform are used to take aside the noises and then the EEG signal are decomposed into five sub-band signals.  ... 
doi:10.11591/ijeecs.v4.i3.pp568-579 fatcat:arndno5f2bcnfid5j5ds5nfske

A Comprehensive Review on Brain-Computer Interface Controlled Movements

Kulsheet Kaur Virdi, Satish Pawar
The EEG or the brain activity can be used in real time to control external devices via a complete BCI system.  ...  For these applications there is need of such machine learning application which can be efficiently applied on these EEG signals.  ...  [7] designed an algorithm for classification of right and left arm movement. Discrete wavelet transform is used for feature extraction.  ... 
doi:10.24113/ijoscience.v5i6.243 fatcat:mnszamfmjfgtvg5b5lbsid7b3m

Signal Processing in fNIRS: A Case for the Removal of Systemic Activity for Single Trial Data

Franziska Klein, Cornelia Kranczioch
2019 Frontiers in Human Neuroscience  
Yet as evidence is lacking on how these approaches perform on independent data, choosing one approach over another can be difficult.  ...  Researchers using functional near infrared spectroscopy (fNIRS) are increasingly aware of the problem that conventional filtering methods do not eliminate systemic noise at frequencies overlapping with  ...  ACKNOWLEDGMENTS We want to thank Ling-Chia Chen for passing over her fNIRS knowledge and helping to plan the original MI neurofeedback study.  ... 
doi:10.3389/fnhum.2019.00331 pmid:31607880 pmcid:PMC6769087 fatcat:bplkcgp36nayxbterjya4uwada

Noninvasive BCIs: Multiway Signal-Processing Array Decompositions

A. Cichocki, Y. Washizawa, T. Rutkowski, H. Bakardjian, Anh-Huy Phan, Seungjin Choi, Hyekyoung Lee, Qibin Zhao, Liqing Zhang, Yuanqing Li
2008 Computer  
As a result, the majority of promising BCI systems to date exploit EEG signals. [1] [2] [3] [4] [5] [6] [7] [8] [9] Raw brain data is rarely of substantial benefit, as its real value depends on data quality  ...  and on signal-processing, machine-learning, and data-mining tools to analyze the data and extract useful information.  ...  These constraints mean that commands must be based on well-characterized mental tasks that produce well-differentiated neuronal activity. 8 An efficient BCI system should have the ability to extract  ... 
doi:10.1109/mc.2008.431 fatcat:auvhydbdcnagdehqhjbmxxgnnm

ADHD as a Specific Cause for Learning Disability [chapter]

Nada Pop-Jordanova
2020 Learning Disabilities [Working Title]  
As a used nonpharmacological therapeutic approach, very positive outcome of neurofeedback treatment of these children is accentuated.  ...  Additionally, Q-EEG recording using Mitsar 19-channel Q-EEG 201 system was performed.  ...  Based on extensive research during the last decade, we now recognize the existence of Q-EEG subtypes in ADHD patients and understand the need of different neurofeedback protocols to correct these abnormalities  ... 
doi:10.5772/intechopen.91272 fatcat:qs7zayfywncghdzpghrruwxbye

Classification of complex emotions using EEG and virtual environment: proof of concept and therapeutic implication [article]

Eleonora De Filippi, Mara Wolter, Bruno Melo, Carlos Julio Tierra Criollo, Tiago Soares Bortolini, Gustavo Deco, Jorge Moll
2020 bioRxiv   pre-print
These results suggest that complex emotions show distinct electrophysiological correlates, which paves the way for future EEG-based, real-time neurofeedback training of complex emotional states.  ...  Spectral features, together with the frontal-alpha asymmetry, were extracted using Complex Morlet Wavelet analysis.  ...  One of the benefits of the CMW convolution over other methods such as Short-Fast Fourier Transform or the Hilbert Transform is the Gaussian-shaped wavelet in the frequency-domain.  ... 
doi:10.1101/2020.07.27.223370 fatcat:v6dhhu6zr5extd7ei7xktkfi6y

Comparing Features for Classification of MEG Responses to Motor Imagery

Hanna-Leena Halme, Lauri Parkkonen, Bin He
2016 PLoS ONE  
system.  ...  The evaluated MI-related features were power spectral density (PSD), Morlet wavelets, short-time Fourier transform (STFT), common spatial patterns (CSP), filter-bank common spatial patterns (FBCSP), spatio-spectral  ...  neurofeedback system.  ... 
doi:10.1371/journal.pone.0168766 pmid:27992574 pmcid:PMC5161474 fatcat:ggnxrdwunnfnxm6pvct3bwnqze
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