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Classification of EEG Signals in a Seizure Detection System Using Dual Tree Complex Wavelet Transform and Least Squares Support Vector Machine

Dattaprasad Torse, Veena Desai, Rajashri Khanai
2018 International Journal of Image Graphics and Signal Processing  
This paper proposes a EEG feature extraction technique using Dual Tree Complex Wavelet Transform (DTℂWT) to overcome the problem of shift variance in DWT.  ...  The estimation of improved multi-scale Permutation Entropy (IMPmEn) is done for the level-3 subband of DTℂWT.  ...  Prabhakar Kore Hospital and Medical Research Center, Belagavi, India for providing real time EEG signal database for the conducted experiments.  ... 
doi:10.5815/ijigsp.2018.01.07 fatcat:nagloz62dbb4djozoqy7jizsli

A Parallel Multiscale Filter Bank Convolutional Neural Networks for Motor Imagery EEG Classification

Hao Wu, Yi Niu, Fu Li, Yuchen Li, Boxun Fu, Guangming Shi, Minghao Dong
2019 Frontiers in Neuroscience  
In this study, we propose a parallel multiscale filter bank convolutional neural network (MSFBCNN) for MI classification.  ...  However, designing and training an end-to-end network to fully extract potential features from EEG signals remains a challenge in MI.  ...  For new applications of MI, a demand for robust and more general feature extraction techniques is gradually increasing.  ... 
doi:10.3389/fnins.2019.01275 pmid:31849587 pmcid:PMC6901997 fatcat:yot5qoufcbgh3mahxiswvvccc4

Common Spatial Filter for Improving the Classification of EEG using Artificial Neural Network

Shreyas J, Bhavani D, Udayaprasad P K, Srinidi N N, Dharamendra Chouhan, S M Dilip Kumar
2021 Zenodo  
Common spatial pattern is used in feature extraction for the improvement of the classifier of different subjects and tested with artificial neural network (ANN).  ...  Machine learning in motor imagery, the classifier performance of electro-encephalo-graphy (EEG) data varies for different subjects.  ...  Ugur Halici et al [14] have proposed deep learning methods for the improvement of performance of EEG classification motor tasks brain signals.  ... 
doi:10.5281/zenodo.5805957 fatcat:6fnpncbz25dcbolxufznkx6ghy

Adaptive Error Detection Method for P300-based Spelling Using Riemannian Geometry

Attaullah Sahito, M. Abdul, Jamil Ahmed
2016 International Journal of Advanced Computer Science and Applications  
In second step most relevant features are extracted using xDAWN spatial filtering along with covariance matrices for handling high dimensional data and in final step elastic net classification algorithm  ...  Brain-Computer Interface (BCI) systems have become one of the valuable research area of ML (Machine Learning) and AI based techniques have brought significant change in traditional diagnostic systems of  ...  Different techniques have been investigated in literature to improve EEG signal, spatial filtering one of them. For instance, independent component analysis (ICA) was used [17] to enhance SNR.  ... 
doi:10.14569/ijacsa.2016.071143 fatcat:wow5x6svibexdbpvmarazmvbre

EEG-based Processing and Classification Methodologies for Autism Spectrum Disorder: A Review

Gunavaran Brihadiswaran, Dilantha Haputhanthri, Sahan Gunathilaka, Dulani Meedeniya, Sampath Jayarathna
2019 Journal of Computer Science  
This study explores different techniques and tools used for pre-processing, feature extraction and feature selection techniques, classification models and measures for evaluating the model.  ...  Recent studies have revealed that early intervention is highly effective for improving the condition.  ...  Dilantha Haputhanthri: Carried out the survey for the feature extraction phase and related content. Sahan Gunathilaka: Carried out the survey for classification phase and related content.  ... 
doi:10.3844/jcssp.2019.1161.1183 fatcat:clvsf7hfdbhrhgdjfpmdgcmuny

Signal processing techniques for motor imagery brain computer interface: A review

Swati Aggarwal, Nupur Chugh
2019 Array  
Common Spatial Pattern (CSP) has been potent and is widely used in BCI for extracting features in motor imagery tasks. The classifiers translate these features into device commands.  ...  This paper provides a comprehensive review of dominant feature extraction methods and classification algorithms in brain-computer interface for motor imagery tasks.  ...  Conclusion The paper presented the comprehensive comparison of prominent feature extraction techniques used for EEG based BCI for motor imagery tasks.  ... 
doi:10.1016/j.array.2019.100003 fatcat:tlkzqreshzgfpeusxub3f5h4bq

Improving Accuracy of Emotion Detection using Brain Waves and Adaptive Swarm Intelligence

2020 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
Because of the unnecessary degrees of unwanted signal from EEG recording, a solitary feature alone can't accomplish great execution. Distinct feature is key for automatic feeling identification.  ...  Right now, we present an AI based scheme utilizing various features extricated from EEG recordings.  ...  We are using partial swarm optimization technique for improving the accuracy of the system and due to use of Adaptive filter the noise will be reduce. V.  ... 
doi:10.35940/ijitee.f4163.049620 fatcat:cz5o2lw4fran5ix6rrd7qghpam

High Resolution ISAR Imaging Based on Improved Smoothed L0 Norm Recovery Algorithm

2016 KSII Transactions on Internet and Information Systems  
To analyze the improvements of the system, the changes of feature variations after applying RCSP filters and performance variations between users are also investigated.  ...  (EEG) device that has only 14 pins.  ...  This paper also proposes a preprocessing technique for EEG-based mood classification.  ... 
doi:10.3837/tiis.2016.02.020 fatcat:cbolz4cm2japzfw7ns7ckbhyby

A Proposal to Automate Seizure Detection based on a Comparative Study of EEG Signal Analysis

Hrishikesh Telang, Shreya More, Yatri Modi, Ruhina Karani
2017 International Journal of Computer Applications  
EEG signals are most often used to  ...  People with epilepsy suffer from multiple types of seizures and Electroencephalography is an important clinical tool for diagnosing, monitoring and managing neurological disorders related to epilepsy.  ...  More recently, as a specialised technique for WT, a multivariate empirical wavelet transform was proposed in [13] that builds signal adaptive wavelet based filters.  ... 
doi:10.5120/ijca2017915637 fatcat:tfu4m5v2qfaytaud7gi2lfsclq

Automatic Detection of Epilepsy Using EEG Energy and Frequency Bands

Sameh A. Bellegdi, Samer M. A. Arafat
2017 International Journal of Applied Mathematics Electronics and Computers  
This paper demonstrates the effectiveness of information fusion at the feature vectors level for automatic detection of epilepsy.  ...  Experiments used features ranging from separate EEG frequency band waves to combinations of band waves, in addition to signal energy.  ...  Acknowledgements The authors would like to thank the Department of Information and Computer Science at KFUPM for offering the Dtreg software that made the extensive experimentation of this research possible  ... 
doi:10.18100/ijamec.2017specialissue30468 fatcat:j5vnh6jypjfnzhqum52qwbmpbe

Fuzzy-Based Automatic Epileptic Seizure Detection Framework

Aayesha, Muhammad Bilal Qureshi, Muhammad Afzaal, Muhammad Shuaib Qureshi, Jeonghwan Gwak
2022 Computers Materials & Continua  
It applies a feature selection strategy on extracted features to get more discriminating features that build fuzzy machine learning classifiers for the detection of epileptic seizures.  ...  Some characterizing features of epileptic and non-epileptic EEG signals overlap; therefore, it requires that analysis of signals must be performed from diverse perspectives.  ...  An improved correlation-based feature selection (ICFS) technique was proposed by Mursalin et al.  ... 
doi:10.32604/cmc.2022.020348 fatcat:yqsfnkzy3jeflgijk3hfboc4py

EEG Signal Classification using Fusion of DWT, SWT and PCA Features

2017 International Journal of Recent Trends in Engineering and Research  
Among the noninvasive techniques for probing human brain dynamics, electroencephalography (EEG) provides a direct measure of cortical activity with millisecond temporal resolution.  ...  Human brain contains millions of neurons which are responsible for information flow. We have classified the publically available dataset for testing between normal and epileptic persons.  ...  display the efficiency of their proposed system.Guo et al.[4]applied genetic programming (GP) to achieve automatic feature extraction from unique feature database with for improving the biased performance  ... 
doi:10.23883/ijrter.2017.3315.ser3o fatcat:mq7w6fayhjep5cgj3kcafphr2y

Epileptic Seizure Detection and Experimental Treatment: A Review

Taeho Kim, Phuc Nguyen, Nhat Pham, Nam Bui, Hoang Truong, Sangtae Ha, Tam Vu
2020 Frontiers in Neurology  
Seizures can be detected by monitoring the brain and muscle activities, heart rate, oxygen level, artificial sounds, or visual signatures through EEG, EMG, ECG, motion, or audio/video recording on the  ...  Next, many recent efforts have focused on building a stable setup features representing the presence of seizures to improve the detection accuracy (15) (16) (17) (18) .  ...  The wavelet-based technique is another denoising method that has been proposed for EEG signals.  ... 
doi:10.3389/fneur.2020.00701 pmid:32849189 pmcid:PMC7396638 fatcat:dnnl7tyninc6zkqciwmuwo3esu

Automatic Emotion Recognition Using Temporal Multimodal Deep Learning

Bahareh Nakisa, Mohammad Naim Rastgoo, Andry Rakotonirainy, Frederic Maire, Vinod Chandran
2020 IEEE Access  
ACKNOWLEDGEMENT Frederic Maire acknowledges continued support from the Queensland University of Technology (QUT) through the Centre for Robotics.  ...  Pre-processing techniques such as band-pass filtering (6 𝑡ℎ order Butterworth filtering), Notch filtering and ICA were applied to the EEG signals.  ...  There are two main approaches for feature extraction: handcrafted feature extraction methods and deep learning techniques.  ... 
doi:10.1109/access.2020.3027026 fatcat:brbdfo5xijgb7ewufmpqqwnkim

Common Spatial Pattern Technique with EEG Signals for Diagnosis of Autism and Epilepsy Disorders

Fahd A. Alturki, Majid Aljalal, Akram M. Abdurraqeeb, Khalil AlSharabi, Abdullrahman A. Al-Shamma'a
2021 IEEE Access  
In this study, artifacts were removed from the EEG datasets using Independent Component Analysis and were filtered using a fifth-order band-pass Butterworth filter to remove interference and noise.  ...  However, here the use of entropy, energy, and band power with CSP was proposed to extract features of EEGs.  ...  Currently, diverse research is being conducted in this area to build and improve efficient diagnostic systems.  ... 
doi:10.1109/access.2021.3056619 fatcat:owvc5xrnqzb73ops5ykyyhhuhu
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