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Multiclass Common Spatial Pattern for EEG based Brain Computer Interface with Adaptive Learning Classifier [article]

Hardik Meisheri, Nagraj Ramrao, Suman Mitra
2021 arXiv   pre-print
In Brain Computer Interface (BCI), data generated from Electroencephalogram (EEG) is non-stationary with low signal to noise ratio and contaminated with artifacts.  ...  Common Spatial Pattern (CSP) algorithm has been proved to be effective in BCI for extracting features in motor imagery tasks, but it is prone to overfitting.  ...  Ankit Das for their immense support in this research work.  ... 
arXiv:1802.09046v2 fatcat:uci4vobesbgttk4twkntdxwqoq

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.  ...  Motor Imagery Brain Computer Interface (MI-BCI) provides a non-muscular channel for communication to those who are suffering from neuronal disorders.  ...  record Multiclass Multiuser both feet movement Motor Imagery Brain Computer Interface using Bayesian Network and ANN [22] A Deep Learning 2017 Common Spatial Variance based 2 Left and Right hand Deep  ... 
doi:10.1016/j.array.2019.100003 fatcat:tlkzqreshzgfpeusxub3f5h4bq

Multiclass EEG motor-imagery classification with sub-band common spatial patterns

Javeria Khan, Muhammad Hamza Bhatti, Usman Ghani Khan, Razi Iqbal
2019 EURASIP Journal on Wireless Communications and Networking  
In this study, we aim to improve multiclass classification accuracy for motor imagery movement using sub-band common spatial patterns with sequential feature selection (SBCSP-SBFS) method.  ...  Electroencephalogram (EEG) signal classification plays an important role to facilitate physically impaired patients by providing brain-computer interface (BCI)-controlled devices.  ...  Acknowledgements We would like to acknowledge our subjects who helped us with EEG data acquisition.  ... 
doi:10.1186/s13638-019-1497-y fatcat:qlphflzn6ne5vhizzgsy6eadhe

A Computationally Efficient Multiclass Time-Frequency Common Spatial Pattern Analysis on EEG Motor Imagery [article]

Ce Zhang, Azim Eskandarian
2020 arXiv   pre-print
Common spatial pattern (CSP) is a popular feature extraction method for electroencephalogram (EEG) motor imagery (MI).  ...  The EEG MI data is gathered from the Brain-Computer Interface (BCI) Competition IV. At first, a bandpass filter and a time-frequency analysis are performed for each experiment trial.  ...  Common Spatial Pattern (CSP) Originally, a common spatial pattern algorithm is designed for two-class feature extraction.  ... 
arXiv:2008.11227v1 fatcat:vol64pdy5vc5fhasswnurbkguy

Deep Learning Algorithm for Brain-Computer Interface

Asif Mansoor, Muhammad Waleed Usman, Noreen Jamil, M. Asif Naeem
2020 Scientific Programming  
Some classification algorithms for EEG-based BCI systems are adaptive classifiers, tensor classifiers, transfer learning approach, and deep learning, as well as some miscellaneous techniques.  ...  Currently, the brain-computer interface (BCI) systems provide two types of signals, raw signals and logic state signals. The latter signals are used to turn on/off the devices.  ...  Classification methods Input EEG pattern Features References Adaptive classifiers  ... 
doi:10.1155/2020/5762149 fatcat:qjtytc3oofd5lhqj4jmtwnvgl4

Machine Learning Verdict of EEG Signals in Brain Computer Interface

M. Jeyanthi, C. Velayutham
2018 International Journal of Scientific Research in Computer Science Engineering and Information Technology  
, SVM classifier using BCI dataset.  ...  In this paper, We extracted features using statistical Haralick features from the raw EEG data .  ...  (2018) [18] This paper focused on common spatial pattern algorithm for multiclass EEG classification with a pre-processing step which can improve the generalization of CSP covariance matrices by removing  ... 
doi:10.32628/cseit1838114 fatcat:7jmdd3lylregfca6wwj2khxtiq

Review of Machine Learning Algorithms for Brain Stroke Diagnosis and Prognosis by EEG Analysis [article]

Mohammad-Parsa Hosseini, Cecilia Hemingway, Jerard Madamba, Alexander McKee, Natalie Ploof, Jennifer Schuman, Elliot Voss
2020 arXiv   pre-print
Machine learning, specifically techniques that utilize Brain-Computer Interfaces (BCIs) to help the patient either restore neurologic pathways or effectively communicate with an electronic prosthetic,  ...  The various machine learning techniques and algorithms that are addressed and combined with BCI technology show that the use of BCIs for stroke treatment is a promising and rapidly expanding field.  ...  that were approximately taken 6 months apart. 49 A aching learning model was created using an off-line BCI design that was based on a Filter Bank Common Spatial Pattern (FBCSP) that has the adaptability  ... 
arXiv:2008.08118v1 fatcat:ipt2pr5xdfe7lhn7llo7migycu

Analysis and Classification of Motor Imagery Using Deep Neural Network

Isah Salim Ahmad, Shuai Zhang, Sani Saminu, Isselmou Abd El Kader, Jamil Maaruf Musa, Imran Javid, Souha Kamhi, Ummay Kulsum
2021 Journal of Applied Materials and Technology  
In this article, a deep neural network (DNN) is proposed for classification of left and right movement of EEG signal using Common Spatial Pattern (CSP) as feature extraction with standard gradient descent  ...  (GD) with momentum and adaptive learning rate LR.  ...  A framework for overcoming EEG uncertainties in real-time multiclass MI BCI was proposed, the multiclass extension of the common spatial pa ern (CSP) was used for artifact rejection and joint approximate  ... 
doi:10.31258/jamt.2.2.85-93 fatcat:oejwrzjj5zhg5a5rxm5kcm2hmy

Evaluating deep learning EEG-based mental stress classification in adolescents with autism for breathing entrainment BCI

Avirath Sundaresan, Brian Penchina, Sean Cheong, Victoria Grace, Antoni Valero-Cabré, Adrien Martel
2021 Brain Informatics  
We trained a total of eleven subject-dependent models-four with conventional brain-computer interface (BCI) methods and seven with deep learning approaches-on the EEG of neurotypical (n=5) and ASD (n=8  ...  Our results show that a multiclass two-layer LSTM RNN deep learning classifier is capable of identifying mental stress from ongoing EEG with an overall accuracy of 93.27%.  ...  Acknowledgements We would like to thank The Nueva School, Learning Farm Educational Resources, OpenBCI, and Muvik Labs for their kind and invaluable support.  ... 
doi:10.1186/s40708-021-00133-5 pmid:34255197 fatcat:seicln3w2rdavijacrrlrkh63q

Multiclass Informative Instance Transfer Learning Framework for Motor Imagery-Based Brain-Computer Interface

Ibrahim Hossain, Abbas Khosravi, Imali Hettiarachchi, Saeid Nahavandi
2018 Computational Intelligence and Neuroscience  
A widely discussed paradigm for brain-computer interface (BCI) is the motor imagery task using noninvasive electroencephalography (EEG) modality.  ...  Then, active learning (AL) driven informative instance transfer learning has been attempted for multiclass BCI.  ...  Introduction Brain-computer interface (BCI) is a system that establishes a communication channel between the brain and control devices without using the neuromuscular system of human body [1] .  ... 
doi:10.1155/2018/6323414 pmid:29681924 pmcid:PMC5842743 fatcat:rrp6erufxjcxzdu7onwqugppay

Data Space Adaptation for Multiclass Motor Imagery-based BCI

Joshua Giles, Kai Keng Ang, Lyudmila Mihaylova, Mahnaz Arvaneh
2018 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)  
Various adaptation techniques have been proposed to address the non-stationarity issue faced by electroencephalogram (EEG)-based brain-computer interfaces (BCIs).  ...  Hence the results showed the effectiveness of the proposed MDSA algorithm in addressing non-stationarity issue for multiclass EEG-based BCI.  ...  INTRODUCTION EEG-based brain-computer interfaces (BCIs) are systems which use the electrical signals generated from the user's brain to allow communication directly between the brain and a computer interface  ... 
doi:10.1109/embc.2018.8512643 pmid:30440793 fatcat:q55wuay4xzgwtnxsviqqtbufli

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.  ...  Multi-emotions may be classified using the developed model, which is based on brain EEG signals.  ... 
doi:10.5281/zenodo.5805957 fatcat:6fnpncbz25dcbolxufznkx6ghy

Brain-Controlled Biometric Signals Employed to Operate External Technical Devices [chapter]

Vasily I. Mironov, Sergey A. Lobov, Innokentiy A. Kastalskiy, Susanna Y. Gordleeva, Alexey S. Pimashkin, Nadezhda P. Krilova, Kseniya V. Volkova, Alexey E. Ossadtchi, Victor B. Kazantsev
2017 Proceedings of the Scientific-Practical Conference "Research and Development - 2016"  
Our interface solution based on a combination of signals of different modalities is capable to provide composite command and proportional multisite control of different technical devices with theoretically  ...  Such signals include multiple electrode electroencephalographic (EEG) signals, electromyography (EMG) signals reflecting muscle contraction pattern, geometrical pattern of body limb kinematics, and other  ...  The first one was based on common spatial patterns (CSP) and the second one on spatio-spectral decomposition (SSD) [18] .  ... 
doi:10.1007/978-3-319-62870-7_7 fatcat:jauxvhver5cornzh7byklinayy

Brainwave Classification of Task Performed by Stroke Patients using ANN

S.K. Narudin, N.H.M. Nasir, N. Fuad
2021 Annals of Emerging Technologies in Computing  
The data set of each subject is used to classify the state of the subject during electroencephalogram (EEG) recording.  ...  In this research, 14 stroke patient's brainwave activity with open eyes (OE) and close eyes (CE) sessions are used.  ...  Acknowledgment The research is financially sponsored by UTHM for TIER 1 Grant (H273) and GPPS (H407).  ... 
doi:10.33166/aetic.2021.05.004 fatcat:ysim576e55h37nfsfhlueswvp4

Signal Processing and Classification Approaches for Brain-Computer Interface [chapter]

Tarik Al-ani, Dalila Tr
2010 Intelligent and Biosensors  
Hoffman and the EPFL-Brain-Computer team for the data and the software given in (Hoffman et al., 2008) that they were used in this work. The authors would like also to thank Dr. A.  ...  Classication of EEG signals from four subjects during five mental tasks. In Solving Engineering Problems with Neural Networks. References  ...  Spatial filters: (SL) and (CAR) Common spatial patterns (CSP) As described by , the CSP method uses the covariance to design common spatial patterns and is based on the simultaneous diagonalisation of  ... 
doi:10.5772/7032 fatcat:jusb6fypyncytbn4d6bdvdk2xe
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