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Methodological Note: Recent Advances in Hybrid Brain-Computer Interface Systems: A Technological and Quantitative Review

Sahar Sadeghi, Ali Maleki
2018 Basic and Clinical Neuroscience  
In a Hybrid Brain-Computer Interface (HBCI), a BCI control signal combines with one or more BCI control signals or with Human-Machine Interface (HMI) biosignals to increase classification accuracy, boost  ...  Brain-Computer Interface (BCI) is a system that enables users to transmit commands to the computer using their brain activity recorded by electroencephalography.  ...  Keywords used in search engines were "Hybrid" AND "Brain computer interface", "Hybrid" AND "Brain machine interface", "Brain computer interface" AND "Electroencephalography", "Brain computer interface"  ... 
doi:10.32598/bcn.9.5.373 pmid:30719252 pmcid:PMC6360492 fatcat:efnlvrzvevardbh4ucxw2hjpqe

Back Propagation Classification of EEG for Brain-Computer Interface

Rajashekhar U., Neelappa Anon, Rashmi M. Hullamani, Bhagyamma S., Pavithra P. S., Choodarathnakara A. L.
2021 JNNCE Journal of Engineering and Management  
The diagnosis system presents to classify the Electroencephalogram (EEG) brain signal of patient to distinguish between normal and abnormal which are tumor and epilepsy with better classification accuracy  ...  To design automated classification of EEG signals for the detection of normal and abnormal activities using Wavelet transform and Artificial Neural Network (ANN) Classifier is considered.  ...  Introduction A Brain-Computer interface (BCI) is a one of the communication between human brain and an external device. It is also called neural-control interface, or brain machine interface.  ... 
doi:10.37314/jjem.2020.040208 fatcat:lbn2ayjlgbe6pbockaamclblmm

Hybrid Brain–Computer Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review

Keum-Shik Hong, Muhammad Jawad Khan
2017 Frontiers in Neurorobotics  
In the case of brain signals, motor imagination/movement tasks are combined with cognitive tasks to increase active brain-computer interface (BCI) accuracy.  ...  In this article, non-invasive hybrid brain-computer interface (hBCI) technologies for improving classification accuracy and increasing the number of commands are reviewed.  ...  As a mean of overcoming the problem of the reduction of classification accuracy upon an increase in the number of control commands, the concept of hybrid brain-computer interface (hBCI) was introduced  ... 
doi:10.3389/fnbot.2017.00035 pmid:28790910 pmcid:PMC5522881 fatcat:wcg2mc32mvei3mtvsyropxbzim

Improving brain computer interface performance by data augmentation with conditional Deep Convolutional Generative Adversarial Networks [article]

Qiqi Zhang, Ying Liu
2018 arXiv   pre-print
to improve the performance of convolutional neural networks in brain computer interface field and overcome the small training dataset problems.  ...  The results show that the generated artificial EEG data from Gaussian noise can learn the features from raw EEG data and has no less than the classification accuracy of raw EEG data in the testing dataset  ...  CONCLUSION AND FUTURE WORK With the development of brain computer interface, many algorithms are proposed to improve its performance.  ... 
arXiv:1806.07108v2 fatcat:oewpyzl4lzbaharydnz5subx4m

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

Swati Aggarwal, Nupur Chugh
2019 Array  
Authors discuss existing challenges in the domain of motor imagery brain-computer interface and suggest possible research directions.  ...  This paper provides a comprehensive review of dominant feature extraction methods and classification algorithms in brain-computer interface for motor imagery tasks.  ...  Introduction A Brain Computer Interface (BCI) utilizes signals to establish a connection between a person's state of mind and a computer-based signal processing system, which interprets the signals [1  ... 
doi:10.1016/j.array.2019.100003 fatcat:tlkzqreshzgfpeusxub3f5h4bq

Brain Computer Interface for Neurodegenerative Person Using Electroencephalogram

Li Junwei, S. Ramkumar, G. Emayavaramban, D. Franklin vinod, M. Thilagaraj, V. Muneeswaran, M. Pallikonda Rajasekaran, V. Venkataraman, Ahmed Faeq Hussein
2019 IEEE Access  
The result shows that an overall average classification accuracy of 92.50% and individual tasks with an average classification of 95%, 87.50%, 92.50%, and 95.00% were achieved for the four tasks.  ...  INDEX TERMS Brain computer interface, band power, radial basis function, FRDM-KL25Z.  ...  Han and Im [19] developed real time communication using EEG-based brain control interface system for the patients in completely locked-in state and obtain classification accuracy of 87.5 % for online  ... 
doi:10.1109/access.2018.2886708 fatcat:fl73fiatjvehzm72pmdm6s5umy

Review of Sparse Representation-Based Classification Methods on EEG Signal Processing for Epilepsy Detection, Brain-Computer Interface and Cognitive Impairment

Dong Wen, Peilei Jia, Qiusheng Lian, Yanhong Zhou, Chengbiao Lu
2016 Frontiers in Aging Neuroscience  
SRC methods have been used to analyze the EEG signals of epilepsy, cognitive impairment and brain computer interface (BCI), which made rapid progress including the improvement in computational accuracy  ...  At present, the sparse representation-based classification (SRC) has become an important approach in electroencephalograph (EEG) signal analysis, by which the data is sparsely represented on the basis  ...  ), Alzheimer's disease (AD) and brain computer interface (BCI) .  ... 
doi:10.3389/fnagi.2016.00172 pmid:27458376 pmcid:PMC4937019 fatcat:vn5jpvjzfnejvgmtojqngbtcru

Automatic Exam Evaluation based on Brain Computer Interface

Hameda F. Balat, M.A. El-dosuky, El-Saeed M. Abd El-Razek, Magdi Z. Rashed
2020 International Journal of Computer Applications  
This paper introduces a system based on brain computer interface (BCI) to be used in education to measure intended learning outcomes and measure the impact of noise on the degree of system accuracy.  ...  Brain computer interface applications can be used to overcome learning problems, especially student anxiety, lack of focus, and lack of attention.  ...  Brain research, especially the brain computer interface (BCI), is one of the most active areas of research today that based on Electro-Encephalo-Gram (EEG)-based brain activity observation [1] .  ... 
doi:10.5120/ijca2020920792 fatcat:55cwqgncjbf3libdzyeezpx3wq

IEEE Access Special Section Editorial: Neural Engineering Informatics

Zehong Cao, Peng Xu, Zhiguo Zhang, Gang Wang, Samu Taulu, Leandro Beltrachini
2020 IEEE Access  
empirical wavelet transform (EWT)-based signal decomposition method for improving the classification accuracy of MI-based EEG signals.  ...  The suggested method has the potential of improving the accuracy of brain-computer interfaces aimed at helping severely disabled people.  ... 
doi:10.1109/access.2020.3036265 fatcat:j33u2arqifgvzbn3xn5hgcj7za

Brain Computer Interface for Micro-controller Driven Robot Based on Emotiv Sensors

Parth Gargava, Krishna Asawa
2017 International Journal of Interactive Multimedia and Artificial Intelligence  
A Brain Computer Interface (BCI) is developed to navigate a micro-controller based robot using Emotiv sensors.  ...  The BCI system has a pipeline of 5 stages-signal acquisition, pre-processing, feature extraction, classification and CUDA inter-facing.  ...  Various challenges were faced by us in developing an application based on Brain Computer Interface system such as: • Acquisition of brain signals from the sensor device Emotiv Epoch requires an expert  ... 
doi:10.9781/ijimai.2017.457 fatcat:mmcmghqggvhu5klirwzwvzg2ka

Towards Brain-Computer Interfaces for Drone Swarm Control [article]

Ji-Hoon Jeong, Dae-Hyeok Lee, Hyung-Ju Ahn, Seong-Whan Lee
2020 arXiv   pre-print
Noninvasive brain-computer interface (BCI) decodes brain signals to understand user intention.  ...  Hence, we could confirm the feasibility of the drone swarm control system based on EEG signals for performing high-level tasks.  ...  The authors thanks to B.-H. Kwon for their help with the design of experimental paradigm.  ... 
arXiv:2002.00519v1 fatcat:4dyvhzqqvjgtha2sbh3g5ly7bq

Ergonomic Issues in Brain-Computer Interface Technologies: Current Status, Challenges, and Future Direction

Hyun Jae Baek, Hyun Seok Kim, Minkyu Ahn, Hohyun Cho, Sangtae Ahn
2020 Computational Intelligence and Neuroscience  
Acknowledgments e editors would like to thank all the authors and reviewers who made this special issue available. ey hope that this collection of articles will be useful for the industrialization of BCI  ...  In the article entitled "Comparison of Visual Stimuli for Steady-State Visual Evoked Potential-Based Brain-Computer Interfaces in Virtual Reality Environment in terms of Classification Accuracy and Visual  ...  In the review article entitled "Enhancing the Usability of Brain-Computer Interface Systems," H. J.  ... 
doi:10.1155/2020/4876397 pmid:32089668 pmcid:PMC7029259 fatcat:qor5t7qp6jhgjnjwxif7iv2pae

Bagging of EEG Signals for Brain Computer Interface

K. Akilandeswari, G. M. Nasira
2014 2014 World Congress on Computing and Communication Technologies  
Classification of the features is achieved through Bagging and decision tree classifiers.  ...  This study investigates effects of feature selection for Electroencephalograph (EEG) signals.  ...  The fitness function of the particles is evaluated based on the classification accuracy and the number of features.  ... 
doi:10.1109/wccct.2014.42 fatcat:ra4zarcxanbvbbd5vcy62zm7qi

Multimodal Fuzzy Fusion for Enhancing the Motor-Imagery-Based Brain Computer Interface

Li-Wei Ko, Yi-Chen Lu, Humberto Bustince, Yu-Cheng Chang, Yang Chang, Javier Ferandez, Yu-Kai Wang, Jose Antonio Sanz, Gracaliz Pereira Dimuro, Chin-Teng Lin
2019 IEEE Computational Intelligence Magazine  
To enhance the classification accuracy of brain-computer interfaces, we adopted fuzzy integrals, after employing the classification method of traditional brain-computer interfaces, to consider possible  ...  Subsequently, we proposed a novel classification framework called the multimodal fuzzy fusion-based brain-computer interface system.  ...  INTRODUCTION Brain-computer interfaces (BCIs) are a method of communication between the human brain and an external device [1] .  ... 
doi:10.1109/mci.2018.2881647 fatcat:d3qu6lhzi5ggreydwggi7ofbkq

Guest Editorial: Advanced Machine-Learning Methods for Brain-Machine Interfacing or Brain-Computer Interfacing

Kaijian Xia, Yizhang Jiang, Yudong Zhang, Wen Si
2021 IEEE/ACM Transactions on Computational Biology & Bioinformatics  
Interfacing or Brain-Computer Interfacing.  ...  classification model (called HD-SRC) for EEG signal detection.  ...  In the survey article entitled "EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications,"  ... 
doi:10.1109/tcbb.2021.3078145 fatcat:hojaokclpjhgpcqob5jbwrlktm
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