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Towards Adaptive Classification using Riemannian Geometry approaches in Brain-Computer Interfaces

Satyam Kumar, Florian Yger, Fabien Lotte
2019 2019 7th International Winter Conference on Brain-Computer Interface (BCI)  
Also, the improvement in average classification accuracy for a 3-class mental imagery BCI with hybrid adaption is as high as around 17% above the baseline non-adaptive classifier.  ...  The omnipresence of non-stationarity and noise in Electroencephalogram signals restricts the ubiquitous use of Brain-Computer interface.  ...  Indeed, the mental commands from the users are often incorrectly recognized by the BCI, due to the low signal-to-noise ratio of EEG signals, to their nonstationarity and to the limited amount of calibration  ... 
doi:10.1109/iww-bci.2019.8737349 fatcat:zancg5jekvgf3er7gf37ixgnma

Designing Brain-Computer Interfaces for Attention-Aware Systems

Evan M. Peck, Emily Carlin, Robert Jacob
2015 Computer  
the optimum time to interrupt the user.  ...  A prototype BCI that optimizes email notifications in noisy, complex environments, CARSON combines multiple measures from the brain to predict both cognitive workload and message relevancy to determine  ...  features are trained, CARSON streams them to the model for real-time classification.  ... 
doi:10.1109/mc.2015.315 fatcat:mk4x6qf4irdfthpujcenzkcfvu

Understanding changes in mental workload during execution of goal-directed tasks and its application for interruption management

Brian P. Bailey, Shamsi T. Iqbal
2008 ACM Transactions on Computer-Human Interaction  
the optimum time to interrupt the user.  ...  A prototype BCI that optimizes email notifications in noisy, complex environments, CARSON combines multiple measures from the brain to predict both cognitive workload and message relevancy to determine  ...  features are trained, CARSON streams them to the model for real-time classification.  ... 
doi:10.1145/1314683.1314689 fatcat:f2c62ozrhjgl3ljrurcfvqck2a

A self-paced BCI system with low latency for motor imagery onset detection based on time series prediction paradigm [article]

Navid Ayoobi, Elnaz Banan Sadeghian
2022 arXiv   pre-print
The onsets of the MI commands are detected shortly by comparing the incoming signal with the predicted signal. The proposed method is validated on dataset IVc from BCI competition III.  ...  In a self-paced motor-imagery brain-computer interface (MI-BCI), the onsets of the MI commands presented in a continuous electroencephalogram (EEG) signal are unknown.  ...  In real-world applications, however, the BCI system must analyze continuously incoming signals where the onsets and durations of MI-task signals and rest states are unknown.  ... 
arXiv:2204.05450v1 fatcat:7oeotl3grvgixd5ybjopbusv6u

A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update

F Lotte, L Bougrain, A Cichocki, M Clerc, M Congedo, A Rakotomamonjy, F Yger
2018 Journal of Neural Engineering  
In particular, the main challenges faced by classification methods for BCI are the low signal-to-noise ratio of EEG signals [172, 228] , their nonstationarity over time, within or between users, where  ...  In particular for BCI, EEG signals are typically filtered both in the time domain (band-pass filter), and spatial domain (spatial filter) before features are extracted from the resulting signals.  ... 
doi:10.1088/1741-2552/aab2f2 pmid:29488902 fatcat:7brd44bmqnegzov6mi4idtwjbi

Adaptive Riemannian BCI for Enhanced Motor Imagery Training Protocols

Daniel Freer, Fani Deligianni, Guang-Zhong Yang
2019 2019 IEEE 16th International Conference on Wearable and Implantable Body Sensor Networks (BSN)  
However, this protocol only permits the BCI to update between sessions, with little real-time evaluation of how the classifier has improved.  ...  Three variations of an adaptive Riemannian BCI were implemented and compared on data from both our own recorded datasets and the commonly used BCI Competition IV Dataset 2a.  ...  ACKNOWLEDGMENTS The authors would like to thank Shamas Khan for help in setting up experiments.  ... 
doi:10.1109/bsn.2019.8771079 dblp:conf/bsn/FreerDY19 fatcat:xv5lmz6uazbfvhas5fzsw33tbe

Neuro-Fuzzy Prediction for Brain-Computer Interface Applications [chapter]

Wei-Yen Hsu
2012 Fuzzy Inference System - Theory and Applications  
suitable for the prediction of non-stationary EEG signals.  ...  It has become popular for BCI systems on motor imagery (MI) EEG signals in the last decade [8] .  ...  The former obtains multiscale information of EEG signals while the latter decreases the effect of noise.  ... 
doi:10.5772/37495 fatcat:72ifcr35zbhaxh4ojtwcnbwirm

Generative adversarial neural networks maintain decoder accuracy during signal disruption in simulated long-term recordings from brain computer interfaces [article]

Thomas Stephens, Jon Cafaro, Ryan MacRae, Stephen Simons
2021 bioRxiv   pre-print
To test the accuracy of signal recovery we employ a common BCI paradigm wherein a classification algorithm (neural decoder) is trained on the starting (non-disrupted) set of signals.  ...  AbstractChronically implanted brain-computer interfaces (BCIs) provide amazing opportunities to those living with disability and for the treatment of chronic disorders of the nervous system.  ...  Each time a signal adapts it can increase or decrease its amplitude by 10-20% prior to accounting for the impedance change.  ... 
doi:10.1101/2021.05.18.444641 fatcat:zvpoo2fikneevp7ht75b4xkpz4

Brain Computing Interface using Deep Learning for Blind People

2019 International journal of recent technology and engineering  
In this study, analyze of brain's behavior using BCI for blind people in spatial activity. The common beliefs in blind people using other senses by compensate their lack of vision.  ...  The developing area of research in Brain Computer Interface (BCI) is used to enhance the quality of human computer applications.  ...  Basically, the major task of FC layer is to extract the incoming features for extracting the data about the brain signal for blind people.  ... 
doi:10.35940/ijrte.d8906.118419 fatcat:wzavwirodjfrvnhrdvlr46defi

Hybrid brain–computer interface for biomedical cyber-physical system application using wireless embedded EEG systems

Rifai Chai, Ganesh R. Naik, Sai Ho Ling, Hung T. Nguyen
2017 BioMedical Engineering OnLine  
For the real-time testing of the intentional signal on patients with tetraplegia, the average success rate of detection is 70% and the speed of detection varies from 2 to 4 s.  ...  For the offline artificial neural network classification for the target group of patients with tetraplegia, the hybrid BCI system combines three mental tasks, three SSVEP frequencies and eyes closed, with  ...  For the real-time testing of the intentional signal on patients with tetraplegia, the average success rate of detection is 70% and the speed of detection varies from 2 to 4 s.  ... 
doi:10.1186/s12938-016-0303-x pmid:28086889 pmcid:PMC5234249 fatcat:and6uw356rbitbkxkylgrqeq5e

Comparison of Different Visual Feedback Methods for SSVEP-Based BCIs

Mihaly Benda, Ivan Volosyak
2020 Brain Sciences  
This finding is very important for BCI experiments, as by determining the optimal feedback for the user, the speed of the BCI can be improved without impairing the accuracy.  ...  According to results from our previous studies, changes in stimulus size and contrast as online feedback of classification progress have great impact on BCI performance in SSVEP-based spellers.  ...  Acknowledgments: We would like to thank all of our participants, as well as our student assistants. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/brainsci10040240 pmid:32325633 pmcid:PMC7226383 fatcat:s42yx6xeenf53itjd2h2acxbqe

A Single-Channel Consumer-Grade EEG Device for Brain-Computer Interface: Enhancing Detection of SSVEP and Its Amplitude Modulation [article]

Phairot Autthasan, Xiangqian Du, Jetsada Arnin, Sirakorn Lamyai, Maneesha Perera, Sirawaj Itthipuripat, Tohru Yagi, Poramate Manoonpong and Theerawit Wilaiprasitporn
2019 arXiv   pre-print
SSVEP signals from 16 subjects were then recorded from electrodes placed at the central occipital site using a low-cost, consumer-grade EEG.  ...  In this study, we aim to develop an integrated approach to simultaneously estimate the frequency and contrast-related amplitude modulations of the SSVEP signal.  ...  ACKNOWLEDGMENT We would like to thank Fryderyk Kögl for their assistance in data collection and Thummanoon Kunanuntakij for his work in programming.  ... 
arXiv:1809.07356v3 fatcat:zn3bngvuwfghnjrmcxlr2dhkwu

A Low Cost Eeg Based Bci Prosthetic Using Motor Imagery

Daniel Elstob, Emanuele Lindo Secco
2016 International Journal of Advanced Information Technology  
This latter one allowed for the training and classification of EEG signals for motor imagery tasks.  ...  The proposed architecture performs overall good results for the design and implementation of economically convenient BCI and prosthesis.  ...  EEG Headset & Cognitive Suite The BCI headset is an Emotiv EPOC+ premium, i.e. a commercial scientific contextual EEG headset that will be used to record the EEG real-time signals from the human brain  ... 
doi:10.5121/ijitcs.2016.6103 fatcat:bdxzqelglvgghpkax5hmlmso6e

Poststroke Cognitive Impairment Research Progress on Application of Brain-Computer Interface

Xiaowei Sun, Mingyue Li, Quan Li, Hongna Yin, Xicheng Jiang, Hongtao Li, Zhongren Sun, Tiansong Yang, Lei Jiang
2022 BioMed Research International  
This paper reviews the promise and potential drawbacks of using BCI to treat poststroke cognitive impairment, providing a solid theoretical basis for the application of BCI in this area.  ...  Brain-computer interfaces (BCIs), a new type of rehabilitation technology, pick up nerve cell signals, identify and classify their activities, and convert them into computer-recognized instructions.  ...  The results showed that the average classification accuracy for the speech and thinking groups decreased slightly, while the average classification accuracy of the hearing and the control group was not  ... 
doi:10.1155/2022/9935192 pmid:35252458 pmcid:PMC8896931 fatcat:v6ighq7zmbfflll5ymo7jv2uc4

The Berlin Brain–Computer Interface: EEG-Based Communication Without Subject Training

B. Blankertz, G. Dornhege, M. Krauledat, K.-R. Muller, V. Kunzmann, F. Losch, G. Curio
2006 IEEE transactions on neural systems and rehabilitation engineering  
A more recent study showed that the RP similarily accompanies phantom movements in arm amputees, but the signal strength decreases with longer loss of the limb.  ...  These results are encouraging for an EEG-based BCI system in untrained subjects that is independent of peripheral nervous system activity and does not rely on evoked potentials even when compared to results  ...  ACKNOWLEDGMENTS We would like to thank Steven Lemm, Florin Popescu, and Christin Schäfer for fruitful discussions.  ... 
doi:10.1109/tnsre.2006.875557 pmid:16792281 fatcat:ksbfa7c6rbcflbv7gp4donbxp4
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