Mobile QR Code QR CODE

REFERENCES

1 
Pereira Joana, Sburlea Andreea Ioana, Müller-putz Gernot R, September 2018, EEG patterns of self-paced movement imaginations towards externally-cued and internally- selected targets, nature scientificreportsDOI
2 
Sereshkeh Alborz Rezazadeh, Trott Robert, Bricout Aurelien, Chau Tom, December 2017, EEG Classification of Covert Speech Using Regularized Neural Networks, IEEE/ACM Transactions on Audio Speech and Language Processing, Vol. 25, pp. 2292-2300DOI
3 
Hickok Gregory, February 2012, Computational neuroanatomy of speech production, Nature reviews-Neuroscience, Vol. 13, pp. 135-145DOI
4 
Rahman1 K. A. A., Ibrahim1 B. S. K. K., Leman A.M., Jamil M. M. A., December 2012, Fundamental study on brain signal for BCI-FES system development, IEEE-EMBS Conference on Biomedical Engineering and Sciences, pp. 195-198DOI
5 
Matsumotoa Mariko, Hori Junichi, November 2013, Classification of silent speech using support vector machine and relevance vector machine, Applied Soft Computing, Vol. 20, pp. 95-102DOI
6 
Xu Xiaolong, Liu Qingxiang, Luo Yun, Peng Kai, Zhang Xuyun, Meng Shunmei, January 2019, A computation offloading method over big data for IoT-enabled cloud-edge computing, Future Generation Computer Systems, Vol. 95, pp. 522-533DOI
7 
Jafferson A. Joshua, JAN 2020, ,A Review on Machine Learning Mechanisms for Imagined Speech Classification, Journal of Advanced Research in Dynamical and Control Systems, Vol. volume 12, No. 1, pp. 137-142DOI
8 
Vijayakumar , abajieet , balaji , February 2019, A Palm Vein Recognition System based on support vector machine, IEIE Transaction on smart processing and computing, Vol. 8, No. 1DOI
9 
Ganga Revanth Chowdary, Vijayakumar P., Badrinath Pratyush, Singh Ankit Raj, Singh Mohit, Drone Control Using EEG Signal, Journal of Advanced Research in Dynamical and Control Systems, Vol. 11, No. 4, pp. 2107-2113URL
10 
Cooney Ciaran, Folli Raffaella, Coyle Damien, October 2018, Neurolinguistics Research Advancing Development of a Direct-Speech Brain-Computer Interface, iScience Cellpress Reviews, Vol. 8, No. , pp. 103-125DOI
11 
Sereshkeh Alborz Rezazadeh, Yousefi Rozhin, Wong Andrew T, November 2018, Online classification of imagined speech using functional near-infrared spectroscopy signals, Journal of neural engineering, Vol. 16DOI
12 
Valente Giancarlo, Kaas Amanda L., Formisano Elia, Goebel Rainer, 2019, Optimizing fMRI experimental design for MVPA-based BCI control: Combining the strengths of block and event-related designs, NeuroImage, Vol. 186, pp. 369-381DOI
13 
Brumberg Jonathan S., Krusienski Dean J., Chakrabarti Shreya, Gunduz Aysegul, Brunner Peter, Ritaccio Anthony L., Schalk Gerwin, November 2016, Spatio-temporal progression of cortical activity related to continuous overt and covert speech production in a reading task, PLoS ONE, Vol. 11, pp. 1-21DOI
14 
Jahangiri1 Amir, Sepulveda Francisco, 2019, The Relative Contribution of High-Gamma Linguistic Processing Stages of Word Production, and Motor Imagery of Articulation in Class Separability of Covert Speech Tasks in EEG Data, Journal of Medical Systems, Vol. 43DOI
15 
Wang Li, Zhang Xiong, Zhong Xuefei, Zhang Yu, 2013, Analysis and classification of speech imagery EEG for BCI, Biomedical Signal Processing and Control, Vol. 8, pp. 901-908DOI
16 
Siuly Siuly , Li Yan, Zhang Yanchun, 2016, EEG Signal Analysis and Classification Techniques and Applications, Health Information ScienceDOI
17 
Martin Stephanie, Brunner Peter, Iturrate1 Iñaki, Millán1 José del R., Schalk Gerwin, Knight Robert T., Pasley Brian N., May 2016, Word pair classification during imagined speech using direct brain recordings, Nature Scientific Reports, Vol. 6DOI