Comparing Performance of Dry and Gel EEG Electrodes in VR using MI Paradigms

Author:

Ahmadi Mohammad1ORCID,Farrokhi Nia Alireza2ORCID,Michalka Samantha W.3ORCID,Sumich Alexander L.4ORCID,Wuensche Burkhard5ORCID,Billinghurst Mark6ORCID

Affiliation:

1. Empathic Computing Lab, University of Auckland, New Zealand

2. Empathic Computing Lab, Auckland Bioengineering Institute, New Zealand

3. Olin College of Engineering, United States

4. NTU Psychology, Nottingham Trent University, United Kingdom

5. University of Auckland, New Zealand

6. Empathic Computing Lab, The University of Auckland, New Zealand

Publisher

ACM

Reference8 articles.

1. H Altaheri , G Muhammad , M Alsulaiman , S Amin , G Altuwaijri , W Abdul , M Bencherif , and M Faisal . 2021. Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: A review. Neural Computing and Applications ( 2021 ), 1–42. H Altaheri, G Muhammad, M Alsulaiman, S Amin, G Altuwaijri, W Abdul, M Bencherif, and M Faisal. 2021. Deep learning techniques for classification of electroencephalogram (EEG) motor imagery (MI) signals: A review. Neural Computing and Applications (2021), 1–42.

2. Determining the optimal feature for two classes Motor-Imagery Brain-Computer Interface (L/R-MI-BCI) systems in different binary classifiers;Alzahab N;International Journal of Mechanical and Mechatronics Engineering,2019

3. Y Chu , X Zhao , Y Zou , W Xu , J Han , and Y Zhao . 2018. A decoding scheme for incomplete motor imagery EEG with deep belief network. Frontiers in neuroscience 12 ( 2018 ), 680. Y Chu, X Zhao, Y Zou, W Xu, J Han, and Y Zhao. 2018. A decoding scheme for incomplete motor imagery EEG with deep belief network. Frontiers in neuroscience 12 (2018), 680.

4. Brain computer interface game controlling using fast fourier transform and learning vector quantization;Djamal E;Journal of Telecommunication, Electronic and Computer Engineering (JTEC),2017

5. M Kousarrizi , A Ghanbari , M Teshnehlab , M Shorehdeli , and A Gharaviri . 2009. Feature extraction and classification of EEG signals using wavelet transform , SVM and artificial neural networks for brain computer interfaces. In 2009 international joint conference on bioinformatics, systems biology and intelligent computing . IEEE , 352–355. M Kousarrizi, A Ghanbari, M Teshnehlab, M Shorehdeli, and A Gharaviri. 2009. Feature extraction and classification of EEG signals using wavelet transform, SVM and artificial neural networks for brain computer interfaces. In 2009 international joint conference on bioinformatics, systems biology and intelligent computing. IEEE, 352–355.

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