Decoding the Human Brain Activity and Predicting the Visual Stimuli from Magnetoencephalography (MEG) Recordings-Reference-Cited by-同舟云学术

Decoding the Human Brain Activity and Predicting the Visual Stimuli from Magnetoencephalography (MEG) Recordings

Published:2019-04-19 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2019 International Conference on Intelligent Medicine and Image Processing
language:
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Author:

Zarief Christine Nasser¹,Hussein Walid²

Affiliation:

1. Software Engineering Department, The British University in Egypt, Cairo, Egypt

2. Computer Science Department, The British University in Egypt, Cairo, Egypt

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3332340.3332352

Reference18 articles.

1. Pulkit Agrawal. 2011. A Probe into Decoding Brain Activity Using fMRI and MEG Pulkit Agrawal. 2011. A Probe into Decoding Brain Activity Using fMRI and MEG

2. Abdullah Caliskan Mehmet Yuksel Hasan Badem and Alper Basturk. 2017. A Deep Neural Network Classifier for Decoding Human Brain Activity Based on Magnetoencephalography. Elektronika ir Elektrotechnika 23 (2017). http://eejournal.ktu.lt/index.php/elt/article/view/18002 Abdullah Caliskan Mehmet Yuksel Hasan Badem and Alper Basturk. 2017. A Deep Neural Network Classifier for Decoding Human Brain Activity Based on Magnetoencephalography. Elektronika ir Elektrotechnika 23 (2017). http://eejournal.ktu.lt/index.php/elt/article/view/18002

3. Multivariate pattern analysis of MEG and EEG: A comparison of representational structure in time and space

4. S. Foldes Wei Wang Jennifer L. Collinger Xin Li Jinyin Zhang Gustavo Sudre Anto Bagic Douglas J. Weber and R Sathish Srinivasan. 2011. Accessing and Processing MEG Signals in Real-Time: Emerging Applications and Enabling Technologies. S. Foldes Wei Wang Jennifer L. Collinger Xin Li Jinyin Zhang Gustavo Sudre Anto Bagic Douglas J. Weber and R Sathish Srinivasan. 2011. Accessing and Processing MEG Signals in Real-Time: Emerging Applications and Enabling Technologies.

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