Brain-Machine Interface (BMI) based on Electroencephalography (EEG)
Main program: bmi_three_channels
The explanation of the project methodology and results is presented on:
http://www.youtube.com/watch?v=4IodfA_fHUM
The signal processing algorithm and pattern recognition system are presented in the IEEE publication:
Eduardo López-Arce Vivas, Alejandro García-González, Iván Figueroa, and Rita Fuentes. Discrete Wavelet Transform and ANFIS Classifier for Brain-Machine Interface based on EEG. International Conference on Human System Interaction, 2013. (THE BEST PAPER AWARD in the area of Human Machine Interaction)
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6577814&tag=1
Special Features:
*Algorithm for Real-Time Discrete Wavelet Transform.
*NI DAQ USB-6009 for Matlab 64-bits using listener and event.
*NI DAQ USB-6009 for Matlab 64-bits: analog input and digital output simultaneous sessions.
*On-line data plotted on GUI.
*Off-line Short-Time Fourier Transform data analysis.
*Save data acquired on GUI.
Cite As
Eduardo (2024). Brain-Machine Interface (BMI) based on Electroencephalography (EEG) (https://www.mathworks.com/matlabcentral/fileexchange/43795-brain-machine-interface-bmi-based-on-electroencephalography-eeg), MATLAB Central File Exchange. Retrieved .
MATLAB Release Compatibility
Platform Compatibility
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- Control Systems > Fuzzy Logic Toolbox >
- Sciences > Neuroscience > Human Brain Mapping > EEG/MEG/ECoG >
- Sciences > Neuroscience > Brain Computer Interfaces >
- Signal Processing > Wavelet Toolbox > Discrete Multiresolution Analysis > Signal Analysis >
- Engineering > Biomedical Engineering > Biomedical Signal Processing >
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Brain-Machine Interface/
Version | Published | Release Notes | |
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1.0.0.0 |