Deep Learning Enhanced BCI Technology for 3D Printing

• Published in: 2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) pp. 0125 - 0130
• Main Authors: Kachhia, Jahnavi, Natharani, Rashika, George, Kiran
• Format: Conference Proceeding
• Language: English
• Published: IEEE 28.10.2020
• Subjects: 3D printing; Brain modeling; convolutional neural network (CNN); Data models; deep learning; EEG signals; Electroencephalography; long short-term memory (LSTM); mental imagery; recurrent neural network (RNN); Shape; Solid modeling; Three-dimensional displays; Training
• DOI: 10.1109/UEMCON51285.2020.9298124

The purpose of this paper is to combine Deep Learning with Brain-Computer Interface (BCI) for 3D Printing without human interference. This design will eliminate the intermediate steps and enable people to generate 3D prints faster. To collect the data, subjects are asked to wear g.Nautilus headsets and perform a mental imagery task. These collected brain waves are preprocessed using MATLAB and then are used to train different Neural Network architectures. The Neural Network model recognizes patterns in these brain waves to predict the shape imagined by the user. In this paper, we introduce CNN-LSTM that servers the purpose of classifying objects accurately. Once the shape is identified, the CAD file is generated in STL format using the predefined size. Lastly, this STL file is converted into G-code and serially transferred to the 3D Printer.