|--- datasets - all the datasets are provided here
|--- Signal acquisition - in this folder, all the schematics of the EEG designs and the EEG visualisation tools and codes are there.
| |---circuits
| | |--- prototype EEG components - all the component circuits needed in the construction of an EEG is here.
| | | |--- DRL_circuit.sch
| | | |--- RFI_filter.sch
| | | |--- Instrumentation_Amplifier.sch
| | | |--- Variable_amp_circut.sch
| | | |--- HighPassFilter.sch
| | | |--- LowPassFilter.sch
| | | |--- CMR_circut.sch
| | | |--- Buffer_circuit.sch
| | | |--- Clamp_circuit.sch
| | | |--- REF_circuit.sch
| | | |--- analogue_digital_convert.sch
| | | |--- IsolateCircuit.sch
| | | |--- ProtectionCircuit.sch
| | | |--- Notch_filter.sch
| | |--- complete EEG circuit diagram
| | | |--- EEG_circuit.sch - this file has the circuit design for a single channel EEG circuit
| | | |--- EEG.pcb - this file contains the compiled EEG biosensing board with at least 8 channels.
| |--- visualisation tools
| | |--- plot.py - this file is for the visualisation of the given dataset
|--- Models - This is the folder where your training models will stay
| |--- Model01 - This is our first model
| | |--- training.py - This is script we used to training our first model
| | |--- analysis.py - This is the script we used to run test and get the confusion matrix
| | |--- result.jpeg - The resulting confusion matrix
|--- device output - contains the files related to robotics.
- DL/ML and related python libraries (PyTorch, TensorFlow, scipy, sklearn, or some more)
- Basic understanding of BCI and EEG devices
- PCB design using Autodesk Eagle
- understanding of Arduino
- ROS basics (optional)
- a general idea of time-domain and frequency-domain circuits.
- classification algorithms (like CNN, SVM, k-clustering, and so on).
- shell script (UNIX commands)
Firstly, the project is divided into 3 parts:
- EEG circuit building
- Brain Signal analysis
- robotics and controls subsystem
You choose any of the parts and start working on it according to the instructions and resources provided.
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This is a must read about general layout of a BCI as a system, EEG channel selection, different signal classification algorithms, etc -
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3304110/ -
Brain Computer Interface w/ Python and OpenBCI. (This will get you familiar with using DL models on EEG signals.) https://www.youtube.com/playlist?list=PLQVvvaa0QuDeU-QCAwZl2UmSpfb4sWbxY
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Deep learning for electroencephalogram (EEG) classification tasks: a review (This has in-depth analysis of DL techniques people have used so far) https://iopscience.iop.org/article/10.1088/1741-2552/ab0ab5