Sunday 21 April 2019

FPGA BASED BRAIN MACHINE CONTROL

Objective
To design and implement a highly powerful Network on chip configuration through Brain-Machine interface Controls

Scope of the Study
This Study helpful for us to get more knowledge on FPGA based reconfigurable platforms
This study enable us to learn more about BCI applications in Digital Platforms

 Module 1 Design of Preprocessing
This module is used to extract the useful info from the Brain wave datasets collected from the Test Peoples.The info vectors are extracted through DWT filters. The extraction of Alpha Beta Teta Delta Gamma waves are extracted separately.The frequency of the waves also determined by an cymometer designed inside.

 Module 2: Design of Neural Network Configuration
This block is used to design a Neural network platform used to analyze the input vectors with the pre-trained vectors of the database. The input vectors are compared with the trained vectors. The neural configuration is iteratively repeated with number of epocs.

 Module 3: Design of FPGA Configurations
This module focus on configuring the network on chip system with the matched vectors to process the data for controlling the available machines. The Machines are normally connected with the FPGA board in parallel manner. The Configured bits are further used for the control of machineries and other control applications.




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