A Prosthetic Arm Based on Electroencephalography by Signal Acquisition and Processing on MATLAB


  • Arushi Chaudhry
  • Uzma Khan
  • Mukesh Reddy Palla
  • Shyam Bramhadev Singh
  • Shubham Vijaykumar Deshmukh


Electroencephalography, Brain-Computer Interface (BCI), Steady-state visual evoked potential (SSVEP), Fast Fourier Transform (FFT)


This paper presents the prosthetic arm based on electroencephalography by signal acquisition and processing. Around the world, there are 5-6 million people with partial hand amputation due to traumatic accidents, various health issues and wars. Recent advancements show prosthetic arms are purely mechanical and tedious. In order to solve this problem, Brain-Computer Interface (BCI)-based control strategies were introduced into robot control. The methods adopted should take into consideration the nature of the application, for example, Electroencephalography (EEG) signal is ideal for our application due to its convenient approach. Particularly, for EEG-based BCI systems, a set of sensors are needed to acquire the EEG signals from different brain areas. The Fast Fourier Transform algorithm is adopted for feature extraction of the EEG signals and python is used to save the data in .txt file. The .txt file is imported into MATLAB and data analysis is done by signal processing and analysis tool. Next, Signal classification is done and then the signal is carried to end-effector. Our findings indicate that the rise of 3D printing industry, advanced printers and materials will allow students to develop more ‘commercial-like prosthetic devices – robust and durable systems that could benefit a wide range of people with a missing limb. With ongoing research, more technological advancements in EEG would definitely result in improvements which will hopefully lead to a system that is more durable and offers improved dexterity and control.


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How to Cite

A. Chaudhry, U. Khan, M. R. Palla, S. B. Singh, and S. V. Deshmukh, “A Prosthetic Arm Based on Electroencephalography by Signal Acquisition and Processing on MATLAB”, IJRESM, vol. 5, no. 1, pp. 119–124, Jan. 2022.