Brain Controlled Robotic Arm Using Motor Movements Using EEG Signals

International Journal of Electronics and Communication Engineering

© 2024 by SSRG - IJECE Journal

Volume 11 Issue 11

Year of Publication : 2024

Authors : S. Thejaswini, R. Banuprakash, Siddiq Iqbal, N. Ramesh Babu

10.14445/23488549/IJECE-V11I11P106

How to Cite?

S. Thejaswini, R. Banuprakash, Siddiq Iqbal, N. Ramesh Babu, "Brain Controlled Robotic Arm Using Motor Movements Using EEG Signals," SSRG International Journal of Electronics and Communication Engineering, vol. 11,  no. 11, pp. 54-61, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I11P106

Abstract:

Brain-computer interface systems are a promising technology that allows individuals with physical disabilities to control various devices and applications through their brain activity. One of the vital challenges in developing effective BCI systems is the accurate classification of motor actual/imagery movements from electroencephalography signals. This study investigates the classification of actual motor and imagery-based BCI tasks identified using convolutional neural networks. Temporal features were extracted through spectrogram analysis, and the resulting images were fed to the CNN model to classify the data into four distinct classes. The model achieved an approximate prediction accuracy of 62% with a classification rate of 100% for Class 1, 50% for Classes 2 and 3, and 75% for Class 4. This model demonstrated a reasonably effective ability to detect the intended motor movements from Electroencephalography signals. Additionally, a robotic prototype is developed that is capable of performing specific functions, including moving backwards, moving forward, pinching in, and pinching out, based on the output of the classification model.

Keywords:

Brain Computer Interface, Short-term fourier transforms, Spectrograms, CNN, EEG.

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