Labeled EEG for Mind-Controlled Arm

Developing mind-controlled prosthetics that seamlessly integrate with the human nervous system is a significant challenge in the field of bioengineering. This project investigates the use of labelled brainwave patterns to control a bionic arm equipped with a sense of touch. The core objective is to establish a communication channel between the brain and the artificial limb, enabling intuitive and natural control while incorporating sensory feedback.

The project involves:

• Data Acquisition: Recording brainwave activity using electroencephalography (EEG) while participants perform various hand and arm movement
• Signal Processing: Labelling and extracting relevant features from the recorded brainwave patterns.
• Machine Learning: Training a machine learning model to decode the labelled brainwave patterns and translate them into control signals for the bionic arm.
• Integration: Integrating the trained model with the bionic arm, enabling real-time control based on the user's intent.
• Sensory Feedback: Incorporating sensory feedback mechanisms into the bionic arm to provide the user with a sense of touch, enhancing control and natural interaction.

The successful completion of this project has the potential to revolutionize the field of prosthetics by offering amputees a more intuitive and natural control over their artificial limbs, ultimately improving their quality of life.