Neural Control using EEG as a BCI Technique for Low Cost Prosthetic Arms

There have been significant advancements in brain computer interface (BCI) techniques using EEG-like methods. EEG can serve as non-invasive BMI technique, to control devices like wheelchairs, cursors and robotic arm. In this paper, we discuss the use of EEG recordings to control low-cost robotic arms by extracting motor task patterns and indicate where such control algorithms may show promise towards the humanitarian challenge. Studies have shown robotic arm movement solutions using kinematics and machine learning methods. With iterative processes for trajectory making, EEG signals have been known to be used to control robotic arms. The paper also showcases a case-study developed towards this challenge in order to test such algorithmic approaches. Non-traditional approaches using neuro-inspired processing techniques without implicit kinematics have also shown potential applications. Use of EEG to resolve temporal information may, indeed, help understand movement coordination in robotic arm.