A Brain-Computer Interface (BCI) is a computer-based system that acquires brain signals, analyzes them, and translates them into commands that are relayed to an output device to carry out a desired action. Although traditional BCI systems have been dependent only on brain signals recorded using Electroencephalography (EEG) and have used a rule-based translation algorithm to generate control commands. However, the recent use of multi-sensor data fusion and machine learning-based translation algorithms has improved the accuracy of such systems and makes them more suitable to control external agents like robot. In this seminar paper, BCI is review in details with its possible application for robotic control. The review discusses BCI application using multi-sensor fusion and machine learning to control robot to perform desired tasks. It also covers benefits, limitations, drawbacks, current trends and future path of BCI systems.
The paper should be in the format below;
Table of Content
2. Literature Review
5. Future Directions
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