30 March 2012 Wireless brain-machine interface using EEG and EOG: brain wave classification and robot control
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A brain-machine interface (BMI) links a user's brain activity directly to an external device. It enables a person to control devices using only thought. Hence, it has gained significant interest in the design of assistive devices and systems for people with disabilities. In addition, BMI has also been proposed to replace humans with robots in the performance of dangerous tasks like explosives handling/diffusing, hazardous materials handling, fire fighting etc. There are mainly two types of BMI based on the measurement method of brain activity; invasive and non-invasive. Invasive BMI can provide pristine signals but it is expensive and surgery may lead to undesirable side effects. Recent advances in non-invasive BMI have opened the possibility of generating robust control signals from noisy brain activity signals like EEG and EOG. A practical implementation of a non-invasive BMI such as robot control requires: acquisition of brain signals with a robust wearable unit, noise filtering and signal processing, identification and extraction of relevant brain wave features and finally, an algorithm to determine control signals based on the wave features. In this work, we developed a wireless brain-machine interface with a small platform and established a BMI that can be used to control the movement of a robot by using the extracted features of the EEG and EOG signals. The system records and classifies EEG as alpha, beta, delta, and theta waves. The classified brain waves are then used to define the level of attention. The acceleration and deceleration or stopping of the robot is controlled based on the attention level of the wearer. In addition, the left and right movements of eye ball control the direction of the robot.
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Sechang Oh, Sechang Oh, Prashanth S. Kumar, Prashanth S. Kumar, Hyeokjun Kwon, Hyeokjun Kwon, Vijay K. Varadan, Vijay K. Varadan, } "Wireless brain-machine interface using EEG and EOG: brain wave classification and robot control", Proc. SPIE 8344, Nanosensors, Biosensors, and Info-Tech Sensors and Systems 2012, 83440U (30 March 2012); doi: 10.1117/12.918159; https://doi.org/10.1117/12.918159

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