Brain-Controlled Robotic Arm System Based on Multi-Directional CNN-BiLSTM Network Using EEG Signals

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 28; no. 5; pp. 1226 - 1238
• Main Authors: Jeong, Ji-Hoon, Shim, Kyung-Hwan, Kim, Dong-Joo, Lee, Seong-Whan
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Brain; Brain–machine interface (BMI); Convolution; Correlation analysis; Correlation coefficient; Correlation coefficients; Decoding; Deep learning; EEG; electroencephalogram (EEG); Electroencephalography; Extremities; Image acquisition; Interfaces; intuitive robotic arm control; Long short-term memory; Machine learning; Man-machine interfaces; Manipulators; motor imagery; Neural networks; Robot arms; Robot control; Robotics; Task analysis; Three-dimensional displays; Velocity distribution

Brain-machine interfaces (BMIs) can be used to decode brain activity into commands to control external devices. This paper presents the decoding of intuitive upper extremity imagery for multi-directional arm reaching tasks in three-dimensional (3D) environments. We designed and implemented an experi...