Green EEG energy control robot for supporting bedfast patients

Abstract

The objectives of this study were (1) to develop a prototype for controlling the movements of a robot by using green electroencephalography (EEG) energy and (2) to test a prototype algorithm’s effectiveness for controlling robot movements. The study was conducted as experimental research. An EEG detects electrical energy from activities of the brain and nervous system. The brain’s electrical energy or EEG is produced by thinking or the mind controlling, blinking, meditating, and focusing. The raw acquired EEG data underwent preprocessing and feature extraction. The acquired EEG signals were used to control the robot. Algorithms were designed and developed to connect the EEG data and the robot. The effectiveness of the prototype algorithm in controlling a robot to perform unidirectional movement was determined. The algorithm achieved an accuracy of 81.43%. Likewise, an evaluation of the effect of the prototype algorithm in controlling a robot to perform continuous movement was performed. The algorithm was modified and combined with a gyroscope accelerometer magnetic sensor (IMU) node in a hybrid manner. The experimental result presented 80.22% accuracy, and the algorithm worked properly and consumed 2.40 min of time. The results indicated that the EEG algorithm could correctly control robot movements.

Keywords

• Green electrical energy in the brain
• Brain energy
• Electroencephalography
• EEG