Application of EEG Signals Integration to Proprietary Classification Algorithms in the Implementation of Mobile Robot Control with the Use of Motor Imagery Supported by EMG Measurements

Abstract

This article is a continuation and extension of research on a new approach to the classification and recognition of EEG signals. Their goal is to control the mobile robot through mental commands, using a measuring set such as Emotiv Epoc Flex Gel. The headset, despite its relative advancement, is rarely found in this type of research, which makes it possible to search for its advanced and innovative applications. The uniqueness of the proposed approach is the use of an EMG measuring device located on the biceps, i.e., MyoWare Muscle Sensor. This is to verify pure mental commands without additional muscle contractions. The participants of the study were asked to imagine the forearm movement that was responsible for triggering the movement command of the LEGO Mindstorms EV3 robot. The change in direction of movement is controlled by artifacts in the signal caused by the blink of an eyelid. The measured EEG signal was subjected to meticulous analysis by an expert system containing a classic classification algorithm and an artificial neural network. It was supposed to recognize mental commands, as well as detect artifacts in the form of blinking and change the direction of the robot’s movement. In addition, the system monitored the analysis of the EMG signal, detecting possible muscle tensions. The output of the expert algorithm was a control signal sent to the mobile robot.

Keywords

• EEG
• Emotiv EPOC Flex Gel
• NeuroHeadset
• EMG
• LEGO Mindstorms
• brain–computer interface (BCI)