Control of a 7-DOF Robotic Arm System With an SSVEP-Based BCI

Abstract

Although robot technology has been successfully used to empower people who suffer from motor disabilities to increase their interaction with their physical environment, it remains a challenge for individuals with severe motor impairment, who do not have the motor control ability to move robots or prosthetic devices by manual control. In this study, to mitigate this issue, a noninvasive brain-computer interface (BCI)-based robotic arm control system using gaze based steady-state visual evoked potential (SSVEP) was designed and implemented using a portable wireless electroencephalogram (EEG) system. A 15-target SSVEP-based BCI using a filter bank canonical correlation analysis (FBCCA) method allowed users to directly control the robotic arm without system calibration. The online results from 12 healthy subjects indicated that a command for the proposed brain-controlled robot system could be selected from 15 possible choices in 4[Formula: see text]s (i.e. 2[Formula: see text]s for visual stimulation and 2[Formula: see text]s for gaze shifting) with an average accuracy of 92.78%, resulting in a 15 commands/min transfer rate. Furthermore, all subjects (even naive users) were able to successfully complete the entire move-grasp-lift task without user training. These results demonstrated an SSVEP-based BCI could provide accurate and efficient high-level control of a robotic arm, showing the feasibility of a BCI-based robotic arm control system for hand-assistance.

A novel end‐to‐end deep learning scheme for classifying multi‐class motor imagery electroencephalography signals

“…Brain–computer interface (BCI) systems provide a communication and control path between a computer and a human with applications in diverse areas such as medical (Burns et al, ; Yin et al, ; Sereshkeh et al, ), robotics (Lu et al, ; Chen et al, ; Sabri et al, ), the military, and games. Development of BCI systems requires a multidisciplinary approach crossing many fields including neurobiology, psychology, engineering, mathematics, and computer science that is highly affected by developments in each of those fields (Mirzaei et al, ; Mirzaei and Adeli, ; Jiao et al, ).…”

Quadcopter Control System Using a Hybrid BCI Based on Off-Line Optimization and Enhanced Human-Machine Interaction

“…Y , E indicates the EEG in the alpha band and the power in the alpha band [35], [36]. The percentage ϕ of the alpha-band power in EEG signals was calculated as following (2), in which Y and X indicated the EEG in the alpha band and unfiltered EEG signals.…”

“…The BCI is aimed at assisting persons with severe motor function disability to communicate with the external world freely [1]. Meanwhile, the BCI has been applied to human-computer interaction, virtual reality and human-robot coordination [2]- [4]. The quadcopter, as an important way for the human to explore the physical world, becomes an…”

Fully portable and wireless universal brain–machine interfaces enabled by flexible scalp electronics and deep learning algorithm

“…The fully flexible, wearable system enables real-time long-range wireless data acquisition and accurate classification of SSVEP with a high information transfer rate from only two recording channels. Due to extreme mechanical compliance and small form factor, SKINTRONICS exhibits significant reduction of noise and electromagnetic interference, compared to the existing portable EEG systems with rigid electronic components 33,[35][36][37] . Additionally, the use of conformal electronic components allows for easy wearability on the back of the neck or other bare skin locations.…”