Total Design of an FPGA-Based Brain–Computer Interface Control Hospital Bed Nursing System

An Efficient Hardware Implementation for a Motor Imagery Brain Computer Interface System

“…Most of the literature in which a full BCI system is implemented are concentrated on the steady-state visual Evoked Potential, which is an EEG signal response to the ickering visual stimulus [44], [45], [46]. However, in [47], a BCI system is implemented based on motor imagery, which includes Finite Impulse Response lter as a preprocessing, CSP as a feature extraction, and Mahalanobis distance as a classi cation, on Stratix IV FPGA Board with operational frequency of 200 MHz.…”

“…SSVEP is also utilized in [51] to control environmental devices such as television. Controlling hospital bed nursing system in a FPGA-based BCI system is also addressed in [52].…”

Performance analysis of different techniques for Brain Computer Interfacing

“…H-bridge dc motor drive circuit was implemented to adjust the attitude of the hospital bed and 15 subjects were invited to demonstrate the effectiveness of the proposed BCI based hospital bed control nursing system. Finally, they found average accuracy of 92.5% and an average command transfer interval of 5.22s per command [17]. Recently, also implemented a mental spelling system based on steady-state visual evoked potential (SSVEP), adopting a QWERTY style layout keyboard with 30 LEDs flickering with different frequencies.…”

An Event-Related Potential-Based Adaptive Model for Telepresence Control of Humanoid Robot Motion in an Environment Cluttered With Obstacles

“…It includes N200 potential that is a negative valley appearing at poststimulus 180-325 ms and P300 potential that is a positive peak appearing at poststimulus 250-800 ms [6]. Since the first P300 speller based on the "oddball" paradigm was developed [7], the ERP-based brain-computer interface (BCI) systems have emerged, e.g., spelling sentences [8], controlling electrical applications in a virtual [9] or a lab environment [10], browsing Internet websites [11], controlling a wheelchair [12], [13], a hospital-bed nursing system [14], a robotic arm [15], and a humanoid robot [16]- [19]. Nevertheless, the telepresence control of a humanoid robot [20]- [24] via brain signals to perform complex operational tasks is not only helpful for the disabilities addressed in biomedical engineering but also very important for operators controlling humanoid robots in military, astronautic, and industrial applications.…”