A Wireless BCI-Controlled Integration System in Smart Living Space for Patients

AbstractIn this study, we proposed a wireless brain–computer interface (BCI) with steady-state visually evoked potentials (SSVEP) to control several devices in a smart living space for paralyzed patients. In this system, we used electroencephalography (EEG) acquisition chip to extract SSVEPs from EEG signals and transform them by using of FFT into frequency domain. Then, these SSVEPs can be converted into commands to control several devices such as lights, television, air-condition, electric bed, wheelchair, and short message services through a Bluetooth on a mobile device for patients. In this system, several flickering patterns with different frequencies were generated. NeuroSky EEG chips were used to capture EEG signals from locations Oz and FP2. The patients gazed these flickering patterns to generate SSVEPs, and then these SSVEPs were extracted from location Oz on their occipital lobe. Additionally, eye-winking signal was captured from location FP2 on forehead to generate an emergency command. Then these signals can be transformed by FFT into frequency domain and then transmitted to the hardware through Bluetooth interface. The advantages of the proposed BCI system are low cost, low power consumption and compact size so that the system can be suitable for the paralytic patients. The experimental results showed that feasible actions can be obtained for the proposed BCI system and control circuit with a practical operating in a smart living space for paralyzed patients.

[1]  Po-Lei Lee,et al.  Accounting for Phase Drifts in SSVEP-Based BCIs by Means of Biphasic Stimulation , 2011, IEEE Transactions on Biomedical Engineering.

[2]  Po-Lei Lee,et al.  Total Design of an FPGA-Based Brain–Computer Interface Control Hospital Bed Nursing System , 2013, IEEE Transactions on Industrial Electronics.

[3]  Yijun Wang,et al.  A high-speed BCI based on code modulation VEP , 2011, Journal of neural engineering.

[4]  Po-Lei Lee,et al.  Real-time control of an SSVEP-actuated remote-controlled car , 2010, Proceedings of SICE Annual Conference 2010.

[5]  Jzau Sgeng Lin,et al.  An FPGA-Based Brain-Computer Interface for Wireless Electric Wheelchairs , 2013 .

[6]  Hongtao Wang,et al.  Remote control of an electrical car with SSVEP-Based BCI , 2010, 2010 IEEE International Conference on Information Theory and Information Security.

[7]  G. Pfurtscheller,et al.  Brain-Computer Interfaces for Communication and Control. , 2011, Communications of the ACM.

[8]  G. Pfurtscheller,et al.  An SSVEP BCI to Control a Hand Orthosis for Persons With Tetraplegia , 2011, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[9]  P. Husar,et al.  A periodogram-based method for the detection of steady-state visually evoked potentials , 1998, IEEE Transactions on Biomedical Engineering.

[10]  Brian J. F. Wong,et al.  Needle-Electrode-Based Electromechanical Reshaping of Rabbit Septal Cartilage: A Systematic Evaluation , 2011, IEEE Transactions on Biomedical Engineering.

[11]  Yijun Wang,et al.  Brain-Computer Interfaces Based on Visual Evoked Potentials , 2008, IEEE Engineering in Medicine and Biology Magazine.

[12]  Muhammad Imran,et al.  Low-cost single-channel EEG based communication system for people with lock-in syndrome , 2011, 2011 IEEE 14th International Multitopic Conference.