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• Design a hybrid hardware-driven visual stimulator that can save system computing resources. • Propose a lightweight BCI master control framework for SSVEP-BCI wheelchair signal processing. • Implement a small size, low power, and user-friendly electric wheelchair prototype for system evaluation. Electric wheelchairs, as mobile auxiliary equipment, improve the quality of life and self-independence of the disabled. The brain-computer interface (BCI) is used to establish a direct connection between the brain and the wheelchair, which adopts the electroencephalogram (EEG) signal to control the wheelchair. Compared with other types of BCI, the steady-state visual evoked potentials (SSVEP)-BCI is more suitable to control the electric wheelchair due to the advantages of higher information transmission rate (ITR), higher signal-to-noise ratio (SNR), and less training. However, the existing SSVEP-BCI electric wheelchairs need to be equipped with at least one personal computer to drive the visual stimulator and process EEG signals in real-time. As a result, the electric wheelchair system is complicated, bulky, and limited in movement flexibility, so it is difficult to popularize in real-life scenarios. Therefore, to improve the portability and applicability of the SSVEP-BCI electric wheelchair, a lightweight SSVEP-BCI system is needed, which should be as light and energy-saving as possible while meeting functional requirements. This paper presents an embedded lightweight SSVEP-BCI electric wheelchair with a hybrid stimulator. A hybrid hardware-driven visual stimulator is designed, which combines the advantages of liquid crystal display (LCD) and light-emitting diode (LED) to achieve lower energy consumption than the traditional LCD stimulator. In addition, a lightweight BCI framework is designed to realize BCI program functions on the embedded platform for achieving similar performance as those on a personal computer. Experiments on real systems show that our embedded lightweight SSVEP-BCI electric wheelchair can be successfully operated by all eight subjects with a 93.9% average success rate of command operation. In addition, compared with the traditional LCD stimulator, the hybrid hardware-driven visual stimulator can save up to 27% of energy. [ABSTRACT FROM AUTHOR]

• Design a hybrid hardware-driven visual stimulator that can save system computing resources. • Propose a lightweight BCI master control framework for SSVEP-BCI wheelchair signal processing. • Implement a small size, low power, and user-friendly electric wheelchair prototype for system evaluation. Electric wheelchairs, as mobile auxiliary equipment, improve the quality of life and self-independence of the disabled. The brain-computer interface (BCI) is used to establish a direct connection between the brain and the wheelchair, which adopts the electroencephalogram (EEG) signal to control the wheelchair. Compared with other types of BCI, the steady-state visual evoked potentials (SSVEP)-BCI is more suitable to control the electric wheelchair due to the advantages of higher information transmission rate (ITR), higher signal-to-noise ratio (SNR), and less training. However, the existing SSVEP-BCI electric wheelchairs need to be equipped with at least one personal computer to drive the visual stimulator and process EEG signals in real-time. As a result, the electric wheelchair system is complicated, bulky, and limited in movement flexibility, so it is difficult to popularize in real-life scenarios. Therefore, to improve the portability and applicability of the SSVEP-BCI electric wheelchair, a lightweight SSVEP-BCI system is needed, which should be as light and energy-saving as possible while meeting functional requirements. This paper presents an embedded lightweight SSVEP-BCI electric wheelchair with a hybrid stimulator. A hybrid hardware-driven visual stimulator is designed, which combines the advantages of liquid crystal display (LCD) and light-emitting diode (LED) to achieve lower energy consumption than the traditional LCD stimulator. In addition, a lightweight BCI framework is designed to realize BCI program functions on the embedded platform for achieving similar performance as those on a personal computer. Experiments on real systems show that our embedded lightweight SSVEP-BCI electric wheelchair can be successfully operated by all eight subjects with a 93.9% average success rate of command operation. In addition, compared with the traditional LCD stimulator, the hybrid hardware-driven visual stimulator can save up to 27% of energy. [ABSTRACT FROM AUTHOR]

Satellite selection with genetic algorithm (GA) is proposed in this paper. With this method, selecting more than four satellites under multiple global navigation satellite systems (GNSS) can be achieved accurately and rapidly. By inserting the chromosome merit-based selection in the process of crossover and mutation, the local optimum can be avoided substantially when selecting satellites with GA. The GA parameters for satellite selection are chosen after a series of simulation experiments. Then the performance of satellite selection with GA is confirmed to be remarkable by use of the simulation experiments under different numbers of GNSS constellations and selected satellites. The selected satellites are optimum and the calculation time of satellite selection is acceptable. [ABSTRACT FROM AUTHOR]