Online analogue steady state visually evoked potential brain computer interface for lower limb rehabilitation

Steady state visual evoked potentials (SSVEP) are less vulnerable to noise than other kinds of electroencephalography (EEG) signals and have therefore recently become popular in brain computer interface (BCI) applications. This paper firstly demonstrates an online asynchronous analogue (variable level) SSVEP-based BCI for lower limb rehabilitation in which the movement of robotic exoskeleton is continuously controlled by the user's intent. Such patient participation has proved to be one of the most important factors for rehabilitating the neural system after injury or stroke. Three new and different training protocols are developed specially for rehabilitation exercise and tested with the proposed adjacent narrow band-pass filter (ANBF) method. Results with six participants are presented with accuracy to within a knee angle of 1° after simple training. For the ANBF method with 0.3 Hz filter spans, the overall average recognition accuracy is 95.98% ± 4.15% and the overall average net latency is 2.84 ± 0.61 seconds. For the ANBF method with 0.1 Hz filter spans, the overall average recognition accuracy is 98.91% ± 1.50% and the overall average net latency is 4.29% ± 0.50% seconds. This gives much promise to future development of brain controlled rehabilitation devices.