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Nowadays, everybody knows what a hybrid car is. A hybrid car normally has two engines to enhance energy efficiency and reduce CO2 output. Similarly, a hybrid brain-computer interface (BCI) is composed of two BCIs, or at least one BCI and another system. A hybrid BCI, like any BCI, must fulfill the following four criteria: (i) the device must rely on signals recorded directly from the brain; (ii) there must be at least one recordable brain signal that the user can intentionally modulate to effect goal-directed behaviour; (iii) real time processing; and (iv) the user must obtain feedback. This paper introduces hybrid BCIs that have already been published or are in development. We also introduce concepts for future work. We describe BCIs that classify two EEG patterns: one is the event-related (de)synchronisation (ERD, ERS) of sensorimotor rhythms, and the other is the steady-state visual evoked potential (SSVEP). Hybrid BCIs can either process their inputs simultaneously, or operate two systems sequentially, where the first system can act as a “brain switch”. For example, we describe a hybrid BCI that simultaneously combines ERD and SSVEP BCIs. We also describe a sequential hybrid BCI, in which subjects could use a brain switch to control an SSVEP-based hand orthosis. Subjects who used this hybrid BCI exhibited about half the false positives encountered while using the SSVEP BCI alone. A brain switch can also rely on hemodynamic changes measured through near-infrared spectroscopy (NIRS). Hybrid BCIs can also use one brain signal and a different type of input. This additional input can be an electrophysiological signal such as the heart rate, or a signal from an external device such as an eye tracking system.

This document provides a review of the techniques and therapies used in gait rehabilitation after stroke. It also examines the possible benefits of including assistive robotic devices and brain-computer interfaces in this field, according to a top-down approach, in which rehabilitation is driven by neural plasticity.The methods reviewed comprise classical gait rehabilitation techniques (neurophysiological and motor learning approaches), functional electrical stimulation (FES), robotic devices, and brain-computer interfaces (BCI).From the analysis of these approaches, we can draw the following conclusions. Regarding classical rehabilitation techniques, there is insufficient evidence to state that a particular approach is more effective in promoting gait recovery than other. Combination of different rehabilitation strategies seems to be more effective than over-ground gait training alone. Robotic devices need further research to show their suitability for walking training and their effects on over-ground gait. The use of FES combined with different walking retraining strategies has shown to result in improvements in hemiplegic gait. Reports on non-invasive BCIs for stroke recovery are limited to the rehabilitation of upper limbs; however, some works suggest that there might be a common mechanism which influences upper and lower limb recovery simultaneously, independently of the limb chosen for the rehabilitation therapy. Functional near infrared spectroscopy (fNIRS) enables researchers to detect signals from specific regions of the cortex during performance of motor activities for the development of future BCIs. Future research would make possible to analyze the impact of rehabilitation on brain plasticity, in order to adapt treatment resources to meet the needs of each patient and to optimize the recovery process.

Cortical involvement during upright walking is not well-studied in humans. We analyzed non-invasive electroencephalographic (EEG) recordings from able-bodied volunteers who participated in a robot-assisted gait-training experiment. To enable functional neuroimaging during walking, we applied source modeling to high-density (120 channels) EEG recordings using individual anatomy reconstructed from structural magnetic resonance imaging scans. First, we analyzed amplitude differences between the conditions, walking and upright standing. Second, we investigated amplitude modulations related to the gait phase. During active walking upper μ (10–12 Hz) and β (18–30 Hz) oscillations were suppressed [event-related desynchronization (ERD)] compared to upright standing. Significant β ERD activity was located focally in central sensorimotor areas for 9/10 subjects. Additionally, we found that low γ (24–40 Hz) amplitudes were modulated related to the gait phase. Because there is a certain frequency band overlap between sustained β ERD and gait phase related modulations in the low γ range, these two phenomena are superimposed. Thus, we observe gait phase related amplitude modulations at a certain ERD level. We conclude that sustained μ and β ERD reflect a movement related state change of cortical excitability while gait phase related modulations in the low γ represent the motion sequence timing during gait. Interestingly, the center frequencies of sustained β ERD and gait phase modulated amplitudes were identified to be different. They may therefore be caused by different neuronal rhythms, which should be taken under consideration in future studies.