In this paper, a hybrid brain-computer interface (BCI) system combining P300 and steady-state visual evoked potential (SSVEP) is proposed to improve the performance of asynchronous control. The four groups of flickering buttons were set in the graphical user interface. Each group contained one large button in the center and eight small buttons around it, all of which flashed at a fixed frequency (e.g., 7.5 Hz) to evoke SSVEP. At the same time, the four large buttons of the four groups were intensified through shape and color changes in a random order to produce P300 potential. During the control state, the user focused on a desired group of buttons (target buttons) to evoke P300 potential and SSVEP, simultaneously. Discrimination between the control and idle states was based on the detection of both P300 and SSVEP on the same group of buttons. As an application, this method was used to produce a "go/stop" command in real-time wheelchair control. Several experiments were conducted, and data analysis results showed that combining P300 potential and SSVEP significantly improved the performance of the BCI system in terms of detection accuracy and response time.

Brain-computer interfaces (BCIs) are used to translate brain activity signals into control signals for external devices. Currently, it is difficult for BCI systems to provide the multiple independent control signals necessary for the multi-degree continuous control of a wheelchair. In this paper, we address this challenge by introducing a hybrid BCI that uses the motor imagery-based mu rhythm and the P300 potential to control a brain-actuated simulated or real wheelchair. The objective of the hybrid BCI is to provide a greater number of commands with increased accuracy to the BCI user. Our paradigm allows the user to control the direction (left or right turn) of the simulated or real wheelchair using left- or right-hand imagery. Furthermore, a hybrid manner can be used to control speed. To decelerate, the user imagines foot movement while ignoring the flashing buttons on the graphical user interface (GUI). If the user wishes to accelerate, then he/she pays attention to a specific flashing button without performing any motor imagery. Two experiments were conducted to assess the BCI control; both a simulated wheelchair in a virtual environment and a real wheelchair were tested. Subjects steered both the simulated and real wheelchairs effectively by controlling the direction and speed with our hybrid BCI system. Data analysis validated the use of our hybrid BCI system to control the direction and speed of a wheelchair.

Through the hybrid BCI system, command following was detected in four healthy subjects, two of 7 DOC patients, and one LIS patient. We suggest that the hybrid BCI system could be used as a supportive bedside tool to detect awareness in patients with DOC.

Cognitive motor dissociation describes a subset of patients with disorders of consciousness who show neuroimaging evidence of consciousness but no detectable command-following behaviours. Although essential for family counselling, decision-making, and the design of rehabilitation programmes, the prognosis for patients with cognitive motor dissociation remains under-investigated. The current study included 78 patients with disorders of consciousness who showed no detectable command-following behaviours. These patients included 45 patients with unresponsive wakefulness syndrome and 33 patients in a minimally conscious state, as diagnosed using the Coma Recovery Scale-Revised. Each patient underwent an EEG-based brain-computer interface experiment, in which he or she was instructed to perform an item-selection task (i.e. select a photograph or a number from two candidates). Patients who achieved statistically significant brain-computer interface accuracies were identified as cognitive motor dissociation. Two evaluations using the Coma Recovery Scale-Revised, one before the experiment and the other 3 months later, were carried out to measure the patients’ behavioural improvements. Among the 78 patients with disorders of consciousness, our results showed that within the unresponsive wakefulness syndrome patient group, 15 of 18 patients with cognitive motor dissociation (83.33%) regained consciousness, while only five of the other 27 unresponsive wakefulness syndrome patients without significant brain-computer interface accuracies (18.52%) regained consciousness. Furthermore, within the minimally conscious state patient group, 14 of 16 patients with cognitive motor dissociation (87.5%) showed improvements in their Coma Recovery Scale-Revised scores, whereas only four of the other 17 minimally conscious state patients without significant brain-computer interface accuracies (23.53%) had improved Coma Recovery Scale-Revised scores. Our results suggest that patients with cognitive motor dissociation have a better outcome than other patients. Our findings extend current knowledge of the prognosis for patients with cognitive motor dissociation and have important implications for brain-computer interface-based clinical diagnosis and prognosis for patients with disorders of consciousness.