Exploring gaze-motor imagery hybrid brain-computer interface design

Non-invasive Brain-Computer Interface (BCI) has appeared as a new hope for a large population of disabled people, who were waiting for a new communication means that would translate some brain responses into actions. After several decades of research in fields such as neuroscience and machine learning, the performance remains too low due to the low signal to noise ratio of the EEG signal, and the time that has to be dedicated to the recording of the brain responses. Hybrid BCIs consider the combination of several modalities, including brain responses, for new communication systems. The creation of a Hybrid BCI requires particular care as it possesses the constraints from several modalities. We propose to investigate the performance that could be achieved in a paradigm, where gaze control is used for the selection of an item on a computer screen and motor imagery is used to enable the selected item on the screen. Based on the results obtained from gaze detection with an eye tracker, and motor imagery detection with non-invasive EEG recording, we show that the performance of a parallel Hybrid BCI is only beneficial if the accuracy of each modality reaches a particular limit, and if the number of commands from each modality is carefully chosen.