A comparison of three brain-computer interfaces based on event-related desynchronization, steady state visual evoked...

Brain-computer interface (BCI) systems rely on the direct measurement of brain signals, such as event-related desynchronization (ERD), steady state visual evoked potentials (SSVEPs), P300s, or slow cortical potentials. Unfortunately, none of these BCI approaches work for all users. This study compares two conventional BCI approaches (ERD and SSVEP) within subjects, and also evaluates a novel hybrid BCI based on a combination of these signals. We recorded EEG data from 12 subjects across three conditions. In the first condition, subjects imagined moving both hands or both feet (ERD). In the second condition, subjects focused on one of the two oscillating visual stimuli (SSVEP). In the third condition, subjects simultaneously performed both tasks. We used logarithmic band power features at sites and frequencies consistent with ERD and SSVEP activity, and subjects received real-time feedback based on their performance. Subjects also completed brief questionnaires. All subjects could simultaneously perform the movement and visual task in the hybrid condition even though most subjects had little or no training. All subjects showed both SSVEP and ERD activity during the hybrid task, consistent with the activity in both single tasks. Subjects generally considered the hybrid condition moderately more difficult, but all of them were able to complete the hybrid task. Results support the hypothesis that subjects who do not have strong ERD activity might be more effective with an SSVEP BCI, and suggest that SSVEP BCIs work for more subjects. A simultaneous hybrid BCI is feasible, although the current hybrid approach, which involves combining ERD and SSVEP in a two-choice task to improve accuracy, is not significantly better than a comparable SSVEP BCI. Switching to an SSVEP BCI could increase reliability in subjects who have trouble producing the EEG activity necessary to use an ERD BCI. Subjects who are proficient in both BCI approaches might be able to combine these approaches in different ways and for different goals.