Brain-computer interfaces (BCIs) provide a viable alternative means of communication for persons with severe disabilities who may not have access to functional speech or gesture. BCIs based on electroencephalography (EEG) sense the electrical activity that arises from the brain via electrodes placed on top of the head. A BCI speller allows people to select characters of the alphabet sequentially, simply by focusing their attention on the desired character amidst an array of characters. These characters can be displayed on a computer screen and illuminated rhythmically and in random order. The characters can alternatively be encoded and presented to the BCI user in the form physical vibrations. While BCI spellers show great promise for providing individuals with severe disability with access to communication, they require improved speed and accuracy to be effective. To improve the speed and accuracy, we propose to combine the information from visual and tactile BCIs which should enable shorter stimulus presentation times resulting in faster communication. Additionally, we will detect the error-related potential (ErrP) that is elicited in participant's EEG signal when the BCI chooses the incorrect character and use this to adapt the BCI speller. This should reduce the number of mistakes made by the BCI speller.