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EEG-based Speech Recognition: Impact of Experimental Design on Performance

Electroencephalography (EEG) has proven to be a valuable method for communication for speech-impaired persons. Recently, attempts have been made to use EEG data for the recognition of unspoken speech. Unspoken speech means that a subject thinks a given word without the use of the facial muscles and without uttering any audible sound. In spite of promising first results [40], the question has been raised in [9] whether the system may have recognized temporal patterns in the data instead of actual words since the words were presented in blocks. The contribution of this thesis is to investigate in depth the impact of experimental design on the recognition rate of unspoken speech. For this study, 23 subjects were recorded for 71 sessions in total, using a high-density EEG cap with 128 electrodes out of which 16 were recorded. The vocabulary domain consisted of 5 words each of which were repeated 20 times. Between sessions, the order in which the words were presented has been varied as well as the length of the breaks. Besides the previously used orders blocks, sequential and random, short blocks were tested consisting of blocks of 5 words. It could be shown that except for the block mode which yielded an average recognition rate of 45.50%, all other modes had recognition rates at chance level. This may be partially explained by the fact that block data contains less noise and the fact that this mode facilitates the task to think words in a consistent way. However, it could be shown that temporal artifacts indeed superimpose the signal of interest in block recordings.