| Electroencephalography (EEG) has been used since the 1920s. In the past few decades, research has been based on averaging trials to provide information on evoked response potentials. Here, I study both average and individual trials. The analysis is formulated as a classification task with supervised learning using multichannel EEG data. Category labels were assigned to each stimulus. Both visual and auditory stimuli were presented, including images, syllables, words and sentences---all related to natural language.; For both average and individual trial classifications, significant rates were obtained with p-values less than 10-10. These results serve as good evidence for the existence of a brain wave representation for words and sentences. By combining a multichannel multi-output perceptron with Independent Component Analysis, I was able to further improve the already significant single channel results, both strengthening the evidence and opening up possibilities for brain-computer interface applications. The details of various machine learning techniques, statistical evaluations and parameter interpretations were discussed in this manuscript. |
