How can we apply AI and Machine Learning to EEG data? There is evidence that EEG characteristics can be used as an indication (a biomarker) of some diseases. For example, in a project funded by The Michael J. Fox Foundation, our findings indicate that there are significant differences in the EEG data of different RBD patients compared to healthy populations. More specifically, RBD subjects as a group had larger power in the frontal EEG electrodes than healthy subjects. Again taken as a group. Therefore, there is statistical significance in the difference between one group and the other.
However, if we want to use this as a means for diagnosis, we need to take into account that diagnostic decisions are made on individuals, not on groups. For this to happen we need a decision system. We would input the data of a particular individual subject. Then we would get an answer on whether this individual is likely to develop, for instance, a neurodegenerative disease. Here is where Machine Learning and Deep Learning come into play.
For more information about BCI/EEG press here.
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