The Strange and Promising Relationship Between EEG and AI Methods of Analysis

Nowadays, the utility of AI methods for classifying EEG data is widespread in research laboratories. The constant in this field of research is to find out a suite method for differentiating EEG data accurately. The principal methods of AI used in EEG data analysis are machine learning and deep learning. In this article, we explore the scope of AI in light of the results in EEG analysis data. We begin presenting the scope of computing analysis to set up the context for understanding the procedures of algorithms applied by AI to classify EEG data. Next, we review the achievements of AI classification algorithms to some cases of EEG data. With the result of this, we analyze and better understand the contribution of AI to the epistemology of neuroscience, with special regard to EEG brain imaging neuroscience. Finally, we will show some learnings from this analysis, in which we argue, emerge a fundamental lesson from AI analysis of EEG data to theoretical neuroscience, namely when it is about brain imaging, the need for convergent scientific methods rises the question about the unity of (neuro)science. This opens the possibility of multi-approaches to be the major feature of current practice of this science field. Hence, applications of current AI methods for analyzing brain functioning advance the epistemology of neuroscience to a paradigm from localizing to dynamic representation of data.