EEG-based Imagined Speech Analysis using Functional Connectivity
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Imagined speech involves a network of brain regions that simulate the experience of speaking or listening, though it lacks the physical movement of the vocal apparatus or external sound. This study analyses the brain's role in the imagination of words, sentences, and vowels using an amalgamation of innovative methods, including transfer entropy, graph theory, and functional connectivity. Channels that exhibit stronger connectivity than most of the network play a crucial role in information processing or network integration. This helps analyze the most "active" or "influential" channels of imagination of any class. By selecting channels with connectivity values significantly above the average (i.e., those exceeding the mean + 1.5 standard deviations), this method ensures that you focus on the most distinctive and potentially relevant patterns in the data. The analysis is performed on the original EEG dataset acquired. The proposed analysis is also validated using public datasets (Kara one and ASU) to assess the reliability of the methodology. Overall, the findings support the hypothesis that imagined speech engages a distributed but left leaning network of regions, with task-specific patterns modulated by the complexity and phonetic structure of the stimuli.
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