Classification of Imagined Speech Signals Using Functional Connectivity Graphs and Machine Learning Models.

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Anand Mohan, RS Anand This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4809756/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Jan, 2025 Read the published version in Brain Topography → Version 1 posted 11 You are reading this latest preprint version Abstract EEG involves recording electrical activity generated by the brain through electrodes placed on the scalp. Imagined speech classification has emerged as an essential area of research in brain-computer interfaces (BCIs). Despite significant advances, accurately classifying imagined speech signals remains challenging due to their complex and non-stationary nature. Existing approaches often struggle with low signal-to-noise ratios and high inter-subject variability. A novel method named imagined speech functional connectivity graph (ISFCG) is implemented to deal with these issues. The functional connectivity graphs capture the complex relationships between brain regions during imagined speech tasks. These graphs are then used to extract features that serve as inputs to various machine-learning models. The ISFCG addresses the limitations of traditional methods by providing a more robust representation of imagined speech signals. Also, a convolutional neural network (CNN) is proposed to learn features from these complex graphs, leading to improved classification accuracy. Experimental results on a benchmark dataset demonstrate the effectiveness of our method. EEG Imagined Speech Brain Connectivity Deep Learning CNN Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 28 Jan, 2025 Read the published version in Brain Topography → Version 1 posted Editorial decision: Revision requested 23 Sep, 2024 Reviews received at journal 18 Sep, 2024 Reviews received at journal 11 Sep, 2024 Reviews received at journal 10 Sep, 2024 Reviewers agreed at journal 22 Aug, 2024 Reviewers agreed at journal 19 Aug, 2024 Reviewers agreed at journal 19 Aug, 2024 Reviewers invited by journal 19 Aug, 2024 Editor assigned by journal 27 Jul, 2024 Submission checks completed at journal 27 Jul, 2024 First submitted to journal 26 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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