Analysis of Imagined Speech Characteristics using Phase-based Connectivity Measures

The Aim of this research is to access connectivity in cognitive functioning of brain networks during Imagination of speech prompts from electroencephalography(EEG) signals. Phase based connectivity analysis is performed to identify dominant neurophysiological dynamics of speech imagery paradigm. The...

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Bibliographic Details
Published in2023 IEEE 13th International Conference on Control System, Computing and Engineering (ICCSCE) pp. 68 - 73
Main Authors Bisla, Meenakshi, Anand, R.S
Format Conference Proceeding
LanguageEnglish
Published IEEE 25.08.2023
Subjects
Electroencephalography
Frequency measurement
imagined speech
Inspection
Interstice phase clustering
Neural networks
phase lag index
Phase measurement
phase-based connectivity
Primary motor cortex
Time-frequency analysis
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DOI10.1109/ICCSCE58721.2023.10237139

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Summary:The Aim of this research is to access connectivity in cognitive functioning of brain networks during Imagination of speech prompts from electroencephalography(EEG) signals. Phase based connectivity analysis is performed to identify dominant neurophysiological dynamics of speech imagery paradigm. The connectivity analysis is performed on Publicly available EEG based Imagined speech 'Kara one' dataset. The Connectivity matrices are generated using Interstice phase clustering(ISPC) over trials and Phase lag index(PLI) to quantify strength of connectivity between different regions of brains at different time and frequency points. For qualitative inspection, the thresholding of connectivity metrices is performed using a threshold of one standard deviation above the median connectivity. Topological Maps and time frequency plots are analysed for both ISPC-trials and PLI connectivity matrix to trace connectivity between neural networks at various time-frequency points.
DOI:10.1109/ICCSCE58721.2023.10237139