Evaluating Different Graph Learning Techniques for Mental Task EEG Signal Classification

Graph learning from the brain signals deals with capturing the changes in functional relationship between the brain regions during mental active and relaxed states. This paper investigates different graph learning techniques, namely geometry, signal similarity, and Graphical LASSO based methods for...

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Bibliographic Details
Main Authors: Mathur, Priyanka, Chakka, Vijay Kumar
Format: Conference Proceeding
Language:English
Subjects:
Conferences
Electroencephalogram(EEG)
Electroencephalography
Geometry
Geometry based graph learning
Graph Signal Processing (GSP)
Graph Spectral Energy
Graphical LASSO
Measurement
Pattern classification
Signal processing
Similarity based graph learning
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Summary:Graph learning from the brain signals deals with capturing the changes in functional relationship between the brain regions during mental active and relaxed states. This paper investigates different graph learning techniques, namely geometry, signal similarity, and Graphical LASSO based methods for the classification of mental task from electroencephalogram (EEG) signals. Graph spectral energy based metric using Graph Signal Processing (GSP) technique is presented to classify mental active state from relaxed state. A binary KNN classifier is used to analyse each graph learning technique on publicly available Keirn and Aunon mental task EEG database. Performance of different graphs is then analysed and compared using classification Accuracy and F-Score.
ISSN:2325-9418
DOI:10.1109/INDICON52576.2021.9691598