EEG (Electro Encephalogram) detects MS with 100% accuracy

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A simplified method for relapsing-remitting multiple sclerosis detection: Insights from resting EEG signals

https://www.sciencedirect.com/science/a ... 2524008138

Highlights

• Introduction of the first healthy-MS classification method achieving 100% accuracy using only two channels of EEG signals obtained during rest.

• Utilization of band powers, band power ratios, and relative powers of subbands obtained through the Fast Fourier Transform (FFT) during the resting state for classification purposes.

• Implementation of subject-independent classification through Leave-One Subject Out Cross Validation, ensuring robustness and generalizability of the classification model.

• Comprehensive classification of all possible channel pairs in EEG using various algorithms such as LDA, SVM, CART, and kNN, followed by comparison of their performances to determine the most effective approach.