USING THE K-NEAREST NEIGHBOR METHOD TO CATEGORIZE EEG DATA IN A CONCEALED INFORMATION TEST

Abdurashidova K.; Kurbonboev Kh.

doi:10.5281/zenodo.10396067

USING THE K-NEAREST NEIGHBOR METHOD TO CATEGORIZE EEG DATA IN A CONCEALED INFORMATION TEST

16.12.2023 International Scientific Journal "Science and Innovation". Series A. Volume 2 Issue 12

Abdurashidova K., Kurbonboev Kh.

Abstract. Electrical activity from the brain is captured using electrodes on the scalp. The primary goal is to differentiate EEG data between innocence and guilt. Data from 10 individuals has been collected. The raw EEG signals undergo signal processing via a band-pass filter. The crucial step involves extracting key features from these processed EEG signals, focusing on statistical parameters like mobility, activity, and complexity in the time domain. The classification into guilty or innocent categories is achieved using a k-nearest neighbor classifier. To validate the accuracy of the deception detection system, a 5-fold cross-validation method is applied to each subject. Performance metrics such as accuracy, sensitivity, and specificity are used to assess the classifier's effectiveness. Among three Hjorth parameters tested, mobility demonstrated the highest classification accuracy, reaching up to 96.7%.

Keywords: signal, key, sensitivity, accuracy

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