TY - GEN
T1 - Classifying Known/Unknown Information in the Brain using Electroencephalography (EEG) Signal Analysis
AU - Farizal, Ahmad
AU - Wibawa, Adhi Dharma
AU - Pamungkas, Yuri
AU - Pratiwi, Monica
AU - Mas, Arbintoro
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Electroencephalography (EEG) was studied as another method for detecting in addition to existing polygraph tools, on the premise that EEG signals are more difficult to fool than physiological responses to polygraphs. In the interrogation process carried out by the authorities with the aim of extracting memories and information whether the suspect or witness knows or does not know the object being clarified. But the suspect will also try to cover up the truth by lying and deception. This research is focused on finding spatial patterns of statistical parameters by analyzing EEG signals when the defendant or witness is given a stimulus object in the form of an image. The EEG channels used were T3, T4, T5, T6, 01, and O2. The six channels are analyzed for the alpha, beta, and gamma EEG-subband to find the average of mean, MAV and STD values which will be used as parameters to classify spatial known/unknown objects in the image as a stimulus. The results of this study indicate that there is a pattern where the EEG features in unknown conditions tend to be higher compared to the known condition. Furthermore, the EEG signal data is classified using 4 machine learning algorithms namely Naïve Bayes, SVM, KNN, and Neural Network. Thus, the optimum result was obtained by the KNN algorithm with 87% of accuracy.
AB - Electroencephalography (EEG) was studied as another method for detecting in addition to existing polygraph tools, on the premise that EEG signals are more difficult to fool than physiological responses to polygraphs. In the interrogation process carried out by the authorities with the aim of extracting memories and information whether the suspect or witness knows or does not know the object being clarified. But the suspect will also try to cover up the truth by lying and deception. This research is focused on finding spatial patterns of statistical parameters by analyzing EEG signals when the defendant or witness is given a stimulus object in the form of an image. The EEG channels used were T3, T4, T5, T6, 01, and O2. The six channels are analyzed for the alpha, beta, and gamma EEG-subband to find the average of mean, MAV and STD values which will be used as parameters to classify spatial known/unknown objects in the image as a stimulus. The results of this study indicate that there is a pattern where the EEG features in unknown conditions tend to be higher compared to the known condition. Furthermore, the EEG signal data is classified using 4 machine learning algorithms namely Naïve Bayes, SVM, KNN, and Neural Network. Thus, the optimum result was obtained by the KNN algorithm with 87% of accuracy.
KW - Brain Memory
KW - Deception
KW - EEG-based Lie Detector
KW - Interrogation
KW - Machine Learning
UR - http://www.scopus.com/inward/record.url?scp=85140598522&partnerID=8YFLogxK
U2 - 10.1109/EECCIS54468.2022.9902928
DO - 10.1109/EECCIS54468.2022.9902928
M3 - Conference contribution
AN - SCOPUS:85140598522
T3 - Proceedings - 11th Electrical Power, Electronics, Communications, Control, and Informatics Seminar, EECCIS 2022
SP - 362
EP - 367
BT - Proceedings - 11th Electrical Power, Electronics, Communications, Control, and Informatics Seminar, EECCIS 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th Electrical Power, Electronics, Communications, Control, and Informatics Seminar, EECCIS 2022
Y2 - 23 August 2022 through 25 August 2022
ER -
