Classification of fNIRS signals for deception decoding using LDA and SVM

Recent advancements in the field of neuroimaging have demonstrated that functional near-infrared spectroscopy (fNIRS) has a great potential for deception decoding. fNIRS is relatively new non-invasive optical imaging technique that has the advantages of safety, low cost, portability and easy to use. Light in the range of near infrared is used in fNIRS to measure the hemoglobin concentrations and thus determine the neural activity. In this paper we have compared the abilities of two classification techniques: linear discriminant analysis (LDA) and support vector machine (SVM), to classify the fNIRS data for deception decoding. Five healthy male subjects participated in deception and truth-telling scenarios separately in this study. Signals from the prefrontal cortex of the subjects were collected using continuous wave fNIRS. HbO and HbR signals were used to define the features and then data was classified using LDA and SVM classifiers. The average classification accuracy of SVM is 87.33 % whereas average classification accuracy for LDA is 78.34 %. The higher classification accuracy resulting from SVM is in accordance with the previous literature.