METHODS: Thirty-eight participants were divided into control (n = 19) and MBI (n = 19) groups. Control participants were normal, healthy people, and participants with MBI were assigned into two groups: MBI 1st Test (7 days after a road traffic accident (RTA)) and MBI 2nd Test (2-6 months after RTA with the same participants of the 1st Test group). The 128-ERP nets were used on the heads of the participants during the experiments. Under the auditory oddball paradigm, all participants silently counted the target tones, while ignoring the standard tones. This study used the sLORETA tool in the Net Station software for the source localization of the P300 ERP component. The Mann-Whitney U test was used to compare intensities between groups, while the Wilcoxon Signed-Rank test was applied for paired observations within groups.
RESULTS: Standard stimuli evoked P300 sources in the superior frontal gyrus (BA11) of the right frontal lobe in the control group, the superior temporal gyrus (BA38) of the right temporal lobe in the MBI 1st Test group, and the inferior frontal gyrus (BA47) of the left frontal lobe in the MBI 2nd Test group. Meanwhile, target stimuli evoked P300 sources at BA11 for all groups but in different gyrus: the superior frontal gyrus, orbital gyrus, and rectal gyrus in the control, MBI 1st Test, and MBI 2nd Test groups, respectively. In addition, there were significant differences in dipole intensities between and within groups among control and MBI patients in both standard and target stimuli.
CONCLUSION: P300 source localization was shifted presumably due to the auditory cognitive impairment, and the dipole intensities were significantly higher in the MBI group than in the control group, indicating that the MBI group compensated for both standard and target tone stimuli, reflected in the sLORETA investigation.
MATERIALS AND METHODS: The EEG signal was used as a brain response signal, which was evoked by two auditory stimuli (Tones and Consonant Vowels stimulus). The study was carried out on Malaysians (Malay and Chinese) with normal hearing and with hearing loss. A ranking process for the subjects' EEG data and the nonlinear features was used to obtain the maximum classification accuracy.
RESULTS: The study formulated the classification of Normal Hearing Ethnicity Index and Sensorineural Hearing Loss Ethnicity Index. These indices classified the human ethnicity according to brain auditory responses by using numerical values of response signal features. Three classification algorithms were used to verify the human ethnicity. Support Vector Machine (SVM) classified the human ethnicity with an accuracy of 90% in the cases of normal hearing and sensorineural hearing loss (SNHL); the SVM classified with an accuracy of 84%.
CONCLUSION: The classification indices categorized or separated the human ethnicity in both hearing cases of normal hearing and SNHL with high accuracy. The SVM classifier provided a good accuracy in the classification of the auditory brain responses. The proposed indices might constitute valuable tools for the classification of the brain responses according to the human ethnicity.