Materials and Methods: Simple and complex sounds were used (pure tones and the naturally produced Malay consonant-vowels [CVs]) to evoke the cortical auditory-evoked potential (CAEP) signals. Moreover, this study analyzed the influence of related CAEP components that are distinct to the selected population and determined which of the ERP components among (CAEP) components is most affected by the two distinct stimuli. Moreover, the study used classification algorithms to discover the ability of the brain in distinguishing CAEP evoked by stimuli contrasts.
Results: The results showed some resemblance between our results and ERP waveforms outlined in previous studies conducted on native speakers of English. On the other hand, it was also observed that the P1 and N2 had a significant effect in amplitude due to different stimulus.
Conclusion: The results show high classification accuracy for the brain to distinguish auditory stimuli. Moreover, the results indicated some resemblance to previous studies conducted on native English speakers using similar tones and English CV stimuli. However, the amplitudes and latencies of the P1 were found to have a significant difference due to stimuli complexity.
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.
METHODS: In this study, the frequency and causes of line of sight issues is assessed using recordings of Navigation probe locations and its synchronised video recordings.
RESULTS: The mentioned experiment conducted for a series of 15 neurosurgical operations. This issue occured in all these surgeries except one. Maximum duration of issue presisting reached up to 56% of the navigation usage time.
CONCLUSIONS: The arrangment of staff and equipment is a key factor in avoiding this issue.
METHODS: Two 3D printed models were designed and fabricated using actual patient imaging data with reference marker points embedded artificially within these models that were then registered to a surgical navigation system using 3 different methods. The first method uses a conventional manual registration, using the actual patient's imaging data. The second method is done by directly scanning the created model using intraoperative computed tomography followed by registering the model to a new imaging dataset manually. The third is similar to the second method of scanning the model but eventually uses an automatic registration technique. The errors for each experiment were then calculated based on the distance of the surgical navigation probe from the respective positions of the embedded marker points.
RESULTS: Errors were found in the preparation and printing techniques, largely depending on the orientation of the printed segment and postprocessing, but these were relatively small. Larger errors were noted based on a couple of variables: if the models were registered using the original patient imaging data as opposed to using the imaging data from directly scanning the model (1.28 mm vs. 1.082 mm), and the accuracy was best using the automated registration techniques (0.74 mm).
CONCLUSION: Spatial accuracy errors occur consistently in every 3D fabricated model. These errors are derived from the fabrication process, the image registration process, and the surgical process of registration.