METHODS: Eight electronic databases (Web of Science, PubMed, ScienceDirect, American Psychological Association PsycNet, Cochrane Library, Scopus, Embase, and Ovid) were searched for the study. Articles published from January 1 to December 31, 2022, were considered for this review. A random-effects meta-analysis and between-study heterogeneity analysis were conducted using Comprehensive Meta-Analysis V3.0 software.
RESULTS: We identified 7829 articles of which 28 met the full inclusion criteria and were included in the systematic review and analyses. Our pooled analysis suggested that participants with MCI can be differentiated from HC by significant P200, P300, and N200 latencies. The P100 and P300 amplitudes were significantly smaller in participants with MCI when compared with those in the HCs, and the patients with MCI showed increased N200 amplitudes. Our findings provide new insights into potential electrophysiological biomarkers for diagnosing MCI.
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.
SUBJECTS AND METHODS: The pure-tone audiometry (PTA) and auditory brainstem responses (ABRs) from 22 patients (44 ears) with diagnosed noise-induced permanent hearing loss were studied. Three indices of PTA were average thresholds of 0.5 kHz/, /1 kHz, and 2 kHz (PTA1); 2 kHz and 4 kHz (PTA2); and 4 kHz (PTA3) were subdivided into 3 thresholds of hearing. Their relationships with ABR results were analysed. The patterns of PTA from various groups of ABR wave patterns were studied.
RESULTS: In this study, the abnormal ABR wave patterns were detected in 72.7% of the ears. The ears with prolonged ABR wave latency, absent early waves, prolong interpeak wave I-V latency was 20.5%, 18.2%, and 21.1%, respectively. Normal ABRs were recorded in 27.3% of the ears despite marked thresholds elevation of the PTA at high frequencies. Other relationships between PTA results and ABR wave results were discussed.
CONCLUSION: There were relationships between severity of noise-induced hearing loss indicated by PTA and the patterns of ABR wave abnormalities among workers with noise-induced permanent hearing loss.
MATERIAL AND METHODS: Somatosensory evoked magnetic fields (SEFs) were elicited in 10 patients with somatosensory tumors and in 10 control participants using electrical stimulation of the median nerve via the right and left wrists. We localized the N20m component of the SEFs using dynamic statistical parametric mapping (dSPM) and standardized low-resolution brain electromagnetic tomography (sLORETA) combined with 3D magnetic resonance imaging (MRI). The obtained coordinates were compared between groups. Finally, we statistically evaluated the N20m parameters across hemispheres using non-parametric statistical tests.
RESULTS: The N20m sources were accurately localized to Brodmann area 3b in all members of the control group and in seven of the patients; however, the sources were shifted in three patients relative to locations outside the primary somatosensory cortex (SI). Compared with the affected (tumor) hemispheres in the patient group, N20m amplitudes and the strengths of the current sources were significantly lower in the unaffected hemispheres and in both hemispheres of the control group. These results were consistent for both dSPM and sLORETA approaches.
CONCLUSION: Tumors in the sensorimotor cortex lead to cortical functional reorganization and an increase in N20m amplitude and current-source strengths. Noise-normalized approaches for MEG analysis that are integrated with MRI show accurate and reliable localization of sensorimotor function.
METHODS: EEG single trials are decomposed with discrete wavelet transform (DWT) up to the [Formula: see text] level of decomposition using a biorthogonal B-spline wavelet. The coefficients of DWT in each trial are thresholded to discard sparse wavelet coefficients, while the quality of the signal is well maintained. The remaining optimum coefficients in each trial are encoded into bitstreams using Huffman coding, and the codewords are represented as a feature of the ERP signal. The performance of this method is tested with real visual ERPs of sixty-eight subjects.
RESULTS: The proposed method significantly discards the spontaneous EEG activity, extracts the single-trial visual ERPs, represents the ERP waveform into a compact bitstream as a feature, and achieves promising results in classifying the visual objects with classification performance metrics: accuracies 93.60[Formula: see text], sensitivities 93.55[Formula: see text], specificities 94.85[Formula: see text], precisions 92.50[Formula: see text], and area under the curve (AUC) 0.93[Formula: see text] using SVM and k-NN machine learning classifiers.
CONCLUSION: The proposed method suggests that the joint use of discrete wavelet transform (DWT) with Huffman coding has the potential to efficiently extract ERPs from background EEG for studying evoked responses in single-trial ERPs and classifying visual stimuli. The proposed approach has O(N) time complexity and could be implemented in real-time systems, such as the brain-computer interface (BCI), where fast detection of mental events is desired to smoothly operate a machine with minds.