No abstract available.
Electroencephalography*

Electroencephalography

No abstract available.
Electroencephalography*

No abstract available.
Electroencephalography*

Objective: Clozapine is seen as a gold standard for treatment refractory schizophrenia; however, it is not recommended for the treatment of first-episode psychosis mainly due to concerns of severe side effects. An indicator for response holds tremendous clinical value to select patients who can benefit from clozapine, safely. EEG abnormality has been reported to be one such parameter, yet the definite conclusion of the nature of EEG changes and its predictive value remains undetermined. The present study was undertaken to examine electroencephalographic (EEG) abnormalities and clinical response subsequent to clozapine therapy in schizophrenia. Methods: A total of 80 first-episode patients were recruited for a 12 week study, from a tertiary care centre in Mumbai, India. First episode was defined as an illness of less than 2 years duration and first hospitalization since illness. EEG abnormalities, psychopathology, and positive and negative symptoms were examined at baseline and again after 12 weeks of clozapine treatment. Results: There were some types of EEG abnormalities found in pretreatment state in at least two-thirds of patients. The number of patients showing EEG abnormality at the end of the 12 weeks of treatment increased significantly, which included theta (θ) frequency, slow waves, and sharp waves. Symptomatically, significant improvement was seen in both positive as well as negative symptoms scores. However, there was no significant correlation between EEG changes and clinical outcome. Conclusion: There was significant abnormality in the number of patients exhibiting EEG abnormalities. Baseline pretreatment EEG abnormalities were present in a considerable number of patients, and these EEG abnormalities did not significantly correlate with clinical improvement, except suggesting a trend towards such correlation.
Electroencephalography

Biometrics plays an important role in information society. As a new type of biometrics, electroencephalogram (EEG) signals have special advantages in terms of versatility, durability, and safety. At present, the researches on individual identification approaches based on EEG signals draw lots of attention. Identity feature extraction is an important step to achieve good identification performance. How to combine the characteristics of EEG data to better extract the difference information in EEG signals is a research hotspots in the field of identity identification based on EEG in recent years. This article reviewed the commonly used identity feature extraction methods based on EEG signals, including single-channel features, inter-channel features, deep learning methods and spatial filter-based feature extraction methods, etc. and explained the basic principles application methods and related achievements of various feature extraction methods. Finally, we summarized the current problems and forecast the development trend.
Electroencephalography

No abstract available.
Electroencephalography

Background& Objective: Investigation modalities, such as MRI and CSF examination, are neither sensitive nor specific in the early phase of anti-NMDAR encephalitis. Nuclear imaging may be useful to monitor the response to treatment but limited by the availability.We aimed to determine the role of EEG as a tool for early diagnosis as well as a tool to assess disease progression and response to treatment. Methods: A total of 99 EEGsdone in 16 patients diagnosed with anti-NMDAR encephalitis throughout the course of illness, were reviewed retrospectively. The EEG changes were correlated with the clinical presentations and response to treatment. Sixteen EEGs of patients with schizophrenia and mood disorder, and 10 EEGs of patients with infective encephalitis were included as control. Results: EEGs performed during the psychiatric and cognitive dysfunctionphase in patient with anti-NMDAR encephalitis, showed diffuse background slowing in the delta-theta range in all the patients. Serial EEGs showed that the dominant background frequency improved with improvement in cognitive status. Nine patients had complete recovery with normalisation of the EEG abnormalities. Eight patients had their typical clinical seizure recorded during EEG monitoring, but only 2 (25.0%) with EEG correlation. Ten patients had status epilepticus (62.5%), 5 had EEG recorded during their status epilepticus, of which only one with EEG correlation (20.0%). Eleven patients had asymmetric background (68.8%), but only 1 has correlation with focal changes in the MRI brain (9.1%). Even though the EEGs of patients with infective encephalitis also showed background slowing, their CSF analysis was supportive of an infective cause. EEGs of patients with established psychiatric disorder were within normal limits. Conclusion: EEG abnormality has a good correlation with the degree of psychiatric and cognitive dysfunction in patient with anti-NMDAR encephalitis, and is useful in early diagnosis, monitoring the progress and the response to treatment. However, it has poor correlation with clinical seizures.
Electroencephalography

No abstract available.
Electroencephalography* ; Epilepsy*

No abstract available.
Electroencephalography* ; Scalp*