ArticleFrontiers in human neuroscience2017
Classifying Schizotypy Using an Audiovisual Emotion Perception Test and Scalp Electroencephalography.
Article in Frontiers in human neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of EEG based automated schizophrenia classification through machine learning and deep learning.Frontiers in human neuroscience · 2024Pooled it
- Prominent genetic variants and epigenetic changes in post-traumatic stress disorder among combat veterans.Molecular biology reports · 2024Review
- Assisting schizophrenia diagnosis using clinical electroencephalography and interpretable graph neural networks: a real-world and cross-site study.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2023Article
- Graph-based analysis of EEG for schizotypy classification applying flicker Ganzfeld stimulation.Schizophrenia (Heidelberg, Germany) · 2023Article
- A hybrid deep neural network for classification of schizophrenia using EEG Data.Scientific reports · 2021Article
- Effects of an Audiovisual Emotion Perception Training for Schizophrenia: A Preliminary Study.Frontiers in psychiatry · 2021Article
- Perspectives on Machine Learning for Classification of Schizotypy Using fMRI Data.Schizophrenia bulletin · 2018Article
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Authors and funding
7 authors.
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Abstract
Schizotypy refers to the personality trait of experiencing "psychotic" symptoms and can be regarded as a predisposition of schizophrenia-spectrum psychopathology (Raine, 1991). Cumulative evidence has revealed that individuals with schizotypy, as well as schizophrenia patients, have emotional processing deficits. In the present study, we investigated multimodal emotion perception in schizotypy and implemented the machine learning technique to find out whether a schizotypy group (ST) is distinguishable from a control group (NC), using electroencephalogram (EEG) signals. Forty-five subjects (30 ST and 15 NC) were divided into two groups based on their scores on a Schizotypal Personality Questionnaire. All participants performed an audiovisual emotion perception test while EEG was recorded. After the preprocessing stage, the discriminatory features were extracted using a mean subsampling technique. For an accurate estimation of covariance matrices, the shrinkage linear discriminant algorithm was used. The classification attained over 98% accuracy and zero rate of false-positive results. This method may have important clinical implications in discriminating those among the general population who have a subtle risk for schizotypy, requiring intervention in advance.
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