Identifying Clinically and Functionally Distinct Groups Among Healthy Controls and First Episode Psychosis Patients by Clustering on EEG Patterns

@article{Qu2020IdentifyingCA,
  title={Identifying Clinically and Functionally Distinct Groups Among Healthy Controls and First Episode Psychosis Patients by Clustering on EEG Patterns},
  author={Xiaodong Qu and Saran Liukasemsarn and Jingxuan Tu and Amy Higgins and T. Hickey and Mei-Hua Hall},
  journal={Frontiers in Psychiatry},
  year={2020},
  volume={11}
}
Objective The mismatch negativity (MMN) is considered as a promising biomarker that can inform future therapeutic studies. However, there is a large variability among patients with first episode psychosis (FEP). Also, most studies report a single electrode site and on comparing case–control group differences. Few have taken advantage of the full wealth of multi-channel EEG signals to examine observable patterns. None, to our knowledge, have used machine learning (ML) approaches to investigate… 

EEG-Based Measures in At-Risk Mental State and Early Stages of Schizophrenia: A Systematic Review

Multiple EEG-indices were altered during at-risk mental state and early stages of schizophrenia, supporting the hypothesis that cerebral network dysfunctions appear already before the onset of the disorder.

Unveiling the Associations between EEG Indices and Cognitive Deficits in Schizophrenia-Spectrum Disorders: A Systematic Review

It is suggested that machine learning approaches together with a careful selection of validated EEG and cognitive indices and characterization of clinical phenotypes might contribute to increase the use of EEG-based measures in clinical settings.

EEG4Students: An Experimental Design for EEG Data Collection and Machine Learning Analysis

This paper explores machine learning algorithms that can run efficiently on personal computers for BCI classification tasks and develops the data collection protocol, EEG4Students, that grants non-experts who are interested in a guideline for such data collection.

BrainActivity1: A Framework of EEG Data Collection and Machine Learning Analysis for College Students

A protocol is developed that grants non-experts who are interested a guideline for such data collection during BCI classification tasks and shows that Random Forest and RBF SVM performed well for EEG classification tasks.

Vector-Based Data Improves Left-Right Eye-Tracking Classifier Performance After a Covariate Distributional Shift

This study trains machine learning models utilizing both coarse-grain and fine-grain data and compares their accuracies when tested on data of similar/different distributional patterns in order to determine how susceptible EEG-ET benchmarks are to difierences in distributional data.

Time Majority Voting, a PC-based EEG Classifier for Non-expert Users

A novel algorithm for machine learning called Time Majority Voting (TMV) is created that can operate e-ciently on personal computers for classification tasks involving the BCI and performed better than cutting-edge algorithms.

References

SHOWING 1-10 OF 66 REFERENCES

Longitudinal associations between mismatch negativity and disability in early schizophrenia- and affective-spectrum disorders

A longitudinal study of event related potentials and correlations with psychosocial functioning and clinical features in first episode psychosis patients.

Mismatch negativity is a breakthrough biomarker for understanding and treating psychotic disorders

It is proposed that mismatch negativity (MMN)—a neurophysiological measure of central auditory system functioning—may be informative in developing the next generation of neuroscience-guided cognitive-enhancing treatments.

Disentangling early sensory information processing deficits in schizophrenia

Duration and frequency mismatch negativity shows no progressive reduction in early stages of psychosis

Gray matter deficits, mismatch negativity, and outcomes in schizophrenia.

These findings further support the importance of MMN reduction in schizophrenia by linking frontotemporal cerebral gray matter pathology to an automatically generated event-related potential index of daily functioning.

Mismatch Negativity in First-Episode Schizophrenia

A meta-analysis on the fourteen studies that measured MMN to pitch or duration deviants in healthy controls and patients within 12 months of their first episode of schizophrenia indicates that pitch-deviant MMN is not a candidate biomarker for schizophrenia prediction, while duration-devian MMN may hold some promise, albeit nearly a third as large an effect as in chronic schizophrenia.
...