Single-Trial EEG Classification Using Logistic Regression Based on Ensemble Synchronization

Prasad, Pradeep D.; Halahalli, Harsha N.; John, John P.; Majumdar, Kaushik

doi:10.1109/jbhi.2013.2289741

Cited by 29 publications

(26 citation statements)

References 29 publications

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“…In these previous studies, two methods were used to deal with the feature spaces. In one method, all feature spaces were used in each base classifier [ 34 – 36 ]. In the other method, the feature spaces were randomly partitioned into several subspaces [ 37 – 39 ].…”

Section: Related Workmentioning

confidence: 99%

Detecting Depression Using an Ensemble Logistic Regression Model Based on Multiple Speech Features

Jiang

Liu

et al. 2018

Computational and Mathematical Methods in Medicine

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Early intervention for depression is very important to ease the disease burden, but current diagnostic methods are still limited. This study investigated automatic depressed speech classification in a sample of 170 native Chinese subjects (85 healthy controls and 85 depressed patients). The classification performances of prosodic, spectral, and glottal speech features were analyzed in recognition of depression. We proposed an ensemble logistic regression model for detecting depression (ELRDD) in speech. The logistic regression, which was superior in recognition of depression, was selected as the base classifier. This ensemble model extracted many speech features from different aspects and ensured diversity of the base classifier. ELRDD provided better classification results than the other compared classifiers. A technique for identifying depression based on ELRDD, ELRDD-E, was here suggested and tested. It offered encouraging outcomes, revealing a high accuracy level of 75.00% for females and 81.82% for males, as well as an advantageous sensitivity/specificity ratio of 79.25%/70.59% for females and 78.13%/85.29% for males.

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Section: Related Workmentioning

confidence: 99%

Detecting Depression Using an Ensemble Logistic Regression Model Based on Multiple Speech Features

Jiang

Liu

et al. 2018

Computational and Mathematical Methods in Medicine

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“…As a generalized linear model, LR is widely used in various fields including machine learning and most medical fields, which describing the probability of a binary response based on one or more predictor variables by using a logistic function (Prasad et al 2014). Two main parameters require tuning: penalty and C. Parameter penalty is used to specify the norm used in the penalization.…”

Section: Methodsmentioning

confidence: 99%

A hybrid model for EEG-based gender recognition

Wang

2019

Cogn Neurodyn

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The gender recognition is an important research field to study evidence regarding some personal characteristics in the information and data society. However, some current traditional methods such as vision and sound have been exposed their own security weaknesses. Recently, biometric gender recognition based on Electroencephalography (EEG) signals has been widely used in information safety and medical fields. It is necessary to explore potential of using EEG to present a more robust and accurate result with larger training data based on sophisticated machine learning approaches. In this contribution, we present an automated gender recognition system by a hybrid model based on EEG data of resting state from twenty-eight subjects. These data are useful and handy to get insights into assessing the differences in personal gender. For achieving a good performance and a strong robustness, the system develops a hybrid model of combining random forest and logistic regression, and employs four common entropy measures to analyze the non-stationary EEG signals. Result also suggests that the recognition performance achieve an improved progress with an accuracy of 0.9982 and AUC of 0.9926 based on a nested tenfold cross-validation loop, implying that show a significant potential applicability of the proposed approach and is capable of recognizing personal gender.

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“…If there are n number of EEG channels in a cluster an n × n symmetric matrix A n (t) is created whose ijth entry a ij (t) is the phase synchronization of ith and jth channels as calculated in Equation (3). 16 Ensemble Phase Synchronization. Ensemble phase synchronization proposes a quantitative measure to define an ordering of the synchronization matrices based on Frobenius norm of a matrix.…”

Section: Visual Screening and Extraction Of Eeg Data Epochs During Gementioning

confidence: 99%

NREM Sleep and Antiepileptic Medications Modulate Epileptiform Activity by Altering Cortical Synchrony

et al. 2018

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Single-Trial EEG Classification Using Logistic Regression Based on Ensemble Synchronization

Cited by 29 publications

References 29 publications

Detecting Depression Using an Ensemble Logistic Regression Model Based on Multiple Speech Features

Detecting Depression Using an Ensemble Logistic Regression Model Based on Multiple Speech Features

A hybrid model for EEG-based gender recognition

NREM Sleep and Antiepileptic Medications Modulate Epileptiform Activity by Altering Cortical Synchrony

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