F-score Based EEG Channel Selection Methods for Emotion Recognition

Zhao, Chengjian; Li, Cancheng; Chao, Jinlong; Wang, Tao; Chang, Ling‐Yi; Liu, Juan; Peng, Hong

doi:10.1109/healthcom49281.2021.9399019

Cited by 4 publications

(4 citation statements)

References 14 publications

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“…

where corr is the Pearson correlation coefficient, and x i and x j are the EEG data collected from a single lead for correct and error trials, respectively. Evaluation index based on the F-score [ 32 ]: The F-score is a feature importance evaluation criterion based on inter-class and intra-class distance, which can effectively measure the feature in the realization of a binary classification problem. After calculating the F-score of each sampling point of the EEG data, the mean and maximum values of each lead were utilized to calculate the evaluation indexes:

where n + and n − are the numbers of two types of samples;

, and

represent the mean of the i-th feature on the whole dataset, the mean of the positive dataset, and the mean of the negative dataset, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…(2) Evaluation index based on the F-score [32]: The F-score is a feature importance evaluation criterion based on inter-class and intra-class distance, which can effectively measure the feature in the realization of a binary classification problem. After calculating the F-score of each sampling point of the EEG data, the mean and maximum values of each lead were utilized to calculate the evaluation indexes:…”

Section: Screening Methods For Excellent Errp-bci Subjectsmentioning

confidence: 99%

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Identification and Verification of Error-Related Potentials Based on Cerebellar Targets

Niu,

Yan,

Yin

et al. 2024

Brain Sciences

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The error-related potential (ErrP) is a weak explicit representation of the human brain for individual wrong behaviors. Previously, ErrP-related research usually focused on the design of automatic correction and the error correction mechanisms of high-risk pipeline-type judgment systems. Mounting evidence suggests that the cerebellum plays an important role in various cognitive processes. Thus, this study introduced cerebellar information to enhance the online classification effect of error-related potentials. We introduced cerebellar regional characteristics and improved discriminative canonical pattern matching (DCPM) in terms of data training and model building. In addition, this study focused on the application value and significance of cerebellar error-related potential characterization in the selection of excellent ErrP-BCI subjects (brain–computer interface). Here, we studied a specific ErrP, the so-called feedback ErrP. Thirty participants participated in this study. The comparative experiments showed that the improved DCPM classification algorithm proposed in this paper improved the balance accuracy by approximately 5–10% compared with the original algorithm. In addition, a correlation analysis was conducted between the error-related potential indicators of each brain region and the classification effect of feedback ErrP-BCI data, and the Fisher coefficient of the cerebellar region was determined as the quantitative screening index of the subjects. The screened subjects were superior to other subjects in the performance of the classification algorithm, and the performance of the classification algorithm was improved by up to 10%.

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“…

where n + and n − are the numbers of two types of samples;

, and

represent the mean of the i-th feature on the whole dataset, the mean of the positive dataset, and the mean of the negative dataset, respectively.…”

Section: Methodsmentioning

confidence: 99%

Section: Screening Methods For Excellent Errp-bci Subjectsmentioning

confidence: 99%

Identification and Verification of Error-Related Potentials Based on Cerebellar Targets

Niu,

Yan,

Yin

et al. 2024

Brain Sciences

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show abstract

“…The rate at which true positives are classified is often referred to as the true positive rate. According to Equation (5), support measures each label’s actual replies [ 40 ].

…”

Section: Methodsmentioning

confidence: 99%

“…The F1 score includes both recall and accuracy [ 40 ]. It may be determined using the harmonic mean of both measurements.…”

Section: Methodsmentioning

confidence: 99%

An Innovative Random-Forest-Based Model to Assess the Health Impacts of Regular Commuting Using Non-Invasive Wearable Sensors

Sharif

Tamang

et al. 2023

Sensors

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Regular commutes to work can cause chronic stress, which in turn can cause a physical and emotional reaction. The recognition of mental stress in its earliest stages is very necessary for effective clinical treatment. This study investigated the impact of commuting on human health based on qualitative and quantitative measures. The quantitative measures included electroencephalography (EEG) and blood pressure (BP), as well as weather temperature, while qualitative measures were established from the PANAS questionnaire, and included age, height, medication, alcohol status, weight, and smoking status. This study recruited 45 (n) healthy adults, including 18 female and 27 male participants. The modes of commute were bus (n = 8), driving (n = 6), cycling (n = 7), train (n = 9), tube (n = 13), and both bus and train (n = 2). The participants wore non-invasive wearable biosensor technology to measure EEG and blood pressure during their morning commute for 5 days in a row. A correlation analysis was applied to find the significant features associated with stress, as measured by a reduction in positive ratings in the PANAS. This study created a prediction model using random forest, support vector machine, naive Bayes, and K-nearest neighbor. The research results show that blood pressure and EEG beta waves were significantly increased, and the positive PANAS rating decreased from 34.73 to 28.60. The experiments revealed that measured systolic blood pressure was higher post commute than before the commute. For EEG waves, the model shows that the EEG beta low power exceeded alpha low power after the commute. Having a fusion of several modified decision trees within the random forest helped increase the performance of the developed model remarkably. Significant promising results were achieved using random forest with an accuracy of 91%, while K-nearest neighbor, support vector machine, and naive Bayes performed with an accuracy of 80%, 80%, and 73%, respectively.

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A Swarm Intelligence Approach: Combination of Different EEG-Channel Optimization Techniques to Enhance Emotion Recognition

Balic

Kleybolte

Märtin

2022

Human-Computer Interaction. Technological Innovation

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F-score Based EEG Channel Selection Methods for Emotion Recognition

Cited by 4 publications

References 14 publications

Identification and Verification of Error-Related Potentials Based on Cerebellar Targets

Identification and Verification of Error-Related Potentials Based on Cerebellar Targets

An Innovative Random-Forest-Based Model to Assess the Health Impacts of Regular Commuting Using Non-Invasive Wearable Sensors

A Swarm Intelligence Approach: Combination of Different EEG-Channel Optimization Techniques to Enhance Emotion Recognition

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