Introducing chaos behavior to kernel relevance vector machine (RVM) for four-class EEG classification

Dong, Enzeng; Zhu, Guangxu; Chen, Chao; Tong, Jigang; Jiao, Yingjie; Du, Shengzhi

doi:10.1371/journal.pone.0198786

Cited by 14 publications

(4 citation statements)

References 42 publications

(29 reference statements)

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“…Both methods are pixel-based and further process the statistical analysis based on spectral without considering the characteristics of the image [15], [31]. The Data used as an example can predict based on the variables measured.…”

Section: Image Classification Techniquesmentioning

confidence: 99%

“…The Supervised Classification method was used with the Supervised Image Classification model through the image segmentation process using an Edge detection algorithm and Next to get the prediction value by using Spectral Angle Mapper (SAM) algorithm [11], [15], [16]. The model is proposed because it is relatively simple, which provides predictive value and uncertainty.…”

Section: Introductionmentioning

confidence: 99%

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Supervised Image Classification of Chaos Phenomenon in Cumulonimbus Cloud Using Spectral Angle Mapper

Wanayumini

Sitompul

Suwilo

et al. 2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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In the field of remote sensing, in addition to the weather forecast, atmospheric dynamics, oceans, cloud cumulonimbus, and Tornado are part of the phenomenon of chaos. Because in the clouds cumulonimbus, there are some layers with a gray border indicating irregular and uncertain. There is a boundary line on the layers of Cumulonimbus Clouds that could be identified based on the pixel where the differences in the intensity values are extremes. A cloud layer cumulonimbus with a gray edge border can be used as the basis for predicting the occurrence of a tornado based on a pixel location that has specific characteristics. In this research, a Supervised Image Classification algorithm with Spectral Angle Mapper was performed to get the minimum and maximum pixel intensity interval values based on spectral angles in cumulonimbus clouds. Spectral angles allow for quick mapping in determining the spectral similarities between two spectrums on cumulonimbus cloud layers. The spectral similarities are calculated by referring to the angle between the spectral forming the same dimensional vector space on the RGB color spectrum. Early detection in cumulonimbus cloud layers will indicate the occurrence of chaos phenomenon, which could be used to predict tornadoes. The results showed that the Spectral Angle Mapper approach gave minimum and maximum pixel intensity values interval of the Average Correlation Angle in the dataset image Cumulonimbus Cloud with a classification accuracy value of 95.83%.

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Section: Image Classification Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Supervised Image Classification of Chaos Phenomenon in Cumulonimbus Cloud Using Spectral Angle Mapper

Wanayumini

Sitompul

Suwilo

et al. 2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…Therefore, by using RVs from RVM model, it is expected to gain an in-depth understanding behind the prediction making on data samples. Since its appearance, RVM has been implemented in several studies on EEG such as in motor imagery [14,15], mental fatigue detection [16], and many more.…”

Section: Introductionmentioning

confidence: 99%

Rules Extraction of Relevance Vector Machine for Predicting Negative Emotions from EEG Signals

2022

IJIES

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Many studies have reported that patients who are experiencing long-term negative emotions have higher risk of having health deterioration. Therefore, recognition of negative emotions from Electroencephalography (EEG) signals is crucial for monitoring patient conditions. In EEG emotion recognition, clinicians tend to need a clear explanation regarding the rules behind the EEG emotion classification process. Most of the EEG emotions classification use Support Vector Machine (SVM) causing lack of probabilistic prediction which can trigger longer computation time. To address the limitation, we applied Relevance Vector Machine (RVM) with Bayesian inference algorithm to calculate the probability of predicted output. Similar to SVM, RVM was unable to provide transparent rules behind its classification. Therefore, this study attempts to extract rules from RVM by implementing Random Forest algorithm to the relevance vectors. We extract the average energy spectrum in each frequency band as the leading feature of emotions in EEG. Through the resulted rules of RVM_RF, we found that negative emotions of EEG were determined by the average energy spectrum of delta band at fronto-central electrodes (FCZ>23.683, FC4>24.812), theta band at frontal electrode (F5>23.683), and alpha band in parietal-occipital electrode (PO8>20.212). From the evaluation on three sessions of data measurement, it shows that the proposed approach of RVM_RF can predict the negative emotions of EEG with the higher average accuracy of 85.33% and average precision rate of 0.933 compared with other rule-based methods such as RVM_CN2 Rule, RVM_C4.5 Tree, and SVM. All in all, this proposed approach has demonstrated the possibility to identify negative emotions from EEG signal using rules extraction from sparse learning method, RVM.

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“…However, most of the studies on multi-class MI used multiple binary RVM. For example, Dong et aladded chaos dynamics into the kernel function of the RVM classifier in the framework of one versus one common spatial pattern (OVO-CSP) and thus made it excel in multiclass MI tasks[49]. Zhang et al combined the location of EEG dipoles with CSP to extract features from multi-class MI and extracted features were fed as the input to RVM[50].…”

mentioning

confidence: 99%

Diverse Feature Blend Based on Filter-Bank Common Spatial Pattern and Brain Functional Connectivity for Multiple Motor Imagery Detection

Wang

Tang

et al. 2020

IEEE Access

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Motor imagery (MI) based brain-computer interface (BCI) is a research hotspot and has attracted lots of attention. Within this research topic, multiple MI classification is a challenge due to the difficulties caused by time-varying spatial features across different individuals. To deal with this challenge, we tried to fuse brain functional connectivity (BFC) and one-versus-the-rest filter-bank common spatial pattern (OVR-FBCSP) to improve the robustness of classification. The BFC features were extracted by phase locking value (PLV), representing the brain interregional interactions relevant to the MI, whilst the OVR-FBCSP is used to extract the spatial-frequency features related to the MI. These diverse features were then fed into a multi-kernel relevance vector machine (MK-RVM). The dataset with three motor imagery tasks (left hand MI, right hand MI, and feet MI) was used to assess the proposed method. Experimental results not only showed that the cascade structure of diverse feature fusion and MK-RVM achieved satisfactory classification performance (average accuracy: 83.81%, average kappa: 0.76), but also demonstrated that BFC plays a supplementary role in the MI classification. Moreover, the proposed method has a potential to be integrated into multiple MI online detection owing to the advantage of strong timeefficiency of RVM. INDEX TERMS Multiple motor imagery, filter-bank common spatial pattern (FBCSP), phase locking value (PLV), brain functional connectivity (BFC), multi-kernel relevance vector machine (MK-RVM).

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Introducing chaos behavior to kernel relevance vector machine (RVM) for four-class EEG classification

Cited by 14 publications

References 42 publications

Supervised Image Classification of Chaos Phenomenon in Cumulonimbus Cloud Using Spectral Angle Mapper

Supervised Image Classification of Chaos Phenomenon in Cumulonimbus Cloud Using Spectral Angle Mapper

Rules Extraction of Relevance Vector Machine for Predicting Negative Emotions from EEG Signals

Diverse Feature Blend Based on Filter-Bank Common Spatial Pattern and Brain Functional Connectivity for Multiple Motor Imagery Detection

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