An unsupervised discriminative extreme learning machine and its applications to data clustering

Peng, Yong; Zheng, Wei‐Long; Lu, Bao-Liang

doi:10.1016/j.neucom.2014.11.097

Cited by 38 publications

(23 citation statements)

References 43 publications

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“…These proposed systems aim to explore or improve EEG-based emotion recognition systems. [2,39,41,42,49,50,57,61,63,92,104,108,109,117,131,136,149,152,157,173,174,185,186,189,191,[195][196][197][198][199][200][201][202][203][204][205][206][207][208][209]217,219,[223][224][225]229,[262][263][264][265][266]<...>…”

Section: Monitoringmentioning

confidence: 99%

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Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

et al. 2017

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Abstract:Recent developments and studies in brain-computer interface (BCI) technologies have facilitated emotion detection and classification. Many BCI studies have sought to investigate, detect, and recognize participants' emotional affective states. The applied domains for these studies are varied, and include such fields as communication, education, entertainment, and medicine. To understand trends in electroencephalography (EEG)-based emotion recognition system research and to provide practitioners and researchers with insights into and future directions for emotion recognition systems, this study set out to review published articles on emotion detection, recognition, and classification. The study also reviews current and future trends and discusses how these trends may impact researchers and practitioners alike. We reviewed 285 articles, of which 160 were refereed journal articles that were published since the inception of affective computing research. The articles were classified based on a scheme consisting of two categories: research orientation and domains/applications. Our results show considerable growth of EEG-based emotion detection journal publications. This growth reflects an increased research interest in EEG-based emotion detection as a salient and legitimate research area. Such factors as the proliferation of wireless EEG devices, advances in computational intelligence techniques, and machine learning spurred this growth.

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Section: Monitoringmentioning

confidence: 99%

“…Recently, EEG-based emotion recognition was proposed as a technique that can be used to support a classification task, such as the EEG-based emotional state clustering task [50], image classification [219], and Odor Pleasantness Classification Using Brain and Peripheral Signals [26].…”

Section: Domain Description Referencesmentioning

confidence: 99%

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

et al. 2017

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“…We selected the embedding dimension d from a list of [3,5,15,20], the number of nearest neighbors k from [5,10,15,20,25,30] and the standard deviation of the ILR Gaussian shaped local density σ lapse from [2,3,5,10] times the mean trial interval. We iterated over all combinations of these hyperparameters and selected the best one according to the goodness of fit (5).…”

Section: Resultsmentioning

confidence: 99%

“…Another approach combines both manifold learning and clustering in a unified formulation. For example, Peng et al [10] clustered data based on manifold regularization using extreme learning machine (ELM) [11], using clustering accuracy and normalized mutual information as the measure of goodness of fit.…”

Section: Introductionmentioning

confidence: 99%

Estimating vigilance from EEG using manifold clustering guided by instantaneous lapse rate

Sun

Yan

Sourina

et al. 2015

2015 10th International Conference on Information, Communications and Signal Processing (ICICS)

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Vigilance decrement happens in prolonged and monotonous tasks such as driving, therefore efficient estimation of vigilance using machine learning becomes a growing research field in road safety. However, the ground truth of vigilance level is often unknown. To address the estimation of brain states with unknown ground truth, we proposed an unsupervised manifold clustering method guided by task performance, namely instantaneous lapse rate, without directly using any artificially labels, using electroencephalogram (EEG) as data source. The proposed algorithm utilizes information from both data structure and task performance, which is especially suitable for applications with unknown ground truth. Future research directions include using advanced manifold clustering algorithms to increase the robustness towards the high nonlinearity in the EEG feature space and the embedded space, as well as allowing the mapping from multiple clusters to one vigilance level

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“…ELM is a new and efficient method to train a single-hidden-layer feedforward neural networks (SLFNs) [23]. ELM has been applied in many fields such as signal and information processing due to the good general performance and fast learning with minimal human intervention [23][24][25][26][27][28][29][30][31][32][33]. ELM has been shown to provide better results than SVM and require less training time than BPNN.…”

Section: Elm-assisted Toa Estimationmentioning

confidence: 99%

A novel time of arrival estimation algorithm using an energy detector receiver in MMW systems

Liang

Zhang

Lyu

et al. 2017

EURASIP J. Adv. Signal Process.

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This paper presents a new time of arrival (TOA) estimation technique using an improved energy detection (ED) receiver based on the empirical mode decomposition (EMD) in an impulse radio (IR) 60 GHz millimeter wave (MMW) system. A threshold is employed via analyzing the characteristics of the received energy values with an extreme learning machine (ELM). The effect of the channel and integration period on the TOA estimation is evaluated. Several well-known ED-based TOA algorithms are used to compare with the proposed technique. It is shown that this ELM-based technique has lower TOA estimation error compared to other approaches and provides robust performance with the IEEE 802.15.3c channel models.

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An unsupervised discriminative extreme learning machine and its applications to data clustering

Cited by 38 publications

References 43 publications

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

Estimating vigilance from EEG using manifold clustering guided by instantaneous lapse rate

A novel time of arrival estimation algorithm using an energy detector receiver in MMW systems

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