Standardization-refinement domain adaptation method for cross-subject EEG-based classification in imagined speech recognition

Jiménez-Guarneros, Magdiel; Gómez-Gil, Pilar

doi:10.1016/j.patrec.2020.11.013

Cited by 29 publications

(11 citation statements)

References 23 publications

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“…In essence, data classification investigates the relations between feature variables (i.e., inputs) and output variables. Classification methods have been used in a broad range of applications such as customer target marketing [1,2], medical disease diagnosis [3][4][5], speech and handwriting recognition [6][7][8][9], multimedia data analysis [10,11], biological data analysis [12], document categorization and filtering [13,14], and social network analysis [15][16][17]. Classification algorithms typically contain two steps, the learning step and the testing step.…”

Section: Classification Methodsmentioning

confidence: 99%

Ontology-Based Feature Selection: A Survey

2021

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The Semantic Web emerged as an extension to the traditional Web, adding meaning (semantics) to a distributed Web of structured and linked information. At its core, the concept of ontology provides the means to semantically describe and structure information, and expose it to software and human agents in a machine and human-readable form. For software agents to be realized, it is crucial to develop powerful artificial intelligence and machine-learning techniques, able to extract knowledge from information sources, and represent it in the underlying ontology. This survey aims to provide insight into key aspects of ontology-based knowledge extraction from various sources such as text, databases, and human expertise, realized in the realm of feature selection. First, common classification and feature selection algorithms are presented. Then, selected approaches, which utilize ontologies to represent features and perform feature selection and classification, are described. The selective and representative approaches span diverse application domains, such as document classification, opinion mining, manufacturing, recommendation systems, urban management, information security systems, and demonstrate the feasibility and applicability of such methods. This survey, in addition to the criteria-based presentation of related works, contributes a number of open issues and challenges related to this still active research topic.

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Section: Classification Methodsmentioning

confidence: 99%

Ontology-Based Feature Selection: A Survey

2021

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“…Considering existing domain adaptation approaches may become sensitive where a low discriminative feature space among classes is given. Jiménez-Guarneros and Gómez-Gil [202] proposed a Standardization-Refinement Domain Adaptation (SRDA) method, which trains a target neural network model using Adaptive Batch Normalization (AdaBN) and introducing a novel loss based on the Variation of Information (VOI). Using AdaBN, SRDA makes the marginal distributions similar in source and target domains.…”

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confidence: 99%

EEG Based Emotion Recognition: A Tutorial and Review

et al. 2022

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Emotion recognition technology through analyzing the EEG signal is currently an essential concept in Artificial Intelligence and holds great potential in emotional health care, human-computer interaction, multimedia content recommendation, etc. Though there have been several works devoted to reviewing EEG-based emotion recognition, the content of these reviews needs to be updated. In addition, those works are either fragmented in content or only focus on specific techniques adopted in this area but neglect the holistic perspective of the entire technical routes. Hence, in this paper, we review from the perspective of researchers who try to take the first step on this topic. We review the recent representative works in the EEG-based emotion recognition research and provide a tutorial to guide the researchers to start from the beginning. The scientific basis of EEG-based emotion recognition in the psychological and physiological levels is introduced. Further, we categorize these reviewed works into different technical routes and illustrate the theoretical basis and the research motivation, which will help the readers better understand why those techniques are studied and employed. At last, existing challenges and future investigations are also discussed in this paper, which guides the researchers to decide potential future research directions.

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“…Furthermore, deep learning approaches have recently taken a huge role for imagined speech recognition. Some of these techniques are Deep Neural Networks (DBN) (Lee and Sim, 2015 ; Chengaiyan et al, 2020 ), Correlation Networks (CorrNet) (Sharon and Murthy, 2020 ), Standardization-Refinement Domain Adaptation (SRDA) (Jiménez-Guarneros and Gómez-Gil, 2021 ), Extreme Learning Machine (ELM) (Pawar and Dhage, 2020 ), Convolutional Neural Networks (CNN) (Cooney et al, 2019 , 2020 ; Tamm et al, 2020 ), Recurrent Neural Networks (RNN) (Chengaiyan et al, 2020 ), and parallel CNN+RNN with and without autoencoders autoencoders (Saha and Fels, 2019 ; Saha et al, 2019a , b ; Kumar and Scheme, 2021 ).…”

Section: Classification Techniques In Literaturementioning

confidence: 99%

A State-of-the-Art Review of EEG-Based Imagined Speech Decoding

Lopez-Bernal

Balderas

Ponce

et al. 2022

Front. Hum. Neurosci.

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Currently, the most used method to measure brain activity under a non-invasive procedure is the electroencephalogram (EEG). This is because of its high temporal resolution, ease of use, and safety. These signals can be used under a Brain Computer Interface (BCI) framework, which can be implemented to provide a new communication channel to people that are unable to speak due to motor disabilities or other neurological diseases. Nevertheless, EEG-based BCI systems have presented challenges to be implemented in real life situations for imagined speech recognition due to the difficulty to interpret EEG signals because of their low signal-to-noise ratio (SNR). As consequence, in order to help the researcher make a wise decision when approaching this problem, we offer a review article that sums the main findings of the most relevant studies on this subject since 2009. This review focuses mainly on the pre-processing, feature extraction, and classification techniques used by several authors, as well as the target vocabulary. Furthermore, we propose ideas that may be useful for future work in order to achieve a practical application of EEG-based BCI systems toward imagined speech decoding.

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Standardization-refinement domain adaptation method for cross-subject EEG-based classification in imagined speech recognition

Cited by 29 publications

References 23 publications

Ontology-Based Feature Selection: A Survey

Ontology-Based Feature Selection: A Survey

EEG Based Emotion Recognition: A Tutorial and Review

A State-of-the-Art Review of EEG-Based Imagined Speech Decoding

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