RESUMEN
BACKGROUND: Brain computer interface (BCI) utilizes brain signals to help users interact with external devices directly. EEG is one of the most commonly used techniques for brain signal acquisition in BCI. However, it is notoriously difficult to build a generic EEG recognition model due to significant non-stationarity and subject-to-subject variations, and the requirement for long time training. Transfer learning (TL) is particularly useful because it can alleviate the calibration requirement in EEG-based BCI applications by transferring the calibration information from existing subjects to new subject. To take advantage of geometric properties in Riemann manifold and joint distribution adaptation, a manifold embedded transfer learning (METL) framework was proposed for motor imagery (MI) EEG decoding. NEW METHOD: First, the covariance matrices of the EEG trials are first aligned on the SPD manifold. Then the features are extracted from both the symmetric positive definite (SPD) manifold and Grassmann manifold. Finally, the classification model is learned by combining the structural risk minimization (SRM) of source domain and joint distribution alignment of source and target domains. RESULT: Experimental results on two MI EEG datasets verify the effectiveness of the proposed METL. In particular, when there are a small amount of labeled samples in the target domain, METL demonstrated a more accurate and stable classification performance than conventional methods. COMPARISON WITH EXISTING METHODS: Compared with several state-of-the-art methods, METL has achieved better classification accuracy, 71.81% and 69.06% in single-to-single (STS), 83.14% and 76.00% in multi-to-single (MTS) transfer tasks, respectively. CONCLUSIONS: METL can cope with single source domain or multi-source domains and compared with single-source transfer learning, multi-source transfer learning can improve the performance effectively due to the data expansion. It is effective enough to achieve superior performance for classification of EEG signals.
Asunto(s)
Interfaces Cerebro-Computador , Algoritmos , Electroencefalografía/métodos , Humanos , Imaginación , Aprendizaje , Aprendizaje AutomáticoRESUMEN
Internal states, e.g., emotions, cognitive states, or desires, are often verbalized by figurative means, in particular by embodied metaphors involving human senses, such as touch, taste, and smell. The present paper presents a database for German metaphorical expressions conveying internal states with human senses as their source domains. 168 metaphorical expressions from the source domains of vision, hearing, smell, taste, touch, and temperature combined with literal equivalents were collected and rated by 643 adults. The agreement between the metaphor and an equivalent literal expression, as well as emotional valence, arousal, and familiarity values were assessed using a 7-point Likert scale. Between the metaphorical expressions and their equivalents, familiarity, but not valence or arousal differed significantly while agreement ratings indicated high similarity in meaning. The novel database offers carefully controlled stimuli that can be used in both empirical metaphor research and research on internal state language. Using part of the stimuli in a sentence completion experiment revealed a significant preference for literal over metaphorical expressions that cannot be attributed to higher familiarity levels.
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Lenguaje , Metáfora , Adulto , Comprensión , Emociones , Humanos , Psicolingüística , Reconocimiento en PsicologíaRESUMEN
BACKGROUND: Automatic extraction of biomedical events from literature, that allows for faster update of the latest discoveries automatically, is a heated research topic now. Trigger word recognition is a critical step in the process of event extraction. Its performance directly influences the results of the event extraction. In general, machine learning-based trigger recognition approaches such as neural networks must to be trained on a dataset with plentiful annotations to achieve high performances. However, the problem of the datasets in wide coverage event domains is that their annotations are insufficient and imbalance. One of the methods widely used to deal with this problem is transfer learning. In this work, we aim to extend the transfer learning to utilize multiple source domains. Multiple source domain datasets can be jointly trained to help achieve a higher recognition performance on a target domain with wide coverage events. RESULTS: Based on the study of previous work, we propose an improved multi-source domain neural network transfer learning architecture and a training approach for biomedical trigger detection task, which can share knowledge between the multi-source and target domains more comprehensively. We extend the ability of traditional adversarial networks to extract common features between source and target domains, when there is more than one dataset in the source domains. Multiple feature extraction channels to simultaneously capture global and local common features are designed. Moreover, under the constraint of an extra classifier, the multiple local common feature sub-channels can extract and transfer more diverse common features from the related multi-source domains effectively. In the experiments, MLEE corpus is used to train and test the proposed model to recognize the wide coverage triggers as a target dataset. Other four corpora with the varying degrees of relevance with MLEE from different domains are used as source datasets, respectively. Our proposed approach achieves recognition improvement compared with traditional adversarial networks. Moreover, its performance is competitive compared with the results of other leading systems on the same MLEE corpus. CONCLUSIONS: The proposed Multi-Source Transfer Learning-based Trigger Recognizer (MSTLTR) can further improve the performance compared with the traditional method, when the source domains are more than one. The most essential improvement is that our approach represents common features in two aspects: the global common features and the local common features. Hence, these more sharable features improve the performance and generalization of the model on the target domain effectively.