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In our previous study, we introduced a wavelet transform, which is a time-frequency analysis method, and applied it to extract features, including time information, to enable more accurate discrimination between individuals. An equal error rate (EER) of 9.4 % was achieved using Euclidean distance matching. In this paper, we introduce a machine learning-based approach in order to further improve the verification performance. An EER of 8.1 % is achieved by the proposed method after training the constituent neural networks using ensemble learning with 30 networks.", "subitem_description_type": "Other"}]}, "item_10003_relation_16": {"attribute_name": "情報源", "attribute_value_mlt": [{"subitem_relation_name": [{"subitem_relation_name_text": "T. Miyake, N. Kinjo, I.Nakanishi. Wavelet Transform and Machine Learning-Based Biometric Authentication Using EEG Evoked by Invisible Visual Stimuli. 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