论文标题
基于相关的多重模型，用于改进想象的语音脑电图识别
Correlation based Multi-phasal models for improved imagined speech EEG recognition
论文作者
论文摘要
想象中的语音脑电图（EEG）转换为人类可理解的命令，极大地促进了自然主义的大脑计算机接口的设计。为了获得改进的想象的语音单位分类，这项工作旨在从录制，想象和执行与特定语音单元相对的发音运动时记录，想象和执行发音运动时所记录的多重EEG数据中所包含的平行信息。使用神经网络的双相公共表示模块旨在建模分析阶段和支持阶段之间的相关性和可重复性。然后，使用训练有素的相关网络来提取分析阶段的区分特征。使用机器学习模型，例如基于高斯混合物的隐藏Markov模型和深层神经网络，将这些功能进一步分为五个二元语音类别。所提出的方法进一步处理解码过程中多重数据的不可用。地形可视化以及基于结果的推论表明，本文中提出的多强度相关模型方法增强了想象的语音脑电图识别性能。
Translation of imagined speech electroencephalogram(EEG) into human understandable commands greatly facilitates the design of naturalistic brain computer interfaces. To achieve improved imagined speech unit classification, this work aims to profit from the parallel information contained in multi-phasal EEG data recorded while speaking, imagining and performing articulatory movements corresponding to specific speech units. A bi-phase common representation learning module using neural networks is designed to model the correlation and reproducibility between an analysis phase and a support phase. The trained Correlation Network is then employed to extract discriminative features of the analysis phase. These features are further classified into five binary phonological categories using machine learning models such as Gaussian mixture based hidden Markov model and deep neural networks. The proposed approach further handles the non-availability of multi-phasal data during decoding. Topographic visualizations along with result-based inferences suggest that the multi-phasal correlation modelling approach proposed in the paper enhances imagined-speech EEG recognition performance.
