论文标题
用机器学习技术解释想象的语音波
Interpreting Imagined Speech Waves with Machine Learning techniques
论文作者
论文摘要
这项工作探讨了解码想象的语音（IS）信号的可能性，该信号可用于创建人类计算机界面（HCI）的新设计。由于基础过程生成EEG信号尚不清楚，因此使用各种特征提取方法以及不同的神经网络（NN）模型，用于近似数据分布，并且分类为信号。基于实验结果，具有集合和协方差矩阵转换的特征的前馈NN模型与其他现有方法相比表现出最高的性能。为了进行比较，使用了三个公开可用的数据集。我们报告了在休息状态和想象状态之间的平均分类精度为80％，在两个数据集上的长词和短词的解码为96％和80％。这些结果表明，可以将脑信号（在静止状态生成）与IS脑信号区分开。基于实验结果，我们建议可以使用单词长度和复杂性来解码，这是具有高精度的信号，并且可以使用BCI系统设计的IS信号用于计算机交互。这些想法和结果为开发商业级别提供了方向，基于BCI系统，可用于日常生活中的人类计算机相互作用。
This work explores the possibility of decoding Imagined Speech (IS) signals which can be used to create a new design of Human-Computer Interface (HCI). Since the underlying process generating EEG signals is unknown, various feature extraction methods, along with different neural network (NN) models, are used to approximate data distribution and classify IS signals. Based on the experimental results, feed-forward NN model with ensemble and covariance matrix transformed features showed the highest performance in comparison to other existing methods. For comparison, three publicly available datasets were used. We report a mean classification accuracy of 80% between rest and imagined state, 96% and 80% for decoding long and short words on two datasets. These results show that it is possible to differentiate brain signals (generated during rest state) from the IS brain signals. Based on the experimental results, we suggest that the word length and complexity can be used to decode IS signals with high accuracy, and a BCI system can be designed with IS signals for computer interaction. These ideas, and results give direction for the development of a commercial level IS based BCI system, which can be used for human-computer interaction in daily life.
