论文标题
使用小波特征和深度神经网络解码想象的语音
Decoding Imagined Speech using Wavelet Features and Deep Neural Networks
论文作者
论文摘要
本文提出了一种新型方法，该方法使用深层神经网络来分类想象的语音，从而显着提高了分类精度。所提出的方法仅采用大脑特定区域的EEG通道进行分类，并从每个渠道中得出了不同的特征向量。这为我们提供了更多的数据来培训分类器，从而使我们能够使用深度学习方法。从每个通道中提取小波和时间域特征。每个测试试验的最终类标签是通过在试验中考虑的单个渠道的分类结果上应用多数投票获得的。这种方法用于对想象中的语音的Karaone数据集中的所有11个提示进行分类。拟议的架构和处理数据的方法导致平均分类准确性为57.15％，这比最先进的结果提高了约35％。
This paper proposes a novel approach that uses deep neural networks for classifying imagined speech, significantly increasing the classification accuracy. The proposed approach employs only the EEG channels over specific areas of the brain for classification, and derives distinct feature vectors from each of those channels. This gives us more data to train a classifier, enabling us to use deep learning approaches. Wavelet and temporal domain features are extracted from each channel. The final class label of each test trial is obtained by applying a majority voting on the classification results of the individual channels considered in the trial. This approach is used for classifying all the 11 prompts in the KaraOne dataset of imagined speech. The proposed architecture and the approach of treating the data have resulted in an average classification accuracy of 57.15%, which is an improvement of around 35% over the state-of-the-art results.
