Brain-computer interface (BCI) provides a platform for humans to communicate using Electroencephalogram (EEG) signals by converting them into commands that can be used by the output device to perform the desired tasks. This paper focuses on the identification of vowels from EEG signals. First, a dataset of EEG signals has been created for the identification of vowels by collecting data using a 14-channel EEG device Emotiv- epoc+ from 16 subjects. Then, a deep learning-based model is proposed using a multi-headed Convolutional Neural Network for feature extraction and classification of imagined speech of vowels. Butterworth lowpass and bandpass filter of order five are implemented for denoising and sub-banding of the EEG signals which are further pre-processed using Hilbert Huang Transform. The model has achieved an average accuracy of 97.67% with a five-fold cross-validation technique using all six sub-bands of the EEG signals. The model has achieved an average precision and recall of 95.54% and 95.11% respectively. The proposed model is statistically tested using the Mann-Whitney U test and paired t-test with a p-value less than 0.05.

中文翻译：

---

使用多头 CNN 特征融合网络对脑电图信号中的元音想象语音进行分类

脑机接口（BCI）为人类使用脑电图（EEG）信号进行通信提供了一个平台，将其转换为可由输出设备用来执行所需任务的命令。本文重点研究脑电图信号中元音的识别。首先，通过使用 14 通道脑电图设备 Emotivepoc+ 从 16 名受试者收集数据，创建了脑电图信号数据集，用于识别元音。然后，提出了一种基于深度学习的模型，使用多头卷积神经网络对元音想象语音进行特征提取和分类。巴特沃斯低通和五阶带通滤波器用于脑电图信号的去噪和子带化，并使用希尔伯特黄变换进一步预处理。该模型通过使用脑电图信号的所有六个子带的五倍交叉验证技术，平均准确率达到 97.67%。该模型的平均准确率和召回率分别达到 95.54% 和 95.11%。使用 Mann-Whitney U 检验和 p 值小于 0.05 的配对 t 检验对所提出的模型进行统计测试。