论文标题
转移学习和规格应用于基于SSVEP的BCI分类
Transfer Learning and SpecAugment applied to SSVEP Based BCI Classification
论文作者
论文摘要
目的：我们使用深层卷积神经网络（DCNN）在基于稳态的视觉诱发电势（SSVEP）的单渠道脑机关系界面（BCI）中对脑电图（EEG）信号进行了分类，这不需要对用户的校准进行校准。 方法：将脑电图信号转换为频谱图，并使用传输学习技术作为训练DCNN的输入。我们还修改并应用了一种数据增强方法，通常用于语音识别。此外，为了进行比较，我们使用支持矢量机（SVM）和过滤库的规范相关分析（FBCCA）对SSVEP数据集进行了分类。 结果：从微调过程中排除了评估的用户数据，我们使用较小的数据长度（0.5 s），只有一个电极（oz）和带有转移学习，窗口切片（WS）和示例的时间，我们达到了82.2％的平均测试准确性和0.825的平均F1测试准确性，并在35个受试者上达到35个受试者的平均F1分数。 结论：DCNN结果使用单电极和较小的数据长度超过了SVM和FBCCA性能。转移学习提供了最小的精度变化，但使培训更快。规格创造了较小的性能改进，并成功地与WS结合在一起，产生了更高的精度。 意义：我们提出了一种使用DCNNS解决SSVEP分类问题的新方法。我们还修改了语音识别数据增强技术，并将其应用于BCIS的背景。提出的方法超过了用较小的数据长度和一个电极的BCIS中的FBCCA和SVM（更传统的SSVEP分类方法）获得的性能。这种类型的BCI可用于开发小型和快速的系统。
Objective: We used deep convolutional neural networks (DCNNs) to classify electroencephalography (EEG) signals in a steady-state visually evoked potentials (SSVEP) based single-channel brain-computer interface (BCI), which does not require calibration on the user. Methods: EEG signals were converted to spectrograms and served as input to train DCNNs using the transfer learning technique. We also modified and applied a data augmentation method, SpecAugment, generally employed for speech recognition. Furthermore, for comparison purposes, we classified the SSVEP dataset using Support-vector machines (SVMs) and Filter Bank canonical correlation analysis (FBCCA). Results: Excluding the evaluated user's data from the fine-tuning process, we reached 82.2% mean test accuracy and 0.825 mean F1-Score on 35 subjects from an open dataset, using a small data length (0.5 s), only one electrode (Oz) and the DCNN with transfer learning, window slicing (WS) and SpecAugment's time masks. Conclusion: The DCNN results surpassed SVM and FBCCA performances, using a single electrode and a small data length. Transfer learning provided minimal accuracy change, but made training faster. SpecAugment created a small performance improvement and was successfully combined with WS, yielding higher accuracies. Significance: We present a new methodology to solve the problem of SSVEP classification using DCNNs. We also modified a speech recognition data augmentation technique and applied it to the context of BCIs. The presented methodology surpassed performances obtained with FBCCA and SVMs (more traditional SSVEP classification methods) in BCIs with small data lengths and one electrode. This type of BCI can be used to develop small and fast systems.
