论文标题
用于基于SSVEP的大脑计算机界面的深神经网络
A Deep Neural Network for SSVEP-based Brain-Computer Interfaces
论文作者
论文摘要
目的：脑部计算机界面（BCI）拼写中的目标识别是指预测受试者打算拼写的目标特征的脑电图（EEG）分类。当每个字符的视觉刺激都以明显的频率标记时，EEG记录了稳态的视觉诱发电势（SSVEP），其频谱由目标频率的谐波主导。在这种情况下，我们解决了目标识别，并提出了一种新颖的深神经网络（DNN）体系结构。方法：所提出的DNN处理多通道SSVEP，并在整个谐波，通道，时间和分类的子频段上进行了卷积，并在完全连接的层上进行了分类。我们使用两个公开可用的大型（基准和β）数据集进行测试，该数据集由105位具有40个字符的受试者组成。我们的第一阶段培训通过利用所有受试者之间的统计共同点来学习一个全球模型，以及通过利用个性分别对每个主题的第二阶段微调。结果：我们的DNN在两个数据集上分别达到了265.23位/分钟和196.59位/min的令人印象深刻的信息传输速率（ITR），仅刺激仅为0.4秒。该代码可在https://github.com/osmanberke/deep-ssvep-bci上可重复可重复。结论：提出的DNN强烈胜过最先进的技术，因为我们的准确性和ITR率是这些数据集上报告的最高性能结果。意义：由于其前所未有的高拼写器ITR和对通用SSVEP系统的完美适用性，我们的技术在BCIS的各种生物医学工程环境中具有巨大的潜力，例如通信，康复和控制。
Objective: Target identification in brain-computer interface (BCI) spellers refers to the electroencephalogram (EEG) classification for predicting the target character that the subject intends to spell. When the visual stimulus of each character is tagged with a distinct frequency, the EEG records steady-state visually evoked potentials (SSVEP) whose spectrum is dominated by the harmonics of the target frequency. In this setting, we address the target identification and propose a novel deep neural network (DNN) architecture. Method: The proposed DNN processes the multi-channel SSVEP with convolutions across the sub-bands of harmonics, channels, time, and classifies at the fully connected layer. We test with two publicly available large scale (the benchmark and BETA) datasets consisting of in total 105 subjects with 40 characters. Our first stage training learns a global model by exploiting the statistical commonalities among all subjects, and the second stage fine tunes to each subject separately by exploiting the individualities. Results: Our DNN achieves impressive information transfer rates (ITRs) on both datasets, 265.23 bits/min and 196.59 bits/min, respectively, with only 0.4 seconds of stimulation. The code is available for reproducibility at https://github.com/osmanberke/Deep-SSVEP-BCI. Conclusion: The presented DNN strongly outperforms the state-of-the-art techniques as our accuracy and ITR rates are the highest ever reported performance results on these datasets. Significance: Due to its unprecedentedly high speller ITRs and flawless applicability to general SSVEP systems, our technique has great potential in various biomedical engineering settings of BCIs such as communication, rehabilitation and control.
