With the rapid development of Machine Learning technology applied in electroencephalography (EEG) signals, Brain-Computer Interface (BCI) has emerged as a novel and convenient human-computer interaction for smart home, intelligent medical and other Internet of Things (IoT) scenarios. However, security issues such as sensitive information disclosure and unauthorized operations have not received sufficient concerns. There are still some defects with the existing solutions to encrypted EEG data such as low accuracy, high time complexity or slow processing speed. For this reason, a classification and recognition method of encrypted EEG data based on neural network is proposed, which adopts Paillier encryption algorithm to encrypt EEG data and meanwhile resolves the problem of floating point operations. In addition, it improves traditional feed-forward neural network (FNN) by using the approximate function instead of activation function and realizes multi-classification of encrypted EEG data. Extensive experiments are conducted to explore the effect of several metrics (such as the hidden neuron size and the learning rate updated by improved simulated annealing algorithm) on the recognition results. Followed by security and time cost analysis, the proposed model and approach are validated and evaluated on public EEG datasets provided by PhysioNet, BCI Competition IV and EPILEPSIAE. The experimental results show that our proposal has the satisfactory accuracy, efficiency and feasibility compared with other solutions.

随着应用于脑电图（EEG）信号的机器学习技术的飞速发展，脑机接口（BCI）已经成为一种新型且便捷的人机交互方式，适用于智能家居，智能医疗和其他物联网（IoT）场景。但是，诸如敏感信息泄露和未授权操作之类的安全问题尚未引起足够的关注。现有的加密EEG数据解决方案仍然存在一些缺陷，例如准确性低，时间复杂度高或处理速度慢。为此，提出了一种基于神经网络的加密脑电数据分类识别方法，采用Paillier加密算法对脑电数据进行加密，解决了浮点运算的问题。此外，它通过使用逼近函数代替激活函数来改进传统的前馈神经网络（FNN），并实现了加密的EEG数据的多分类。进行了广泛的实验，以探索几种指标（例如隐藏的神经元大小和通过改进的模拟退火算法更新的学习率）对识别结果的影响。其次是安全性和时间成本分析，然后在PhysioNet，BCI Competition IV和EPILEPSIAE提供的公共EEG数据集上对提出的模型和方法进行验证和评估。实验结果表明，与其他方案相比，我们的方案具有令人满意的准确性，效率和可行性。进行了广泛的实验，以探索几种指标（例如隐藏的神经元大小和通过改进的模拟退火算法更新的学习率）对识别结果的影响。其次是安全性和时间成本分析，然后在PhysioNet，BCI Competition IV和EPILEPSIAE提供的公共EEG数据集上对提出的模型和方法进行验证和评估。实验结果表明，与其他方案相比，我们的方案具有令人满意的准确性，效率和可行性。进行了广泛的实验，以探索几种指标（例如隐藏的神经元大小和通过改进的模拟退火算法更新的学习率）对识别结果的影响。其次是安全性和时间成本分析，然后在PhysioNet，BCI Competition IV和EPILEPSIAE提供的公共EEG数据集上对提出的模型和方法进行验证和评估。实验结果表明，与其他方案相比，我们的方案具有令人满意的准确性，效率和可行性。在PhysioNet，BCI Competition IV和EPILEPSIAE提供的公共EEG数据集上对提出的模型和方法进行了验证和评估。实验结果表明，与其他方案相比，我们的方案具有令人满意的准确性，效率和可行性。在PhysioNet，BCI Competition IV和EPILEPSIAE提供的公共EEG数据集上对提出的模型和方法进行了验证和评估。实验结果表明，与其他方案相比，我们的方案具有令人满意的准确性，效率和可行性。