School of Electrical Engineering and Automation, Tianjin University of Technology, Tianjin 300384, China.
First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin 300193, China.
Sensors (Basel). 2024 Sep 15;24(18):5988. doi: 10.3390/s24185988.
Decoding semantic concepts for imagination and perception tasks (SCIP) is important for rehabilitation medicine as well as cognitive neuroscience. Electroencephalogram (EEG) is commonly used in the relevant fields, because it is a low-cost noninvasive technique with high temporal resolution. However, as EEG signals contain a high noise level resulting in a low signal-to-noise ratio, it makes decoding EEG-based semantic concepts for imagination and perception tasks (SCIP-EEG) challenging. Currently, neural network algorithms such as CNN, RNN, and LSTM have almost reached their limits in EEG signal decoding due to their own short-comings. The emergence of transformer methods has improved the classification performance of neural networks for EEG signals. However, the transformer model has a large parameter set and high complexity, which is not conducive to the application of BCI. EEG signals have high spatial correlation. The relationship between signals from different electrodes is more complex. Capsule neural networks can effectively model the spatial relationship between electrodes through vector representation and a dynamic routing mechanism. Therefore, it achieves more accurate feature extraction and classification. This paper proposes a spatio-temporal capsule network with a self-correlation routing mechaninsm for the classification of semantic conceptual EEG signals. By improving the feature extraction and routing mechanism, the model is able to more effectively capture the highly variable spatio-temporal features from EEG signals and establish connections between capsules, thereby enhancing classification accuracy and model efficiency. The performance of the proposed model was validated using the publicly accessible semantic concept dataset for imagined and perceived tasks from Bath University. Our model achieved average accuracies of 94.9%, 93.3%, and 78.4% in the three sensory modalities (pictorial, orthographic, and audio), respectively. The overall average accuracy across the three sensory modalities is 88.9%. Compared to existing advanced algorithms, the proposed model achieved state-of-the-art performance, significantly improving classification accuracy. Additionally, the proposed model is more stable and efficient, making it a better decoding solution for SCIP-EEG decoding.
解码想象和感知任务的语义概念（SCIP）对于康复医学和认知神经科学都很重要。脑电图（EEG）在相关领域中被广泛应用，因为它是一种低成本、非侵入性、具有高时间分辨率的技术。然而，由于 EEG 信号中包含高水平的噪声，导致信噪比低，因此解码基于 EEG 的想象和感知任务的语义概念（SCIP-EEG）具有挑战性。目前，由于自身的局限性，神经网络算法，如卷积神经网络（CNN）、循环神经网络（RNN）和长短时记忆网络（LSTM），在 EEG 信号解码方面几乎已经达到了极限。由于其自身的局限性，Transformer 方法的出现提高了神经网络对 EEG 信号的分类性能。然而，Transformer 模型具有较大的参数集和较高的复杂性，不利于 BCI 的应用。EEG 信号具有较高的空间相关性。不同电极之间的信号关系更为复杂。胶囊神经网络可以通过向量表示和动态路由机制有效地对电极之间的空间关系进行建模。因此，它可以实现更准确的特征提取和分类。本文提出了一种具有自相关路由机制的时空胶囊网络，用于语义概念 EEG 信号的分类。通过改进特征提取和路由机制，该模型能够更有效地从 EEG 信号中提取高度变化的时空特征，并建立胶囊之间的连接，从而提高分类准确性和模型效率。所提出的模型的性能使用来自巴斯大学的公开想象和感知任务的语义概念数据集进行验证。在三个感觉模态（图像、正字法和音频）中，我们的模型分别达到了 94.9%、93.3%和 78.4%的平均准确率。在三个感觉模态的总体平均准确率为 88.9%。与现有的先进算法相比，所提出的模型实现了最先进的性能，显著提高了分类准确性。此外，所提出的模型更加稳定和高效，是 SCIP-EEG 解码的更好解码解决方案。
