Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China; Department of Biomedical Engineering , Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China.
Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China.
Comput Biol Med. 2022 Sep;148:105871. doi: 10.1016/j.compbiomed.2022.105871. Epub 2022 Jul 15.
As the scale of neural recording increases, Brain-computer interfaces (BCIs) are restrained by high-dimensional neural features, so dimensionality reduction is required as a preprocess of neural features. In this context, we propose a novel framework based on deep learning to reduce the dimensionality of neural features that are typically extracted from electrocorticography (ECoG) or local field potential (LFP).
A high-performance autoencoder was implemented by chaining convolutional layers to deal with spatial and frequency dimensions with bottleneck long short-term memory (LSTM) layers to deal with the temporal dimension of the features. Furthermore, this autoencoder is combined with a fully connected layer to regularize the training.
By applying the proposed method to two different datasets, we found that this dimensionality reduction method largely outperforms kernel principal component analysis (KPCA), partial least square (PLS), preferential subspace identification (PSID), and latent factor analysis via dynamical systems (LFADS). Besides, the new features obtained by our method can be applied to various BCI decoders, without significant differences in decoding performance.
A novel method is proposed as a reliable tool for efficient dimensionality reduction of neural signals. Its high performance and robustness are promising to enhance the decoding accuracy and long-term stability of online BCI systems based on large-scale neural recordings.
随着神经记录规模的扩大，脑机接口（BCI）受到高维神经特征的限制，因此需要降维作为神经特征的预处理。在这种情况下，我们提出了一种基于深度学习的新框架，以降低通常从脑电（ECoG）或局部场电位（LFP）中提取的神经特征的维数。
通过链式卷积层实现高性能自动编码器，以处理空间和频率维度，瓶颈长短期记忆（LSTM）层用于处理特征的时间维度。此外，该自动编码器与全连接层相结合以进行正则化训练。
通过将所提出的方法应用于两个不同的数据集，我们发现这种降维方法大大优于核主成分分析（KPCA）、偏最小二乘（PLS）、优先子空间识别（PSID）和通过动力系统的潜在因子分析（LFADS）。此外，我们方法获得的新特征可应用于各种 BCI 解码器，解码性能没有显著差异。
提出了一种新方法作为神经信号高效降维的可靠工具。其高性能和鲁棒性有望提高基于大规模神经记录的在线 BCI 系统的解码精度和长期稳定性。
