...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of neural engineering >Inter-subject transfer learning with an end-to-end deep convolutional neural network for EEG-based BCI
【24h】

Inter-subject transfer learning with an end-to-end deep convolutional neural network for EEG-based BCI

机译：基于EEG的BCI的端到端深度卷积神经网络的受试者间迁移学习

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Objective. Despite the effective application of deep learning (DL) in brain-computer interface (BCI) systems, the successful execution of this technique, especially for inter-subject classification, in cognitive BCI has not been accomplished yet. In this paper, we propose a framework based on the deep convolutional neural network (CNN) to detect the attentive mental state from single-channel raw electroencephalography (EEG) data. Approach. We develop an end-to-end deep CNN to decode the attentional information from an EEG time series. We also explore the consequences of input representations on the performance of deep CNN by feeding three different EEG representations into the network. To ensure the practical application of the proposed framework and avoid time-consuming re-training, we perform inter-subject transfer learning techniques as a classification strategy. Eventually, to interpret the learned attentional patterns, we visualize and analyse the network perception of the attention and non-attention classes. Main results. The average classification accuracy is 79.26%, with only 15.83% of 120 subjects having an accuracy below 70% (a generally accepted threshold for BCI). This is while with the inter-subject approach, it is literally difficult to output high classification accuracy. This end-to-end classification framework surpasses conventional classification methods for attention detection. The visualization results demonstrate that the learned patterns from the raw data are meaningful. Significance. This framework significantly improves attention detection accuracy with inter-subject classification. Moreover, this study sheds light on the research on end-to-end learning; the proposed network is capable of learning from raw data with the least amount of pre-processing, which in turn eliminates the extensive computational load of time-consuming data preparation and feature extraction.
机译：目的。尽管深度学习（DL）在脑机接口（BCI）系统中得到了有效的应用，但是该技术的成功执行，尤其是对于受试者间分类，在认知BCI中尚未成功实现。在本文中，我们提出了一个基于深度卷积神经网络（CNN）的框架，用于从单通道原始脑电图（EEG）数据中检测注意力集中状态。方法。我们开发了一个端到端的深度CNN，以解码来自EEG时间序列的注意力信息。通过将三种不同的EEG表示输入到网络中，我们还探索了输入表示对深层CNN性能的影响。为确保所提出框架的实际应用并避免耗时的重新训练，我们将受试者间的转移学习技术作为分类策略。最终，为了解释学习到的注意力模式，我们对注意力和非注意力类别的网络感知进行了可视化和分析。主要结果。平均分类准确性为79.26％，在120名受试者中，只有15.83％的准确性低于70％（BCI的公认阈值）。在使用主体间方法时，实际上很难输出高分类精度。这种端到端的分类框架超过了用于注意力检测的常规分类方法。可视化结果表明，从原始数据中学到的模式是有意义的。意义。该框架通过对象间分类显着提高了注意力检测的准确性。此外，本研究为端到端学习的研究提供了启示。提出的网络能够以最少的预处理量从原始数据中学习，从而消除了费时的数据准备和特征提取所需的大量计算量。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号