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使用EEG和深度学习进行想象语音分类.

Mokhles M Abdulghani1, Wilbur L Walters1, Khalid H Abed1

  • 1Department of Electrical & Computer Engineering and Computer Science, College of Sciences, Engineering & Technology, Jackson State University, Jackson, MS 39217, USA.

Bioengineering (Basel, Switzerland)
|June 28, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种深度学习方法,用于使用EEG信号识别想象中的语音大脑波. 这种方法实现了高精度,为先进的大脑与计算机接口铺平了道路.

关键词:
电脑电磁波解码的解码这是LSTM的LSTM.大脑计算机接口 (BCI)想象中的话语 想象中的话语内在的言语 内在的言语波纹散射转换 (WST) 是一种波纹散射转换.

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科学领域:

  • 神经科学是一个神经科学.
  • 机器学习 机器学习
  • 生物医学工程 生物医学工程

背景情况:

  • 大脑-计算机接口 (BCI) 提供了替代的通信和控制方法.
  • 从脑电图 (EEG) 信号进行虚拟语音识别是一个具有挑战性但有前途的领域.
  • 高效的特征提取和维度减少对于BCI性能至关重要.

研究的目的:

  • 开发和评估一种深度学习模型,用于基于语音的脑波模式识别.
  • 为了研究波纹散射转换用于EEG特征提取的有效性.
  • 评估一个低成本的多通道EEG系统的可行性,用于BCI应用.

主要方法:

  • 使用8通道EEG耳机和MATLAB 2023a进行数据采集.
  • 应用波波散射转换来减少EEG数据的维度和复杂性.
  • 采用长期短期记忆循环神经网络 (LSTM-RNN) 来分类四个想象的语音命令 (上,下,左,右).

主要成果:

  • 在想象式语音识别方面实现了92.50%的整体分类准确度.
  • 证明了高精度 (92.74%),回忆 (92.50%) 和F1得分 (92.62%).
  • 拟议的方法对实时BCI系统有希望.

结论:

  • 开发的深度学习方法有效地从EEG信号中解码想象中的语音.
  • 波纹散射转换是增强EEG特征提取的有价值的技术.
  • 这些发现支持开发可靠和可访问的基于语音的想象BCI.