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一种高效的深度学习方法,用于使用EEG信号进行自动语音识别.

Babu Chinta1, Madhuri Pampana2, Moorthi M3

  • 1Department of Information and Communication Engineering, Anna University, Chennai, India.

Computer methods in biomechanics and biomedical engineering
|February 17, 2025
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概括

本研究介绍了一种高效的深度学习方法 (EDLA),用于使用脑电图 (EEG) 信号识别扬声器. 这种新的方法达到95.2%的准确性,增强了脑计算机接口和语音障碍辅助技术.

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大脑与计算机的接口.电脑电流信号 电脑电流信号埃尔曼经常性神经网络甘特优化算法 甘特优化算法语音识别 语音识别 语言识别

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科学领域:

  • 生物医学工程 生物医学工程
  • 神经科学是一个神经科学.
  • 人工智能的人工智能

背景情况:

  • 电脑电图 (EEG) 信号为人机交互提供了潜力,但由于信号噪音和复杂性,在语音识别方面面临挑战.
  • 来自EEG的准确扬声器识别对于推进脑计算机接口 (BCI) 和辅助技术至关重要.

研究的目的:

  • 开发一种高效的深度学习方法 (EDLA),用于使用EEG信号进行强大的扬声器识别.
  • 将Gannet优化算法 (GOA) 与Elman反复神经网络 (ERNN) 集成,以改进基于EEG的扬声器识别.

主要方法:

  • 使用Savitzky-Golay过器进行EEG数据预处理.
  • 递归特征消除,以实现最佳特征选择.
  • 实施Gannet优化算法 (GOA) 和Elman反复神经网络 (ERNN) 进行扬声器识别.

主要成果:

  • 拟议的EDLA在Kara One数据集上实现了95.2%的扬声器识别准确度.
  • 与现有的基线方法相比,EDLA表现优越.
  • 该框架有效地解决了用于语音识别的EEG信号固有的噪音和复杂性.

结论:

  • 根据EDLA框架,基于EEG的扬声器识别技术取得了重大进展.
  • 这种方法对增强BCI和为语音障碍患者开发辅助技术充满希望.
  • 整合GOA和ERNN为复杂的神经信号处理提供了强大的解决方案.