神经信号,机器学习以及内在语音识别的未来
Adiba Tabassum Chowdhury1, Ahmed Hassanein2, Aous N Al Shibli2
1Department of Electrical and Electronic Engineering, University of Dhaka, Dhaka, Bangladesh.
Frontiers in human neuroscience
|July 25, 2025
概括
机器学习 (ML) 通过分析神经信号来解码内在语言. 本综述综合了内在语音识别 (ISR) 的ML方法,推进脑计算机接口和辅助技术.
科学领域:
- 神经科学是一个神经科学.
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 内部语音识别 (ISR) 是一个新兴领域,具有大脑与计算机接口 (BCI) 和辅助技术的潜力.
- 解码内在语言依赖于分析复杂的神经信号.
- 机器学习 (ML) 为解释这些信号提供了强大的工具.
研究的目的:
- 审查和综合ISR的ML技术.
- 分析神经信号分类的传统和深度学习方法.
- 讨论ISR的ML中的挑战,预处理和未来方向.
主要方法:
- 对ML算法的比较分析,包括支持矢量机器 (SVM),随机森林和卷积神经网络 (CNN).
- 对神经数据的信号采集和预处理技术的审查.
- 在数学框架内综合现有的ISR方法.
主要成果:
- 像CNN这样的深度学习方法对于在ISR中捕捉动态,非线性大脑活动模式是有效的.
- 各种ML技术增强了ISR神经信号的分析和分类.
- 确定了信号采集和质量的挑战,并讨论了预处理解决方案.
结论:
- ML对于推进ISR至关重要,对辅助通信,BCI和认知监测有重大影响.
- 未来的ML进步有望克服ISR当前的技术局限性.
- 本综述提供了一个结构化的框架和比较分析,以指导未来的ISR研究.
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