症分类的进展:当前的趋势和未来的方向
Rajani Rai B1, Karunakara Rai B2, Mamatha A S3
1Dept.of ECE, Vivekananda College of Engineering and Technology, Puttur, D.K., India.
MethodsX
|July 14, 2025
概括
这项调查探讨了使用机器学习和深度学习与脑电图 (EEG) 信号用于的分类. 它强调了混合模型和未来的人工智能驱动的个性化治疗方法.
科学领域:
- 神经学 神经学
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 使用脑电图 (EEG) 信号进行症分类面临挑战.
- 机器学习 (ML) 和深度学习 (DL) 提供先进的解决方案.
- 准确分类焦点和非焦点对于治疗至关重要.
研究的目的:
- 通过使用EEG信号,提供关于分类焦点和非焦点的综合调查.
- 强调ML和DL在克服分类挑战中的作用.
- 综合最先进的技术,并提出新的方向,以提高分类精度.
主要方法:
- 关于EEG信号分析的ML和DL方法的最新进展的审查.
- 专注于混合模型,将信号处理与ML算法结合起来.
- 对症分类当前挑战和潜在解决方案的分析.
主要成果:
- ML和DL显著提高了EEG症分类的准确性.
- 混合型号在集成传统和先进技术方面显示出前景.
- 确定了当前分类方法的关键突破和局限性.
结论:
- 人工智能驱动的方法,特别是混合模型,可以改善的分类.
- 未来的研究应该专注于多式联运数据集成和实时扣押预测.
- 人工智能有可能实现个性化的治疗策略.
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