基于变压器网络的SEEG情感识别,具有通道选择和可解释性.
IEEE journal of biomedical and health informatics
|July 3, 2025
概括
一个基于空间变压器的新型混合网络 (STHN) 使用立体电脑图 (SEEG) 数据有效地识别情绪. 这种脑计算机接口 (BCI) 技术在治疗情绪障碍方面表现有前途.
科学领域:
- 神经科学是一个神经科学.
- 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:
- 脑电脑接口 (BCI) 技术对于治疗耐火情绪障碍至关重要.
- 立体电脑摄影 (SEEG) 从深层大脑结构和皮质提供精确的神经活动记录.
- 对于开发情绪识别BCI,SEEG具有显著的潜力.
研究的目的:
- 使用SEEG数据开发和评估一种用于情绪识别的新算法.
- 从九个受试者的SEEG记录中构建一个情绪数据集.
- 根据现有方法评估拟议的基于空间变压器的混合网络 (STHN) 的性能.
主要方法:
- 收集了9名受试者的SEEG数据,以创建一个情绪数据集.
- 开发了一个基于空间变压器的混合网络 (STHN) 用于SEEG情感识别.
- 与基线方法 (如EEGNet,TSception和深卷积神经网络) 相比,评估了STHN的性能.
主要成果:
- STHN实现了83.56%的三重分类准确度,超过了基线方法.
- STHN展示了道权重和选择能力,识别了用于情感识别的关键大脑区域.
- 高重量通道主要位于与情绪相关的区域,如额叶,叶和海马,表明模型可解释性.
结论:
- 使用STHN开发的SEEG情绪识别算法是有效的,并提供一定程度的可解释性.
- 这种新的方法在监测和治疗耐火情绪障碍患者方面具有重大潜力.
- 这项研究代表了基于SEEG的情绪识别算法的首次开发.
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