Department of Electrical and Computer Engineering, American University of Beirut, Lebanon; School of Electrical and Computer Engineering, University of Oklahoma, USA; Laureate Institute for Brain Research, OK, USA.
Department of Electrical and Computer Engineering, American University of Beirut, Lebanon.
Artif Intell Med. 2018 Mar;86:1-8. doi: 10.1016/j.artmed.2018.01.001. Epub 2018 Feb 1.
Recent technological advances in machine learning offer the possibility of decoding complex datasets and discern latent patterns. In this study, we adopt Liquid State Machines (LSM) to recognize the emotional state of an individual based on EEG data. LSM were applied to a previously validated EEG dataset where subjects view a battery of emotional film clips and then rate their degree of emotion during each film based on valence, arousal, and liking levels. We introduce LSM as a model for an automatic feature extraction and prediction from raw EEG with potential extension to a wider range of applications. We also elaborate on how to exploit the separation property in LSM to build a multipurpose and anytime recognition framework, where we used one trained model to predict valence, arousal and liking levels at different durations of the input. Our simulations showed that the LSM-based framework achieve outstanding results in comparison with other works using different emotion prediction scenarios with cross validation.
最近机器学习领域的技术进步为解码复杂数据集和识别潜在模式提供了可能。在这项研究中，我们采用液体状态机（LSM）来根据 EEG 数据识别个体的情绪状态。LSM 被应用于一个经过验证的 EEG 数据集，该数据集的实验对象观看一系列情感片段，并根据效价、唤醒度和喜好度来评估每个片段的情绪程度。我们引入 LSM 作为一种从原始 EEG 中自动提取特征和进行预测的模型，具有潜在的更广泛的应用扩展。我们还详细阐述了如何利用 LSM 的分离特性来构建一个多用途和随时可用的识别框架，我们使用一个经过训练的模型来预测输入不同持续时间的效价、唤醒度和喜好度。我们的模拟结果表明，与使用不同情绪预测场景进行交叉验证的其他工作相比，基于 LSM 的框架取得了优异的结果。
