The goal of emotional brain state classification on functional MRI (fMRI) data is to recognize brain activity patterns related to specific emotion tasks performed by subjects during an experiment. Distinguishing emotional brain states from other brain states using fMRI data has proven to be challenging due to two factors: a difficulty to generate fast yet accurate predictions in short time frames, and a difficulty to extract emotion features which generalize to unseen subjects. To address these challenges, we conducted an experiment in which 22 subjects viewed pictures designed to stimulate either negative, neutral or rest emotional responses while their brain activity was measured using fMRI. We then developed two distinct Convolution-based approaches to decode emotional brain states using only spatial information from single, minimally pre-processed (slice timing and realignment) fMRI volumes. In our first approach, we trained a 1D Convolutional Network (84.9% accuracy; chance level 33%) to classify 3 emotion conditions using One-way Analysis of Variance (ANOVA) voxel selection combined with hyperalignment. In our second approach, we trained a 3D ResNet-50 model (78.0% accuracy; chance level 50%) to classify 2 emotion conditions from single 3D fMRI volumes directly. Our Convolutional and Residual classifiers successfully learned group-level emotion features and could decode emotion conditions from fMRI volumes in milliseconds. These approaches could potentially be used in brain computer interfaces and real-time fMRI neurofeedback research.
翻译:在功能 MRI (fMRI) 数据中,情感大脑状态分类的目标是识别与实验对象执行的特定情感任务有关的大脑活动模式。使用 FMRI 数据将其他大脑国家的情感大脑状态与使用FMRI 数据的其他大脑状态区别开来,这证明具有挑战性,原因有二:难以在短时间框架内生成快速而准确的预测,难以提取情感特征,这些特征一般化为看不见的主体。为了应对这些挑战,我们进行了一项实验,在实验中,22个对象观看了旨在刺激消极、中立或休息情绪反应的图片,而其大脑活动则使用 FMRI 进行测量。然后,我们开发了两种不同的基于 Convolution-Cond-50 模式(78.0%的准确性能;50级的智商预处理) FRI 数量。在我们的第一个方法中,我们训练了1D 革命网络网络(84.9%的精度;机会的33%) 将三种情绪状况分类为三种情绪状况,使用单向感官分析(ANOVA) 和超声调。在第二个方法中,我们训练了3DNet- 50 模型(78.80 准确性) ; 调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调调,从我们C.