Brain-Computer Interface (BCI) systems allow users to perform actions by translating their brain activity into commands. Such systems usually need a training phase, consisting in training a classification algorithm to discriminate between mental states using specific features from the recorded signals. This phase of feature selection and training is crucial for BCI performance and presents specific constraints to be met in a clinical context, such as post-stroke rehabilitation. In this paper, we present HappyFeat, a software making Motor Imagery (MI) based BCI experiments easier, by gathering all necessary manipulations and analysis in a single convenient GUI and via automation of experiment or analysis parameters. The resulting workflow allows for effortlessly selecting the best features, helping to achieve good BCI performance in time-constrained environments. Alternative features based on Functional Connectivity can be used and compared or combined with Power Spectral Density, allowing a network-oriented approach. We then give details of HappyFeat's main mechanisms, and a review of its performances in typical use cases. We also show that it can be used as an efficient tool for comparing different metrics extracted from the signals, to train the classification algorithm. To this end, we show a comparison between the commonly-used Power Spectral Density and network metrics based on Functional Connectivity. HappyFeat is available as an open-source project which can be freely downloaded on GitHub.

翻译：脑机接口（BCI）系统通过将用户大脑活动转化为指令来执行操作。此类系统通常需要训练阶段，即通过记录信号中的特定特征训练分类算法以区分心理状态。这一特征选择与训练阶段对BCI性能至关重要，且需满足临床应用（如脑卒中后康复）中的特殊约束。本文提出HappyFeat软件，该软件通过将所有必要操作与分析整合至便捷的图形用户界面（GUI），并实现实验参数或分析参数的自动化，简化了基于运动想象（MI）的BCI实验流程。由此产生的工作流支持轻松选择最优特征，有助于在时间受限环境中实现良好BCI性能。基于功能连接的alternative特征可被使用、比较或与功率谱密度相结合，从而支持面向网络的分析方法。随后，本文详述HappyFeat的主要机制，并回顾其在典型使用案例中的性能表现。同时证明其可作为比较信号提取指标以训练分类算法的有效工具。为此，我们展示了常用功率谱密度与基于功能连接的网络指标之间的对比结果。HappyFeat作为开源项目可在GitHub上自由下载。