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：脑机接口系统允许用户通过将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，即通过从记录信号中提取特定特征，训练分类算法以区分不同心理状态。这一特征选择与训练阶段对脑机接口性能至关重要，且在临床场景（如卒中后康复）中需满足特定约束条件。本文提出HappyFeat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），并自动化实验或分析参数，简化基于运动想象的脑机接口实验。该工作流可轻松实现最优特征筛选，助力在时间受限场景下达成良好脑机接口性能。基于功能连接的替代特征可与功率谱密度结合使用、比较或融合，从而支持网络导向的研究方法。我们随后详述HappyFeat的核心机制，并评述其在典型用例中的性能表现。研究还表明，该框架可作为比较信号中不同指标以训练分类算法的有效工具。为此，我们对比了常用功率谱密度与基于功能连接的网络指标。HappyFeat作为开源项目发布，可于GitHub免费下载。