Pub Date : 2024-01-30DOI: 10.1016/j.simpa.2024.100618
Kalpna Gahalaut, Harini Guruhappa
One of the main causes of Human-induced earthquakes is hydroelectric reservoir impoundment, a phenomenon known as Reservoir Triggered Seismicity (RTS). To assess the role of reservoir impoundment in triggering seismicity of a region, several codes are written in MATLAB based on Green’s function solution of poroelastic equations to simulate stress, pore pressure and change in fault stability. All these codes are embedded into a single user-friendly application software. This application takes various inputs on reservoir dimensions, reservoir loading time history, fault orientation, poroelastic properties of the medium for calculations, and presents results in simple graphics.
{"title":"RTSeismo: A new Matlab based Graphical User Interface tool for analysing triggered seismicity due to surface reservoir impoundment","authors":"Kalpna Gahalaut, Harini Guruhappa","doi":"10.1016/j.simpa.2024.100618","DOIUrl":"https://doi.org/10.1016/j.simpa.2024.100618","url":null,"abstract":"<div><p>One of the main causes of Human-induced earthquakes is hydroelectric reservoir impoundment, a phenomenon known as Reservoir Triggered Seismicity (RTS). To assess the role of reservoir impoundment in triggering seismicity of a region, several codes are written in MATLAB based on Green’s function solution of poroelastic equations to simulate stress, pore pressure and change in fault stability. All these codes are embedded into a single user-friendly application software. This application takes various inputs on reservoir dimensions, reservoir loading time history, fault orientation, poroelastic properties of the medium for calculations, and presents results in simple graphics.</p></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266596382400006X/pdfft?md5=474f072bfe7c9f725d70491eb079a2bd&pid=1-s2.0-S266596382400006X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139694807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-30DOI: 10.1016/j.simpa.2024.100617
Muhamad Fajar, Thomas Galih Satria, Francisco Maruli Panggabean, David, Galih Dea Pratama
One method of learning to drive, like operating a car, is to adopt a simulation approach with a strong focus on game elements. In our research, we developed the ProjectStir application, a car driving simulator that uses this game-centric approach. The app immerses users in a virtual representation of Asia’s urban environments known for their narrow streets. It is able to assess driver performance by tracking the number of checkpoints collected and the level of damage incurred. Additionally, the app features a leaderboard, highlighting that hitting more checkpoints and minimizing damage will result in a better ranking. ProjectStir is compatible with the PC platform and can be operated using a joystick, keyboard or steering wheel.
学习驾驶(如操作汽车)的一种方法是采用以游戏元素为主的模拟方法。在我们的研究中,我们开发了 ProjectStir 应用程序,这是一个采用这种以游戏为中心的方法的汽车驾驶模拟器。该应用让用户沉浸在以街道狭窄著称的亚洲城市环境的虚拟场景中。它能够通过跟踪收集的检查点数量和造成的损害程度来评估驾驶员的表现。此外,该应用程序还设有一个排行榜,突出显示击中更多检查点和将损害降至最低将获得更好的排名。ProjectStir 与 PC 平台兼容,可使用手柄、键盘或方向盘进行操作。
{"title":"ProjectStir: A driving car application to measure driving performance with game-centric approaches","authors":"Muhamad Fajar, Thomas Galih Satria, Francisco Maruli Panggabean, David, Galih Dea Pratama","doi":"10.1016/j.simpa.2024.100617","DOIUrl":"https://doi.org/10.1016/j.simpa.2024.100617","url":null,"abstract":"<div><p>One method of learning to drive, like operating a car, is to adopt a simulation approach with a strong focus on game elements. In our research, we developed the ProjectStir application, a car driving simulator that uses this game-centric approach. The app immerses users in a virtual representation of Asia’s urban environments known for their narrow streets. It is able to assess driver performance by tracking the number of checkpoints collected and the level of damage incurred. Additionally, the app features a leaderboard, highlighting that hitting more checkpoints and minimizing damage will result in a better ranking. ProjectStir is compatible with the PC platform and can be operated using a joystick, keyboard or steering wheel.</p></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2665963824000058/pdfft?md5=4c5722d3b1bb52fef2bf88dc0fdf9d41&pid=1-s2.0-S2665963824000058-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139748423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-29DOI: 10.1016/j.simpa.2024.100616
Awol Seid Ebrie , Young Jin Kim
In response to the NP-hard power scheduling problem, the pymops package is developed as a robust solution. The package operates within a multi-agent simulation environment, where power-generating units are represented as reinforcement learning (RL) agents. The environment is designed to account for a comprehensive range of constraints. It also accommodates thermal valve point effects (VPEs) within cost and emissions functions. Moreover, in cases of constraint violations, the environment makes real-time contextual adjustments. Within the environment, the power scheduling problem is broken down into sequential Markov decision processes (MDPs), which serve as inputs for training a deep RL model aimed at solving the optimization problem.
{"title":"pymops: A multi-agent simulation-based optimization package for power scheduling","authors":"Awol Seid Ebrie , Young Jin Kim","doi":"10.1016/j.simpa.2024.100616","DOIUrl":"https://doi.org/10.1016/j.simpa.2024.100616","url":null,"abstract":"<div><p>In response to the NP-hard power scheduling problem, the <span>pymops</span> package is developed as a robust solution. The package operates within a multi-agent simulation environment, where power-generating units are represented as reinforcement learning (RL) agents. The environment is designed to account for a comprehensive range of constraints. It also accommodates thermal valve point effects (VPEs) within cost and emissions functions. Moreover, in cases of constraint violations, the environment makes real-time contextual adjustments. Within the environment, the power scheduling problem is broken down into sequential Markov decision processes (MDPs), which serve as inputs for training a deep RL model aimed at solving the optimization problem.</p></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2665963824000046/pdfft?md5=651e2ac4e426bc6fbbf5275437df2a6b&pid=1-s2.0-S2665963824000046-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139675987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-22DOI: 10.1016/j.simpa.2024.100613
Hugo Gobato Souto, Amir Moradi
This paper presents a Python script that automates the estimation of Yang & Zhang’s stock realized volatility proxy for univariate and multivariate cases. Yang & Zhang’s realized volatility is a stock volatility proxy commonly used by financial researchers and practitioners due to its unbiasedness in the continuous limit, independence of the drift, and consistence in dealing with price jumps. The proposed script allows the efficient estimation of Yang & Zhang realized volatility with local data and the use of Yahoo Finance API.
本文介绍了一个 Python 脚本,该脚本可以自动估算单变量和多变量情况下杨和章的股票已实现波动率代理。Yang & Zhang 的已实现波动率是金融研究人员和从业人员常用的股票波动率替代指标,因为它在连续极限中无偏、独立于漂移,并且在处理价格跳跃时具有一致性。所提出的脚本允许利用本地数据和雅虎财经 API 高效估计杨和张的已实现波动率。
{"title":"Yang & Zhang’s realized volatility: Automated estimation in Python","authors":"Hugo Gobato Souto, Amir Moradi","doi":"10.1016/j.simpa.2024.100613","DOIUrl":"10.1016/j.simpa.2024.100613","url":null,"abstract":"<div><p>This paper presents a Python script that automates the estimation of Yang & Zhang’s stock realized volatility proxy for univariate and multivariate cases. Yang & Zhang’s realized volatility is a stock volatility proxy commonly used by financial researchers and practitioners due to its unbiasedness in the continuous limit, independence of the drift, and consistence in dealing with price jumps. The proposed script allows the efficient estimation of Yang & Zhang realized volatility with local data and the use of Yahoo Finance API.</p></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2665963824000010/pdfft?md5=1afaf63d20dfb8643cb3cc16647d772b&pid=1-s2.0-S2665963824000010-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139632106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-17DOI: 10.1016/j.simpa.2024.100615
AbdElRahman ElSaid
Ant-based Topology Search (ANTS) is a Neural Architecture Search (NAS) inspired by ant colony optimization (ACO). ANTS encodes the neural structure search space within a highly interconnected structure. Optimization agents, like ants, navigate this structure in search of an optimal neural topology. Continuous Ant-based Topology Search (CANTS) builds upon ANTS by replacing the discrete search space with a 3D continuous one. CANTS introduces a fourth dimension for potential neural synaptic weights, transitioning from NAS to NeuroEvolution (NE). This automates artificial neural network design without relying on backpropagation, reducing optimization time and offering a promising approach for machine learning applications.
蚁基拓扑搜索(ANTS)是一种神经结构搜索(NAS),其灵感来自蚁群优化(ACO)。ANTS 将神经结构搜索空间编码为一个高度互联的结构。优化代理就像蚂蚁一样,在这个结构中寻找最优的神经拓扑结构。基于蚂蚁的连续拓扑搜索(Continuous Ant-based Topology Search,CANTS)以 ANTS 为基础,用三维连续空间取代了离散搜索空间。CANTS 为潜在的神经突触权重引入了第四个维度,从 NAS 过渡到神经进化(NE)。这使人工神经网络设计自动化,无需依赖反向传播,减少了优化时间,为机器学习应用提供了一种前景广阔的方法。
{"title":"Continuous Ant-Based Neural Topology Search","authors":"AbdElRahman ElSaid","doi":"10.1016/j.simpa.2024.100615","DOIUrl":"https://doi.org/10.1016/j.simpa.2024.100615","url":null,"abstract":"<div><p>Ant-based Topology Search (ANTS) is a Neural Architecture Search (NAS) inspired by ant colony optimization (ACO). ANTS encodes the neural structure search space within a highly interconnected structure. Optimization agents, like ants, navigate this structure in search of an optimal neural topology. Continuous Ant-based Topology Search (CANTS) builds upon ANTS by replacing the discrete search space with a 3D continuous one. CANTS introduces a fourth dimension for potential neural synaptic weights, transitioning from NAS to NeuroEvolution (NE). This automates artificial neural network design without relying on backpropagation, reducing optimization time and offering a promising approach for machine learning applications.</p></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2665963824000034/pdfft?md5=097fa8999d88be8731a7066fd42c7a07&pid=1-s2.0-S2665963824000034-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139549242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-15DOI: 10.1016/j.simpa.2024.100614
Manuel Domínguez-Dorado , Francisco J. Rodríguez-Pérez , Jesús Galeano-Brajones , Jesús Calle-Cancho , David Cortés-Polo
The implementation of a holistic cybersecurity approach involves engaging multiple functional areas within the organization, each assigned specific actions to achieve strategic cybersecurity objectives. These actions – there can be numerous permutations of them – have associated costs, expertise requirements. Selecting the right combinations requires careful analysis and consideration, leading to time-consuming deliberations and potential conflicts. Identifying inadequate combinations that fail to meet strategic goals also requires significant effort. To streamline this process, we developed FLECO (Fast, Lightweight, and Efficient Cybersecurity Optimization), an adaptable multi-objective genetic algorithm that enables near-instantaneous identification of feasible cross-functional combinations. It serves as a foundation for the cybersecurity workforce to reach a consensus.
{"title":"FLECO: A tool to boost the adoption of holistic cybersecurity management","authors":"Manuel Domínguez-Dorado , Francisco J. Rodríguez-Pérez , Jesús Galeano-Brajones , Jesús Calle-Cancho , David Cortés-Polo","doi":"10.1016/j.simpa.2024.100614","DOIUrl":"https://doi.org/10.1016/j.simpa.2024.100614","url":null,"abstract":"<div><p>The implementation of a holistic cybersecurity approach involves engaging multiple functional areas within the organization, each assigned specific actions to achieve strategic cybersecurity objectives. These actions – there can be numerous permutations of them – have associated costs, expertise requirements. Selecting the right combinations requires careful analysis and consideration, leading to time-consuming deliberations and potential conflicts. Identifying inadequate combinations that fail to meet strategic goals also requires significant effort. To streamline this process, we developed FLECO (Fast, Lightweight, and Efficient Cybersecurity Optimization), an adaptable multi-objective genetic algorithm that enables near-instantaneous identification of feasible cross-functional combinations. It serves as a foundation for the cybersecurity workforce to reach a consensus.</p></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2665963824000022/pdfft?md5=bca6ee9adf4194009c51228dac62315c&pid=1-s2.0-S2665963824000022-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139504128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-21DOI: 10.1016/j.simpa.2023.100612
Mostofa Kamal Rasel
FAW-encoding is an open-source C library that facilitates a memory efficient compression of an array of integers. An integer in an array either lags behind or never exceeds its original place in that array after encoded by FAW-encoding. Therefore, FAW-encoding causes in-place compression of an array of integers that never needs memory allocation to store the encoded data. Due to these special properties, FAW-encoding optimizes algorithms, such as, graph mining and joining, that generally produce large intermediate results. Besides encoding, the open-source C library comprises with methods for decoding or searching an encoded array and intersecting and merging encoded arrays.
FAW-encoding 是一个开放源码的 C 语言库,有助于对整数数组进行内存高效压缩。经过 FAW-encoding 编码后,数组中的整数要么滞后,要么永远不会超出其在数组中的原始位置。因此,FAW 编码可对整数数组进行就地压缩,而无需分配内存来存储编码数据。由于这些特殊属性,FAW-encoding 可以优化通常会产生大量中间结果的算法,如图挖掘和连接。除了编码,开源 C 库还包括解码或搜索编码数组以及交叉和合并编码数组的方法。
{"title":"FAW: Flag aligned word-based encoding for in-place integers compression","authors":"Mostofa Kamal Rasel","doi":"10.1016/j.simpa.2023.100612","DOIUrl":"10.1016/j.simpa.2023.100612","url":null,"abstract":"<div><p>FAW-encoding is an open-source C library that facilitates a memory efficient compression of an array of integers. An integer in an array either lags behind or never exceeds its original place in that array after encoded by FAW-encoding. Therefore, FAW-encoding causes in-place compression of an array of integers that never needs memory allocation to store the encoded data. Due to these special properties, FAW-encoding optimizes algorithms, such as, graph mining and joining, that generally produce large intermediate results. Besides encoding, the open-source C library comprises with methods for decoding or searching an encoded array and intersecting and merging encoded arrays.</p></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2665963823001495/pdfft?md5=eae6d3c541ce5cd4a8a8765336508bd9&pid=1-s2.0-S2665963823001495-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138991928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-12DOI: 10.1016/j.simpa.2023.100609
Komi Mensah Agboka , John Odindi , Elfatih M. Abdel-Rahman , Onisimo Mutanga , Henri E.Z. Tonnang
iPydisp is an open-source, Python-based tool designed to model the spatio-temporal dispersal of insect species such as parasitoids. It provides an accessible interface for interactive spatio-temporal analysis, using cellular automata algorithms for easy data processing and updates. The tool’s main features include intuitive data loading, easy setting of dispersal constraints, and dynamic visualization of dispersal patterns. iPydisp has the potential to improve our understanding of parasitoid dispersal and enhance pest management strategies.
{"title":"A visual and spatial tool for tracking, mapping and forecasting the dispersal of biological control agents","authors":"Komi Mensah Agboka , John Odindi , Elfatih M. Abdel-Rahman , Onisimo Mutanga , Henri E.Z. Tonnang","doi":"10.1016/j.simpa.2023.100609","DOIUrl":"https://doi.org/10.1016/j.simpa.2023.100609","url":null,"abstract":"<div><p><em>iPydisp</em> is an open-source, Python-based tool designed to model the spatio-temporal dispersal of insect species such as parasitoids. It provides an accessible interface for interactive spatio-temporal analysis, using cellular automata algorithms for easy data processing and updates. The tool’s main features include intuitive data loading, easy setting of dispersal constraints, and dynamic visualization of dispersal patterns. <em>iPydisp</em> has the potential to improve our understanding of parasitoid dispersal and enhance pest management strategies.</p></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266596382300146X/pdfft?md5=c5a4b8ec77761df095f66aedc4ce8faa&pid=1-s2.0-S266596382300146X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138656233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-12DOI: 10.1016/j.simpa.2023.100608
José Ángel Martín-Baos , Ricardo García-Ródenas , María Luz López García , Luis Rodriguez-Benitez
This paper presents a software package developed in Python that allows the application of the technique known as Kernel Logistic Regression (KLR), a Machine Learning (ML) tool, to the problem of transport demand prediction. More concretely, it permits the specification of a series of models using KLR and their estimation by means of a Penalised Maximum Likelihood Estimation (PMLE) procedure providing a set of goodness-of-fit indicators and the application of model validation techniques. Another functionality is that it allows to extract from the model several indicators such as the Willingness to Pay (WTP) or the Value of Time (VOT).
{"title":"PyKernelLogit: Penalised maximum likelihood estimation of Kernel Logistic Regression in Python","authors":"José Ángel Martín-Baos , Ricardo García-Ródenas , María Luz López García , Luis Rodriguez-Benitez","doi":"10.1016/j.simpa.2023.100608","DOIUrl":"https://doi.org/10.1016/j.simpa.2023.100608","url":null,"abstract":"<div><p>This paper presents a software package developed in Python that allows the application of the technique known as Kernel Logistic Regression (KLR), a Machine Learning (ML) tool, to the problem of transport demand prediction. More concretely, it permits the specification of a series of models using KLR and their estimation by means of a Penalised Maximum Likelihood Estimation (PMLE) procedure providing a set of goodness-of-fit indicators and the application of model validation techniques. Another functionality is that it allows to extract from the model several indicators such as the Willingness to Pay (WTP) or the Value of Time (VOT).</p></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2665963823001458/pdfft?md5=49b2b660e031d0b50aed0fe8d06e1680&pid=1-s2.0-S2665963823001458-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138656234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-11DOI: 10.1016/j.simpa.2023.100610
Arthur Desbois, Tristan Venot, Fabrizio De Vico Fallani, Marie-Constance Corsi
Brain–Computer Interface (BCI) systems allow to perform actions by translating brain activity into commands. Such systems require training a classification algorithm to discriminate between mental states, using specific features from the brain signals. This step is crucial and presents specific constraints in clinical contexts.
HappyFeat is an open-source software making BCI experiments easier in such contexts: effortlessly extracting and selecting adequate features for training, in a single GUI. Novel features based on Functional Connectivity can be used, allowing graph-oriented approaches. We describe HappyFeat’s mechanisms, showing its performances in typical use cases, and showcasing how to compare different types of features.
{"title":"HappyFeat—An interactive and efficient BCI framework for clinical applications","authors":"Arthur Desbois, Tristan Venot, Fabrizio De Vico Fallani, Marie-Constance Corsi","doi":"10.1016/j.simpa.2023.100610","DOIUrl":"https://doi.org/10.1016/j.simpa.2023.100610","url":null,"abstract":"<div><p>Brain–Computer Interface (BCI) systems allow to perform actions by translating brain activity into commands. Such systems require training a classification algorithm to discriminate between mental states, using specific features from the brain signals. This step is crucial and presents specific constraints in clinical contexts.</p><p><em>HappyFeat</em> is an open-source software making BCI experiments easier in such contexts: effortlessly extracting and selecting adequate features for training, in a single GUI. Novel features based on Functional Connectivity can be used, allowing graph-oriented approaches. We describe HappyFeat’s mechanisms, showing its performances in typical use cases, and showcasing how to compare different types of features.</p></div>","PeriodicalId":29771,"journal":{"name":"Software Impacts","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2665963823001471/pdfft?md5=5153af78ee15d29c255a763514aedc4b&pid=1-s2.0-S2665963823001471-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138570414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}