Paper Title
EEG Motor Imagery Signal Classification with Coleeg Open-Source Software

Electroencephalography (EEG) has many medical and technological applications, one of which is brain-computer interface (BCI). In BCI, the EEG signals are classified and used to represent the corresponding subject's motor imagery (MI) events. BCI gives hope to people with movement difficulties, such as amputees and paralyzed people. Convolutional neural networks (CNNs) have been proven powerful for EEG signal classification and have been used intensively by my researchers. One major problem in research is that the code is not published openly and is not designed to facilitate the application of different research aspects. Coleeg software has been developed to address this problem. Coleeg is an opensource tool that is designed to facilitate research in EEG signal classification using CNNs. This paper presents the 3rd release of Coleeg and describes its main features. Further analysis has been carried out for the CNN1D_MFBF model, which has been developed and tested using Coleeg software in previously published work. Keywords - Coleeg, Motor Imagery, EEG, Fusion CNN, BCI, Open Source