@article{MTMT:34141034,
	title = {Closed loop BCI system for Cybathlon 2020},
	url = {https://m2.mtmt.hu/api/publication/34141034},
	author = {Köllőd, Csaba Márton and Adolf, András and Márton, Gergely and Wahdow, Moutz and Fadel, Ward and Ulbert, István},
	doi = {10.1080/2326263X.2023.2254463},
	journal-iso = {BRAIN-COMP INTERF},
	journal = {BRAIN-COMPUTER INTERFACES},
	volume = {10},
	unique-id = {34141034},
	issn = {2326-263X},
	year = {2023},
	eissn = {2326-2621},
	pages = {114-128},
	orcid-numbers = {Köllőd, Csaba Márton/0000-0003-3817-6709; Ulbert, István/0000-0001-9941-9159}
}

@article{MTMT:33271235,
	title = {Multi frequency band fusion method for EEG signal classification},
	url = {https://m2.mtmt.hu/api/publication/33271235},
	author = {Wahdow, Moutz and Alnaanah, Mahmoud and Fadel, Ward and Adolf, András and Köllőd, Csaba Márton and Ulbert, István},
	doi = {10.1007/s11760-022-02399-6},
	journal-iso = {SIGNAL IMAGE VIDEO PROCES},
	journal = {SIGNAL IMAGE AND VIDEO PROCESSING},
	volume = {17},
	unique-id = {33271235},
	issn = {1863-1703},
	abstract = {This paper proposes a novel convolutional neural network (CNN) fusion method for electroencephalography (EEG) motor imagery (MI) signal classification. The method is named MFBF, which stands for multifrequency band fusion. The MFBF method relies on filtering the input signal with different frequency bands and feeding each band signal to a duplicate of a CNN model; then, all duplicates are concatenated to form a fusion model. This paper also introduces the second release of Coleeg software, which is used for evaluation. The MFBF method has the advantage of the flexibility of choosing any model and any number of frequency bands. In the experimental evaluation, the CNN1D model and three frequency bands were used to form the CNN1D_MFBF model, and it was evaluated against the EEGNet_fusion model on three different datasets, which are: Physionet, BCI competition IV-2a, and a dataset from the Hungarian Academy of Sciences Research Centre for Natural Sciences (MTA-TTK). The CNN1D_MFBF model had comparable or better accuracy results with less than one-fifth of the training time, which is a significant advantage for the proposed method.},
	year = {2023},
	eissn = {1863-1711},
	pages = {1883-1887},
	orcid-numbers = {Köllőd, Csaba Márton/0000-0003-3817-6709; Ulbert, István/0000-0001-9941-9159}
}

@{MTMT:31400880,
	title = {Chessboard EEG Images Classification for BCI Systems Using Deep Neural Network},
	url = {https://m2.mtmt.hu/api/publication/31400880},
	author = {Fadel, Ward and Wahdow, Moutz and Köllőd, Csaba Márton and Márton, Gergely and Ulbert, István},
	booktitle = {Bio-inspired Information and Communication Technologies},
	doi = {10.1007/978-3-030-57115-3_8},
	unique-id = {31400880},
	year = {2020},
	pages = {97-104},
	orcid-numbers = {Köllőd, Csaba Márton/0000-0003-3817-6709; Ulbert, István/0000-0001-9941-9159}
}

@inproceedings{MTMT:31318596,
	title = {Multi-Class Classification of Motor Imagery EEG Signals Using Image-Based Deep Recurrent Convolutional Neural Network},
	url = {https://m2.mtmt.hu/api/publication/31318596},
	author = {Fadel, Ward and Köllőd, Csaba Márton and Wahdow, Moutz and Ibrahim, Yahya and Ulbert, István},
	booktitle = {2020 8th International Winter Conference on Brain-Computer Interface (BCI)},
	doi = {10.1109/BCI48061.2020.9061622},
	unique-id = {31318596},
	year = {2020},
	orcid-numbers = {Köllőd, Csaba Márton/0000-0003-3817-6709; Ulbert, István/0000-0001-9941-9159}
}