TY  - JOUR
AU  - Köllőd, Csaba Márton
AU  - Adolf, András
AU  - Márton, Gergely
AU  - Wahdow, Moutz
AU  - Fadel, Ward
AU  - Ulbert, István
TI  - Closed loop BCI system for Cybathlon 2020
JF  - BRAIN-COMPUTER INTERFACES
J2  - BRAIN-COMP INTERF
VL  - 10
PY  - 2023
IS  - 2-4
SP  - 114
EP  - 128
PG  - 15
SN  - 2326-263X
DO  - 10.1080/2326263X.2023.2254463
UR  - https://m2.mtmt.hu/api/publication/34141034
ID  - 34141034
LA  - English
DB  - MTMT
ER  - 

TY  - JOUR
AU  - Wahdow, Moutz
AU  - Alnaanah, Mahmoud
AU  - Fadel, Ward
AU  - Adolf, András
AU  - Köllőd, Csaba Márton
AU  - Ulbert, István
TI  - Multi frequency band fusion method for EEG signal classification
JF  - SIGNAL IMAGE AND VIDEO PROCESSING
J2  - SIGNAL IMAGE VIDEO PROCES
VL  - 17
PY  - 2023
IS  - 5
SP  - 1883
EP  - 1887
PG  - 5
SN  - 1863-1703
DO  - 10.1007/s11760-022-02399-6
UR  - https://m2.mtmt.hu/api/publication/33271235
ID  - 33271235
AB  - 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.
LA  - English
DB  - MTMT
ER  - 

TY  - CHAP
AU  - Fadel, Ward
AU  - Wahdow, Moutz
AU  - Köllőd, Csaba Márton
AU  - Márton, Gergely
AU  - Ulbert, István
ED  - Guo, Weisi
ED  - Mahfuz, Mohammad Upal
ED  - Lin, Lin
ED  - Nakano, Tadashi
ED  - Chen, Yifan
TI  - Chessboard EEG Images Classification for BCI Systems Using Deep Neural Network
T2  - Bio-inspired Information and Communication Technologies
PB  - Springer Netherlands
CY  - Cham
SN  - 9783030571146
T3  - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering (LNICST) ; 329.
PY  - 2020
SP  - 97
EP  - 104
PG  - 8
DO  - 10.1007/978-3-030-57115-3_8
UR  - https://m2.mtmt.hu/api/publication/31400880
ID  - 31400880
LA  - English
DB  - MTMT
ER  - 

TY  - CHAP
AU  - Fadel, Ward
AU  - Köllőd, Csaba Márton
AU  - Wahdow, Moutz
AU  - Ibrahim, Yahya
AU  - Ulbert, István
ED  - Institute of Electrical and Electronics Engineers Incorporated, (IEEE)
TI  - Multi-Class Classification of Motor Imagery EEG Signals Using Image-Based Deep Recurrent Convolutional Neural Network
T2  - 2020 8th International Winter Conference on Brain-Computer Interface (BCI)
PB  - Institute of Electrical and Electronics Engineers (IEEE)
CY  - Piscataway (NJ)
SN  - 9781728147062
PY  - 2020
PG  - 4
DO  - 10.1109/BCI48061.2020.9061622
UR  - https://m2.mtmt.hu/api/publication/31318596
ID  - 31318596
LA  - English
DB  - MTMT
ER  -