TY - THES AU - Köllőd, Csaba Márton TI - Development of Brain-Computer Interfaces by using Deep Learning Technologies PB - Pázmány Péter Katolikus Egyetem (PPKE) PY - 2024 SP - 96 DO - 10.15774/PPKE.ITK.2024.001 UR - https://m2.mtmt.hu/api/publication/34529745 ID - 34529745 LA - English DB - MTMT ER - 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 - Köllőd, Csaba Márton AU - Adolf, András AU - Iván, Kristóf AU - Márton, Gergely AU - Ulbert, István TI - Deep Comparisons of Neural Networks from the EEGNet Family JF - ELECTRONICS (SWITZ) VL - 12 PY - 2023 IS - 12 SN - 2079-9292 DO - 10.3390/electronics12122743 UR - https://m2.mtmt.hu/api/publication/34028020 ID - 34028020 AB - A preponderance of brain–computer interface (BCI) publications proposing artificial neural networks for motor imagery (MI) electroencephalography (EEG) signal classification utilize one of the BCI Competition datasets. However, these databases encompass MI EEG data from a limited number of subjects, typically less than or equal to 10. Furthermore, the algorithms usually include only bandpass filtering as a means of reducing noise and increasing signal quality. In this study, we conducted a comparative analysis of five renowned neural networks (Shallow ConvNet, Deep ConvNet, EEGNet, EEGNet Fusion, and MI-EEGNet) utilizing open-access databases with a larger subject pool in conjunction with the BCI Competition IV 2a dataset to obtain statistically significant results. We employed the FASTER algorithm to eliminate artifacts from the EEG as a signal processing step and explored the potential for transfer learning to enhance classification results on artifact-filtered data. Our objective was to rank the neural networks; hence, in addition to classification accuracy, we introduced two supplementary metrics: accuracy improvement from chance level and the effect of transfer learning. The former is applicable to databases with varying numbers of classes, while the latter can underscore neural networks with robust generalization capabilities. Our metrics indicated that researchers should not disregard Shallow ConvNet and Deep ConvNet as they can outperform later published members of the EEGNet family. 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 - Köllőd, Csaba Márton AU - Eftimiu, Nikomidisz Jorgosz AU - Márton, Gergely AU - Ulbert, István ED - Szakál, Anikó TI - Classification of Semi-Automated Labeled MindRove Armband Recorded EMG Data T2 - IEEE Joint 22nd International Symposium on COMPUTATIONAL INTELLIGENCE and INFORMATICS and 8th International Conference on Recent Achievements in Mechatronics, Automation, Computer Science and Robotics (CINTI-MACRo 2022) PB - IEEE Hungary Section CY - Budapest SN - 9798350398823 PY - 2022 SP - 000381 EP - 000386 PG - 6 DO - 10.1109/CINTI-MACRo57952.2022.10029540 UR - https://m2.mtmt.hu/api/publication/34030729 ID - 34030729 LA - English DB - MTMT ER - TY - CHAP AU - Nikomidisz, Eftimiu AU - Köllőd, Csaba Márton AU - Ulbert, István AU - Márton, Gergely ED - Szakál, Anikó TI - A surface electromyography dataset for hand gesture recognition T2 - IEEE 20th Jubilee International Symposium on Intelligent Systems and Informatics (SISY 2022) PB - IEEE CY - Szabadka SN - 9781665489881 PY - 2022 SP - 115 EP - 120 PG - 6 DO - 10.1109/SISY56759.2022.10036305 UR - https://m2.mtmt.hu/api/publication/33631899 ID - 33631899 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 - CONF AU - Köllőd, Csaba Márton AU - Márton, Gergely AU - Ulbert, István TI - Multiple SVM based Brain Computer Interface for Cybathlon 2020 T2 - IBRO Workshop PY - 2020 SP - https://www.mitt2020.hu/abstracts UR - https://m2.mtmt.hu/api/publication/31345209 ID - 31345209 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 - TY - CONF AU - Köllőd, Csaba Márton TI - Developing of Brain-Computer Interfaces by using Deep Learning technologies T2 - PhD PROCEEDINGSANNUAL ISSUES OF THEDOCTORAL SCHOOLFACULTY OF INFORMATION TECHNOLOGY & BIONICS (2019) PY - 2019 SP - 22 UR - https://m2.mtmt.hu/api/publication/31318591 ID - 31318591 LA - English DB - MTMT ER -