TY - CHAP AU - Wang, Zeyu AU - Juhász, Zoltán ED - Zeinalipour, Demetris ED - Blanco Heras, Dora ED - Pallis, George ED - Herodotou, Herodotos ED - Trihinas, Demetris ED - Balouek, Daniel ED - Diehl, Patrick ED - Cojean, Terry ED - Fürlinger, Karl ED - Kirkeby, Maja Hanne ED - Nardellli, Matteo ED - Di Sanzo, Pierangelo TI - Massively Parallel EEG Algorithms for Pre-exascale Architectures T2 - Euro-Par 2023: Parallel Processing Workshops PB - Springer Nature Switzerland CY - Cham SN - 9783031488030 T3 - Lecture Notes in Computer Science, ISSN 0302-9743 ; 14352. PY - 2024 SP - 290 EP - 295 PG - 6 DO - 10.1007/978-3-031-48803-0_34 UR - https://m2.mtmt.hu/api/publication/34797365 ID - 34797365 LA - English DB - MTMT ER - TY - BOOK AU - Juhász, Zoltán AU - Gyulai, Ádám AU - Mohamed, F. Issa AU - Nagy, Zoltán TI - Kvantitatív, sok-csatornás EEG elemzés, mint vizsgáló módszer a post-stroke állapotok leírására PY - 2023 UR - https://m2.mtmt.hu/api/publication/34554081 ID - 34554081 LA - Hungarian DB - MTMT ER - TY - CHAP AU - Suhaili, Iffah Syafiqah binti AU - Juhász, Zoltán ED - Vassányi, István ED - Fogarassyné Vathy, Ágnes TI - Uncovering Neural Saccadic Responses using EEG during Natural Viewing of Paintings T2 - Orvosi informatika. A XXXVI. Neumann Kollokvium konferencia-kiadványa PB - Neumann János Számítógép-tudományi Társaság CY - Veszprém SN - 9789633962725 PY - 2023 SP - 78 EP - 80 PG - 3 UR - https://m2.mtmt.hu/api/publication/34452172 ID - 34452172 LA - English DB - MTMT ER - TY - CHAP AU - Wang, Zeyu AU - Juhász, Zoltán ED - Vassányi, István ED - Fogarassyné Vathy, Ágnes TI - Implementation strategies for EEG processing on multi-GPU computing systems T2 - Orvosi informatika. A XXXVI. Neumann Kollokvium konferencia-kiadványa PB - Neumann János Számítógép-tudományi Társaság CY - Veszprém SN - 9789633962725 PY - 2023 SP - 81 EP - 89 PG - 9 UR - https://m2.mtmt.hu/api/publication/34452166 ID - 34452166 LA - English DB - MTMT ER - TY - JOUR AU - Wang, Zeyu AU - Juhász, Zoltán TI - Efficient GPU Implementation of the Multivariate Empirical Mode Decomposition Algorithm JF - JOURNAL OF COMPUTATIONAL SCIENCE J2 - J COMPUT SCI VL - 2023 PY - 2023 SP - 102180 SN - 1877-7503 DO - 10.1016/j.jocs.2023.102180 UR - https://m2.mtmt.hu/api/publication/34346956 ID - 34346956 LA - English DB - MTMT ER - TY - JOUR AU - Suhaili, Iffah Syafiqah binti AU - Juhász, Zoltán AU - Nagy, Zoltán TI - ON THE PERCEPTION OF PAINTINGS: A HIGH-DENSITY EEG STUDY JF - IBRO NEUROSCIENCE REPORTS J2 - IBRO NEUROSCI REP VL - 15 PY - 2023 SP - S900 EP - S901 SN - 2667-2421 DO - 10.1016/j.ibneur.2023.08.1888 UR - https://m2.mtmt.hu/api/publication/34308780 ID - 34308780 LA - English DB - MTMT ER - TY - JOUR AU - Ihász, Petra AU - Benyhe, András AU - Sáry, Gyula AU - Juhász, Zoltán AU - Navracsics, Judit TI - Phonological awareness of bilinguals in visual word recognition JF - STRANI JEZICI J2 - STRAN JEZ VL - 52 PY - 2023 IS - 1 SP - 49 EP - 73 PG - 25 SN - 0351-0840 DO - 10.22210/strjez/52-1/3 UR - https://m2.mtmt.hu/api/publication/34222679 ID - 34222679 LA - English DB - MTMT ER - TY - JOUR AU - Wang, Zeyu AU - Juhász, Zoltán TI - GPU Implementation of the Improved CEEMDAN Algorithm for Fast and Efficient EEG Time–Frequency Analysis JF - SENSORS J2 - SENSORS-BASEL VL - 23 PY - 2023 IS - 20 SN - 1424-8220 DO - 10.3390/s23208654 UR - https://m2.mtmt.hu/api/publication/34214301 ID - 34214301 AB - Time–frequency analysis of EEG data is a key step in exploring the internal activities of the human brain. Studying oscillations is an important part of the analysis, as they are thought to provide the underlying mechanism for communication between neural assemblies. Traditional methods of analysis, such as Short-Time FFT and Wavelet Transforms, are not ideal for this task due to the time–frequency uncertainty principle and their reliance on predefined basis functions. Empirical Mode Decomposition and its variants are more suited to this task as they are able to extract the instantaneous frequency and phase information but are too time consuming for practical use. Our aim was to design and develop a massively parallel and performance-optimized GPU implementation of the Improved Complete Ensemble EMD with the Adaptive Noise (CEEMDAN) algorithm that significantly reduces the computational time (from hours to seconds) of such analysis. The resulting GPU program, which is publicly available, was validated against a MATLAB reference implementation and reached over a 260× speedup for actual EEG measurement data, and provided predicted speedups in the range of 3000–8300× for longer measurements when sufficient memory was available. The significance of our research is that this implementation can enable researchers to perform EMD-based EEG analysis routinely, even for high-density EEG measurements. The program is suitable for execution on desktop, cloud, and supercomputer systems and can be the starting point for future large-scale multi-GPU implementations. LA - English DB - MTMT ER - TY - JOUR AU - Gyulai, Ádám AU - Körmendi, János AU - Mohamed, F. Issa AU - Juhász, Zoltán AU - Nagy, Zoltán TI - Event‐Related Spectral Perturbation, Inter Trial Coherence, and Functional Connectivity in motor execution: A comparative EEG study of old and young subjects JF - BRAIN AND BEHAVIOR J2 - BRAIN BEHAV VL - 13 PY - 2023 IS - 8 PG - 15 SN - 2162-3279 DO - 10.1002/brb3.3176 UR - https://m2.mtmt.hu/api/publication/34072577 ID - 34072577 N1 - Szentagothai Doctoral School, Semmelweis University, Budapest, Hungary Department of Neurology, Uzsoki Hospital, Budapest, Hungary Laboratory of Bioelectric Brain Imaging, National Mental, Neurological and Neurosurgical Institute, Budapest, Hungary Department of Electrical Engineering and Information Systems, University of Pannonia, Veszprem, Hungary Faculty of Education and Psychology, Institute of Health Promotion and Sport Sciences, Eötvös Loránd University, Budapest, Hungary Faculty of Computers and Artificial Intelligence, Department of Scientific Computing, Benha University, Benha, Egypt Department of Vascular Neurology, Semmelweis University, Budapest, Hungary Export Date: 4 October 2023 Correspondence Address: Gyulai, A.; National Mental, Hungary; email: gyulai@uzsoki.hu Correspondence Address: Nagy, Z.; Laboratory of Bioelectric Brain Imaging, Hungary; email: profnagyzoltan@gmail.com Tradenames: Biosemi Active, BioSemi; MATLAB, Mathworks, United States Manufacturers: BioSemi; Mathworks, United States LA - English DB - MTMT ER - TY - JOUR AU - Ihász, Petra AU - András, Benyhe AU - Gyula, Sáry AU - Juhász, Zoltán AU - Navracsics, Judit TI - Visual Word Recognition Patterns of Hungarian-English Bilinguals – Homograph Effect in Bilingual Language Decision JF - ALKALMAZOTT NYELVTUDOMÁNY J2 - ALKALMAZOTT NYELVTUDOMÁNY VL - 23 PY - 2023 IS - 1 SP - 36 EP - 58 PG - 23 SN - 1587-1061 DO - 10.18460/ANY.2023.1.003 UR - https://m2.mtmt.hu/api/publication/34069273 ID - 34069273 AB - The present study is part of a larger-scale research in which the processes of written word recognition are studied in bilinguals. The research goal of our lexical decision experiments is to gain information about the temporal characteristics of recognition at the orthographic, phonological, and semantic levels of processing. The research questions concern behavioral differences and the ERP components of recognizing English words, Hungarian words, and interlexical homographs. 23 Hungarian-English bilingual participants were tested in an Electroencephalogram laboratory. In recognition of Hungarian and English words and homographs, the mean response language per participant indicated high accuracy for both Hungarian and English conditions (96% and 98%, respectively). In contrast, the homographs are biased towards English responses (27% Hungarian response). The multiple comparisons confirmed no difference in the mean response times of Hungarian and English words, whereas the interlexical homographs produced around 150 ms longer responses. In recognition of Hungarian and English words, there was no difference between the two categories in the early recognition phases, corresponding with the orthographic-phonological level. However, the neural representation of the two languages differed, later reflecting the differences in semantic or decision-related processes. In the case of the Hungarian- English interlexical homographs, the ERP waveforms did not show significant differences between the items perceived as English or Hungarian. Although there is a difference between the brain activations in the temporal and frontal electrode sites, this difference is insignificant. These data coincide with the former findings related to the homograph effect (Navracsics & Sáry, 2013), which explains that participants are exposed to a greater cognitive burden in the recognition, and the reaction time is longer due to the fact that both lexicons are active. LA - English DB - MTMT ER -