Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Ali Momennezhad"'
Autor:
Kenta Abe, Yuki Kambe, Kei Majima, Zijing Hu, Makoto Ohtake, Ali Momennezhad, Hideki Izumi, Takuma Tanaka, Ashley Matunis, Emma Stacy, Takahide Itokazu, Takashi R Sato, Tatsuo Sato
Publikováno v:
eLife, Vol 12 (2024)
Midbrain dopamine neurons impact neural processing in the prefrontal cortex (PFC) through mesocortical projections. However, the signals conveyed by dopamine projections to the PFC remain unclear, particularly at the single-axon level. Here, we inves
Externí odkaz:
https://doaj.org/article/04f9146051cb4d5c82f9e389fefb2b87
Publikováno v:
Applied Medical Informatics, Vol 34, Iss 2, Pp 23-35 (2014)
In this paper, a linear predictive coding (LPC) model is used to improve classification accuracy, convergent speed to maximum accuracy, and maximum bitrates in brain computer interface (BCI) system based on extracting EEG-P300 signals. First, EEG sig
Externí odkaz:
https://doaj.org/article/23064e67ef604f0d9b1f47744a0c4eb0
Autor:
Ali Momennezhad
Publikováno v:
Biomedical Engineering / Biomedizinische Technik. 65:393-404
In this paper, we suggest an efficient, accurate and user-friendly brain-computer interface (BCI) system for recognizing and distinguishing different emotion states. For this, we used a multimodal dataset entitled “MAHOB-HCI” which can be freely
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::356e99003affd65514332651da86b411
https://doi.org/10.1515/bmt-2019-0327
https://doi.org/10.1515/bmt-2019-0327
Autor:
Ali Momennezhad
Publikováno v:
Multimedia Tools and Applications. 77:27089-27106
This paper focuses on EEG (Electroencephalography) signals as a robust method for emotion recognition. In emotion recognition, researchers usually use features such as eye pupil diameter, facial features, EEG signals and physiological signals like: r
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ad1770bb676aef739f3a8cb04bd958fd
https://doi.org/10.1007/s11042-018-5906-8
https://doi.org/10.1007/s11042-018-5906-8
Publikováno v:
International Journal of Computing. :97-106
In this paper, Fisher linear discriminant analysis (FLDA) is used to classify the EEGP-300 signals which are extracted from brain activities. In this case, at first the preprocessing algorithms such as filtering and referencing are applied to the raw
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9b6ef7696378371f6ba14e9b4e1e5e4a
https://doi.org/10.47839/ijc.14.2.806
https://doi.org/10.47839/ijc.14.2.806
Publikováno v:
International Journal of Intelligent Computing in Medical Sciences & Image Processing. 6:17-26
In this paper, a new approach is proposed to categorize the EEG-P300 signals of brain activity using fisher linear discriminant analysis (FLDA) based on smoothed signal achieved from linear predictive coding (LPC) filter. In order to overcome the ove
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bd5249e7e6b6071ec21be34ae23f844c
https://doi.org/10.1080/1931308x.2014.925271
https://doi.org/10.1080/1931308x.2014.925271