Zobrazeno 1 - 10
of 922
pro vyhledávání: '"EEG Classification"'
Autor:
Samaneh Alsadat Saeedinia, Mohammad Reza Jahed-Motlagh, Abbas Tafakhori, Nikola Kirilov Kasabov
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-21 (2024)
Abstract The study introduces a new online spike encoding algorithm for spiking neural networks (SNN) and suggests new methods for learning and identifying diagnostic biomarkers using three prominent deep learning neural network models: deep BiLSTM,
Externí odkaz:
https://doaj.org/article/f4d3247752664c24b58a5c644636ee03
Autor:
Yassine El Ouahidi, Vincent Gripon, Bastien Pasdeloup, Ghaith Bouallegue, Nicolas Farrugia, Giulia Lioi
Publikováno v:
IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 32, Pp 3338-3347 (2024)
We propose EEG-SimpleConv, a straightforward 1D convolutional neural network for Motor Imagery decoding in BCI. Our main motivation is to propose a simple and performing baseline that achieves high classification accuracy, using only standard ingredi
Externí odkaz:
https://doaj.org/article/df4390a5e4cf4d71ac6c9cc20833a516
Autor:
Hossein Ahmadi, Luca Mesin
Publikováno v:
IEEE Access, Vol 12, Pp 103626-103646 (2024)
Electroencephalography (EEG) based Brain-Computer Interfaces (BCIs) are vital for various applications, yet achieving accurate EEG signal classification, particularly for Motor Imagery (MI) tasks, remains a significant challenge. This study introduce
Externí odkaz:
https://doaj.org/article/b0d452233918432d95b18174ccdbe0b2
Publikováno v:
IEEE Access, Vol 12, Pp 79754-79764 (2024)
In the classification of motor imagery Electroencephalogram (MI-EEG) signals through deep learning models, challenges such as the insufficiency of feature extraction due to the limited receptive field of single-scale convolutions, and overfitting due
Externí odkaz:
https://doaj.org/article/82926ceb99de4f79ace856c7be776fb1
Publikováno v:
IEEE Access, Vol 12, Pp 74930-74943 (2024)
Brain-Computer Interface (BCI) is a revolutionary technique that employs wearable electroencephalography (EEG) sensors and artificial intelligence (AI) to monitor and decode brain activity. EEG-based motor imagery (MI) brain signal is widely utilized
Externí odkaz:
https://doaj.org/article/82d2c582fa644eb58d7076d1e5ab8e34
Autor:
Chang Wang, Yang Wu, Chen Wang, Yaning Ren, Jiefen Shen, Ting Pang, Chee Seng Chan, Wenjie Ren, Yi Yu
Publikováno v:
IEEE Access, Vol 12, Pp 8325-8336 (2024)
Motor imagery electroencephalogram (MI-EEG) classification is essential in brain-computer interface (BCI), and many classification methods have been proposed recently. However, the MI-EEG classification accuracy of the public dataset still has room f
Externí odkaz:
https://doaj.org/article/fd87d655bea84eb2920ccbc5ee868fed
Autor:
Sufan Ma, Dongxiao Zhang
Publikováno v:
Sensors, Vol 24, Iss 21, p 7080 (2024)
Background: Domain adaptation (DA) techniques have emerged as a pivotal strategy in addressing the challenges of cross-subject classification. However, traditional DA methods are inherently limited by the assumption of a homogeneous space, requiring
Externí odkaz:
https://doaj.org/article/2652670201a44bac9d1d020572515cb2
Publikováno v:
Applied Sciences, Vol 14, Iss 19, p 8911 (2024)
Situational awareness detection and characterization of mental states have a vital role in medicine and many other fields. An electroencephalogram (EEG) is one of the most effective tools for identifying and analyzing cognitive stress. Yet, the measu
Externí odkaz:
https://doaj.org/article/25fdbe47b71b4b94ad3d86a05e5b9bb0
Publikováno v:
Sensors, Vol 24, Iss 18, p 6125 (2024)
Brain–computer interfaces (BCIs) are promising tools for motor neurorehabilitation. Achieving a balance between classification accuracy and system responsiveness is crucial for real-time applications. This study aimed to assess how the duration of
Externí odkaz:
https://doaj.org/article/2353ce17e277489c9c583dfe9ba5d752
Publikováno v:
Frontiers in Human Neuroscience, Vol 18 (2024)
Attention deficit/hyperactivity disorder (ADHD) is a neuropsychological disorder that occurs in children and is characterized by inattention, impulsivity, and hyperactivity. Early and accurate diagnosis of ADHD is very important for effective interve
Externí odkaz:
https://doaj.org/article/7d37b829670a404d81c03a1d2e7faef1