Advanced Signal Processing and Machine/Deep Learning Approaches on a Preprocessing Block for EEG Artifact Removal: A Comprehensive Review DOI
Said Agounad,

Ousama Tarahi,

Mustapha Moufassih

и другие.

Circuits Systems and Signal Processing, Год журнала: 2024, Номер unknown

Опубликована: Дек. 19, 2024

Язык: Английский

Detection, identification and removing of artifacts from sEMG signals: Current studies and future challenges DOI

Mohamed Ait Yous,

Said Agounad,

Siham Elbaz

и другие.

Computers in Biology and Medicine, Год журнала: 2025, Номер 186, С. 109651 - 109651

Опубликована: Янв. 10, 2025

Язык: Английский

Процитировано

0
A Safe and Efficient Brain–Computer Interface Using Moving Object Trajectories and LED-Controlled Activation DOI Creative Commons
Sefa Aydin, Mesut Melek, Levent GÖKREM

и другие.

Micromachines, Год журнала: 2025, Номер 16(3), С. 340 - 340

Опубликована: Март 16, 2025

Nowadays, brain–computer interface (BCI) systems are frequently used to connect individuals who have lost their mobility with the outside world. These BCI enable control external devices using brain signals. However, these certain disadvantages for users. This paper proposes a novel approach minimize of visual stimuli on eye health system users in employing evoked potential (VEP) and P300 methods. The employs moving objects different trajectories instead stimuli. It uses light-emitting diode (LED) frequency 7 Hz as condition be active. LED is assigned prevent it from being triggered by any involuntary or independent movements user. Thus, user will able use safe single stimulus that blinks side without needing focus through balls. Data were recorded two phases: when was off. data processed Butterworth filter power spectral density (PSD) method. In first classification phase, which performed detect background, highest accuracy rate 99.57% achieved random forest (RF) algorithm. second involves classifying within proposed approach, 97.89% an information transfer (ITR) value 36.75 (bits/min) RF classifier.

Язык: Английский

Процитировано

0
A Comparative Review of Detection Methods in SSVEP-based Brain-Computer Interfaces DOI Creative Commons
Amin Besharat, Nasser Samadzadehaghdam, Reyhaneh Afghan

и другие.

IEEE Access, Год журнала: 2024, Номер 12, С. 181232 - 181270

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

1
A Fuzzy Inference System and Stationary Wavelet Decomposition for Identification and Removal of ECG Artifact from sEMG Signals DOI

Ait Yous Mohamed,

Said Agounad,

E. S. Siham

и другие.

Опубликована: Май 16, 2024

Язык: Английский

Процитировано

0
Enhancing detection of SSVEP-based BCIs via a novel temporally local canonical correlation analysis DOI

Guangshu Xia,

Li Wang,

Shiming Xiong

и другие.

Journal of Neuroscience Methods, Год журнала: 2024, Номер 414, С. 110325 - 110325

Опубликована: Ноя. 20, 2024

Язык: Английский

Процитировано

0
Advanced Signal Processing and Machine/Deep Learning Approaches on a Preprocessing Block for EEG Artifact Removal: A Comprehensive Review DOI
Said Agounad,

Ousama Tarahi,

Mustapha Moufassih

и другие.

Circuits Systems and Signal Processing, Год журнала: 2024, Номер unknown

Опубликована: Дек. 19, 2024

Язык: Английский

Процитировано

0