17 papers · ranked by Valyu relevance
Jing Mu, Shuo Liu, Anthony N. Burkitt, David B. Grayden
The Steady-State Visual Evoked Potential (SSVEP) is a widely used modality in Brain-Computer Interfaces (BCIs). Existing research has demonstrated the capabilities of SSVEP that use single frequencies for each target in various applications with relatively small numbers of commands required in the BCI. Multi-frequency…
Simone Romeni, Laura Toni, Fiorenzo Artoni, Silvestro Micera
Electrical stimulation of the visual nervous system could improve the quality of life of patients affected by acquired blindness by restoring some visual sensations, but requires careful optimization of stimulation parameters to produce useful perceptions. Neural correlates of elicited perceptions could be used for…
Maham Saeidi, Waldemar Karwowski, Farzad V. Farahani, Krzysztof Fiok + 4 more
'Redha Taiar' 'P. A. Hancock' 'Awad Al-Juaid' 'Natsue Yoshimura'] Electroencephalography (EEG) is a non-invasive technique used to record the brain’s evoked and induced electrical activity from the scalp. Artificial intelligence, particularly machine learning (ML) and deep learning (DL) algorithms, are increasingly…
Yijia Wu, Yanjing Mao, Kaiqiang Feng, Donglai Wei + 2 more
'Jude Hemanth'] RGB color is a basic visual feature. Here we use machine learning and visual evoked potential (VEP) of electroencephalogram (EEG) data to investigate the decoding features of the time courses and space location that extract it, and whether they depend on a common brain cortex channel. We show that RGB…
Shuai Zhang, Lei Sun, Xiuqing Mao, Cuiyun Hu + 1 more
With the rapid development of brain-computer interface technology, as a new biometric feature, EEG signal has been widely concerned in recent years. The safety of brain-computer interface and the long-term insecurity of biometric authentication have a new solution. This review analyzes the biometrics of EEG signals…
RuiFang Lyu
Electroencephalography (EEG) is a longstanding means of non-invasively recording brain signals and has become highly valuable for the study of neurological and cognitive processes. Recent progress in deep learning has also greatly improved both EEG signal analysis and interpretation, making more accurate, reliable and…
Christos Orovas, Theodosios Sapounidis, Christina Volioti, Euclid Keramopoulos + 1 more
'Euclid Keramopoulos' 'Francesco Carlo Morabito'] Education is an activity that involves great cognitive load for learning, understanding, concentrating, and other high-level cognitive tasks. The use of the electroencephalogram (EEG) and other brain imaging techniques in education has opened the scientific field of…
Rabindra Gandhi Thangarajoo, Mamun Bin Ibne Reaz, Geetika Srivastava, Fahmida Haque + 4 more
'Fahmida Haque' 'Sawal Hamid Md Ali' 'Ahmad Ashrif A. Bakar' 'Mohammad Arif Sobhan Bhuiyan' 'Yvonne Tran'] Epileptic seizures are temporary episodes of convulsions, where approximately 70 percent of the diagnosed population can successfully manage their condition with proper medication and lead a normal life. Over 50…
Ahmad Chaddad, Yihang Wu, Reem Kateb, Ahmed Bouridane + 2 more
The electroencephalography (EEG) signal is a noninvasive and complex signal that has numerous applications in biomedical fields, including sleep and the brain-computer interface. Given its complexity, researchers have proposed several advanced preprocessing and feature extraction methods to analyze EEG signals. In this…
Anupreet Kaur Singh, Sridhar Krishnan
This paper will focus on electroencephalogram (EEG) signal analysis with an emphasis on common feature extraction techniques mentioned in the research literature, as well as a variety of applications that this can be applied to. In this review, we cover single and multi-dimensional EEG signal processing and feature…
Giulia Cisotto, Davide Chicco, Vincent Chen
Electroencephalography (EEG) is a medical engineering technique aimed at recording the electric activity of the human brain. Brain signals derived from an EEG device can be processed and analyzed through computers by using digital signal processing, computational statistics, and machine learning techniques, that can…
Bewar Neamat Taha, Muhammet Baykara, Talha Burak Alakuş, Ateke Goshvarpour
'Ateke Goshvarpour'] Background and Objectives: Lie detection is crucial in domains such as security, law enforcement, and clinical assessments. Traditional methods suffer from reliability issues and susceptibility to countermeasures. In recent years, electroencephalography (EEG) and particularly the Event-Related…
K. Sheela sobana Rani, S Pravinth Raja, M. Sinthuja, B Vidhya Banu + 2 more
EEG, or Electroencephalogram, is an instrument that examines the brain's functions while it is executing any activity. EEG signals to aid in the identification of brain processes and movements and are thus useful in the detection of neurobiological illnesses. Pulses have a very weak magnitude and are recorded from peak…
Jin Xu, Erqiang Zhou, Zhen Qin, Ting Bi + 4 more
'Tudor Balinisteanu' 'Morteza Izadifar'] An EEG signal (Electroencephalogram) is a bioelectric phenomenon reflecting human brain activities. In this paper, we propose a novel deep learning framework ESML (EEG-based Subject Matching Learning) using raw EEG signals to learn latent representations for EEG-based user…
Joe Saad, Adrian Evans, Ilan Jaoui, Victor Roux-Sibillon + 2 more
'Emmanuel Hardy' 'Lorena Anghel'] Brain signal decoders are increasingly being used in early clinical trials for rehabilitation and assistive applications such as motor control and speech decoding. As many Brain-Computer Interfaces (BCIs) need to be deployed in battery-powered or implantable devices, signal decoding…
Sefa Aydin, Mesut Melek, Levent Gökrem, Cheng-Hsin Chuang
Nowadays, brain-computer interface (BCI) systems are frequently used to connect individuals who have lost their mobility with the outside world. These BCI systems enable individuals to control external devices using brain signals. However, these systems have certain disadvantages for users. This paper proposes a novel…
Marcos Fabietti, Mufti Mahmud, Ahmad Lotfi, M. Shamim Kaiser
Brain signals are recorded using different techniques to aid an accurate understanding of brain function and to treat its disorders. Untargeted internal and external sources contaminate the acquired signals during the recording process. Often termed as artefacts, these contaminations cause serious hindrances in…