19 papers · ranked by Valyu relevance
Reuben Rideaux, Ziyue Hu, Kali Chidley, Martijn A Cloos + 2 more
The natural environment is spatiotemporally structured, and the brain exploits this regularity to predict and prepare for upcoming sensory stimuli. Such predictive processing is thought to increase neural efficiency by reducing metabolic expenditure and altering the fidelity with which newly encountered stimuli are…
Ling Huang, Manuel Varlet, Tijl Grootswagers
High-density EEG recording enhances spatial resolution for neural signal decoding, yet the relationship between electrode density and decoding performance, as well as the minimum number of electrodes required for effective decoding, remains unclear. To address this, we systematically investigated the decoding accuracy…
Hamid Karimi-Rouzbahani
Distinct neural processes are often encoded across distinct time scales of neural activations. However, it has remained unclear if this multiscale coding strategy is also implemented for separate features of the same process. One difficulty is that the conventional methods of time scale analysis provide imperfect…
Alejandro Santos-Mayo, Faith Gilbert, Laura Ahumada, Caitlin Traiser + 4 more
Neuroscience research has increasingly used decoding techniques, in which multivariate statistical methods identify patterns in neural data that allow the classification of experimental conditions or participant groups. Typically, the features used for decoding are spatial in nature, including voxel patterns and…
Matteo Ferrante, Tommaso Boccato, Stefano Bargione, Nicola Toschi
Decoding visual representations from human brain activity has emerged as a thriving research domain, particularly in the context of brain-computer interfaces. Our study presents an innovative method that employs to classify and reconstruct images from the ImageNet dataset using electroencephalography (EEG) data from…
Nitin Sadras, Omid G. Sani, Parima Ahmadipour, Maryam M. Shanechi
When making decisions, humans can evaluate how likely they are to be correct. If this subjective confidence could be reliably decoded from brain activity, it would be possible to build a brain-computer interface (BCI) that improves decision performance by automatically providing more information to the user if needed…
Venkatakrishnan Sutharsan, Alagappan Swaminathan, Saisrinivasan Ramachandran, M. K. Lakshmanan + 1 more
'Saisrinivasan Ramachandran' 'M. K. Lakshmanan' 'M. Balaji'] Abstract. Electroencephalogram (EEG) is the recording which is the result due to the activity of bio-electrical signals that is acquired from electrodes placed on the scalp. In Electroencephalogram signal(EEG) recordings, the signals obtained are contaminated…
Roman Dolgopolyi, Antonis Chatzipanagiotou
An advanced emotion classification model was developed using a CNN-Transformer architecture for emotion recognition from EEG brain wave signals, effectively distinguishing among three emotional states, positive, neutral, and negative. The model achieved a testing accuracy of 91%, outperforming traditional models such…
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…
Naseem Babu, Jimson Mathew, A. P. Vinod
—The growing convergence between Large Language Models (LLMs) and electroencephalography (EEG) research is enabling new directions in neural decoding, brain-computer interfaces (BCIs), and affective computing. This survey offers a systematic review and structured taxonomy of recent advancements that utilize LLMs for…
Nikunj Phutela, P Abhilash, Kaushik Sreevathsan, B N Krupa
—Neuromarketing is an emerging field that combines neuroscience and marketing to understand the factors that influence consumer decisions better. The study proposes a method to understand consumers' positive and negative reactions to advertisements (ads) and products by analysing electroencephalogram (EEG) signals.…
Andaç Demir, Iya Khalil, Bülent Kızıltan
One of the main challenges in electroencephalogram (EEG) based brain-computer interface (BCI) systems is learning the subject/session invariant features to classify cognitive activities within an end-to-end discriminative setting. We propose a novel end-to-end machine learning pipeline, EEG-NeXt, which facilitates…
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…
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…
John Fredy Ochoa-Gómez, Yorguin-José Mantilla-Ramos, Verónica Henao Isaza, Carlos Andrés Tobón + 3 more
Evaluate the reliability of neural components obtained from the appli-cation of the group ICA (gICA) methodology to resting-state EEG datasets acquired from multiple sites. Five databases from three sites, covering a total of 292 healthy subjects, were analyzed. Each dataset was segmented into groups of 15 subjects…
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…
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…