15 papers · ranked by Valyu relevance
Marc M. Himmelberg, Federico G. Segala, Ryan T. Maloney, Julie M. Harris + 1 more
Two stereoscopic cues that underlie the perception of motion-in-depth (MID) are changes in retinal disparity over time (CD) and interocular velocity differences (IOVD). These cues have independent spatiotemporal sensitivity profiles, depend upon different low-level stimulus properties, and are potentially processed…
Greta Tuckute, Sofie Therese Hansen, Nicolai Pedersen, Dea Steenstrup + 1 more
There is significant current interest in decoding mental states from electro-encephalography (EEG) recordings. We demon-strate inter-subject single-trial decoding of naturalistic stimuli based on scalp EEG acquired with user-friendly, portable, 32 dry electrode equipment. We show that Support Vector Machine (SVM)…
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…
Jasper E. Hajonides, Anna C. Nobre, Freek van Ede, Mark G. Stokes
Recent advances have made it possible to decode various aspects of visually presented stimuli from patterns of scalp EEG measurements. As of recently, such multivariate methods have been commonly used to decode visual-spatial features such as location, orientation, or spatial frequency. In the current study, we show…
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…
Hamid Karimi-Rouzbahani, Mozhgan Shahmohammadi, Ehsan Vahab, Saeed Setayeshi + 1 more
How does the human brain encode visual object categories? Our understanding of this has advanced substantially with the development of multivariate decoding analyses. However, conventional electroencephalography (EEG) decoding predominantly use the “mean” neural activation within the analysis window to extract category…
Ben McCartney, Jesus Martinez-del-Rincon, Barry Devereux, Brian Murphy
Brain decoding — the process of inferring a person’s momentary cognitive state from their brain activity — has enormous potential in the field of human-computer interaction. In this study we propose a zero-shot EEG-to-image brain decoding approach which makes use of state-of-the-art EEG preprocessing and feature…
Benjamin Fischer, Andreas Schander, Andreas K. Kreiter, Walter Lang + 1 more
Recordings of epidural field potentials (EFPs) allow to acquire neuronal activity over a large region of cortical tissue with minimal invasiveness. Because electrodes are placed on top of the dura and do not enter the neuronal tissue, EFPs offer intriguing options for both clinical and basic science research. On the…
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…
Laurens R. Krol, Juliane Pawlitzki, Fabien Lotte, Klaus Gramann + 1 more
Electroencephalography (EEG) is a popular method to monitor brain activity, but it can be difficult to evaluate EEG-based analysis methods because no ground-truth brain activity is available for comparison. Therefore, in order to test and evaluate such methods, researchers often use simulated EEG data instead of actual…
William Giroldini, Luciano Pederzoli, Marco Bilucaglia, Simone Melloni + 1 more
Event-Related Potentials (ERPs) are widely used in Brain-Computer Interface applications and in neuroscience. Normal EEG activity is rich in background noise and therefore, in order to detect ERPs, it is usually necessary to take the average from multiple trials to reduce the effects of this noise. The noise produced…
Amanda K Robinson, Praveen Venkatesh, Matthew J. Boring, Michael J. Tarr + 2 more
Standard human EEG systems based on spatial Nyquist estimates suggest that 20-30 mm electrode spacing suffices to capture neural signals on the scalp, but recent studies posit that increasing sensor density can provide higher resolution neural information. Here, we compared “super-Nyquist” density EEG (“SND”) with…
A. F. Rocha
EEG is the oldest tool for studying human cognitive function, but it is blamed to be useless because its poor spatial resolution despite it excellent temporal discrimination. Such comments arise from a reductionist point of view about the cerebral function. However, if the brain is assumed to be a distributed…
Gonzalo M. Rojas, Carolina Alvarez, Carlos Montoya, María de la Iglesia-Vayá + 2 more
Electroencephalography (EEG) is the standard diagnosis method for a wide variety of diseases such as epilepsy, sleep disorders, encephalopathies, and coma, among others. Resting-state functional magnetic resonance (rs-fMRI) is currently a technique used in research in both healthy individuals as well as patients. EEG…