16 papers · ranked by Valyu relevance
Raheel Zafar, Sarat C. Dass, Aamir Saeed Malik, Bin He
Electroencephalogram (EEG)-based decoding human brain activity is challenging, owing to the low spatial resolution of EEG. However, EEG is an important technique, especially for brain-computer interface applications. In this study, a novel algorithm is proposed to decode brain activity associated with different types…
Km Bhavna, Azman Akhter, Romi Banerjee, Dipanjan Roy
Decoding of cognitive states aims to identify individuals' brain states and brain fingerprints to predict behavior. Deep learning provides an important platform for analyzing brain signals at different developmental stages to understand brain dynamics. Due to their internal architecture and feature extraction…
Vito De Feo, Fabio Boi, Houman Safaai, Arno Onken + 2 more
'Alessandro Vato'] Brain-machine interfaces (BMIs) promise to improve the quality of life of patients suffering from sensory and motor disabilities by creating a direct communication channel between the brain and the external world. Yet, their performance is currently limited by the relatively small amount of…
Armin W. Thomas, Hauke R. Heekeren, Klaus-Robert Müller, Wojciech Samek
'Wojciech Samek'] The application of deep learning (DL) models to neuroimaging data poses several challenges, due to the high dimensionality, low sample size, and complex temporo-spatial dependency structure of these data. Even further, DL models often act as black boxes, impeding insight into the association of…
Stefano Panzeri, Houman Safaai, Vito De Feo, Alessandro Vato
Brain-machine interfaces (BMIs) can improve the quality of life of patients with sensory and motor disabilities by both decoding motor intentions expressed by neural activity, and by encoding artificially sensed information into patterns of neural activity elicited by causal interventions on the neural tissue. Yet…
Célia Loriette, Julian L. Amengual, Suliann Ben Hamed
One of the major challenges in system neurosciences consists in developing techniques for estimating the cognitive information content in brain activity. This has an enormous potential in different domains spanning from clinical applications, cognitive enhancement to a better understanding of the neural bases of…
Bing Du, Xiaomu Cheng, Yiping Duan, Huansheng Ning + 5 more
'Andrea Luigi Guerra' 'Gabriele Baronio' 'Domenico Speranza' 'Luca Ulrich'] Brain neural activity decoding is an important branch of neuroscience research and a key technology for the brain-computer interface (BCI). Researchers initially developed simple linear models and machine learning algorithms to classify and…
Lanfang Liu, Jiahao Jiang, Hehui Li, Guosheng Ding + 2 more
'Barbara G Shinn-Cunningham'] Speech comprehension involves the dynamic interplay of multiple cognitive processes, from basic sound perception, to linguistic encoding, and finally to complex semantic-conceptual interpretations. How the brain handles the diverse streams of information processing remains poorly…
J Brendan Ritchie, David Michael Kaplan, Colin Klein
Since its introduction, multivariate pattern analysis (MVPA), or ‘neural decoding’, has transformed the field of cognitive neuroscience. Underlying its influence is a crucial inference, which we call the decoder’s dictum: if information can be decoded from patterns of neural activity, then this provides strong evidence…
Yifan Yang, David A. Leopold, Jeff H. Duyn, Grayson O. Sipe + 1 more
'Xiao Liu'] Title: Abstract The brain's response to external events depends on its internal arousal states, which are dynamically governed by neuromodulatory systems and have recently been linked to coordinated spike timing cascades in widespread brain networks. At rest, both arousal fluctuations and spiking cascades…
Shailaja Akella, Peter Ledochowitsch, Joshua H. Siegle, Hannah Belski + 6 more
'Hannah Belski' 'Daniel D. Denman' 'Michael A. Buice' 'Severine Durand' 'Christof Koch' 'Shawn R. Olsen' 'Xiaoxuan Jia'] Influenced by non-stationary factors such as brain states and behavior, neurons exhibit substantial response variability even to identical stimuli. However, it remains unclear how their relative…
Anira Escrichs, Yonatan Sanz Perl, Carme Uribe, Estela Camara + 14 more
'Basak Türker' 'Nadya Pyatigorskaya' 'Ane López-González' 'Carla Pallavicini' 'Rajanikant Panda' 'Jitka Annen' 'Olivia Gosseries' 'Steven Laureys' 'Lionel Naccache' 'Jacobo D. Sitt' 'Helmut Laufs' 'Enzo Tagliazucchi' 'Morten L. Kringelbach' 'Gustavo Deco'] Significant advances have been made by identifying the levels…
Giulio Mecacci, Pim Haselager
Contemporary brain reading technologies promise to provide the possibility to decode and interpret mental states and processes. Brain reading could have numerous societally relevant implications. In particular, the private character of mind might be affected, generating ethical and legal concerns. This paper aims at…
Tianjiao Zhang, James S. Gao, Tolga Çukur, Jack L. Gallant
Complex natural tasks likely recruit many different functional brain networks, but it is difficult to predict how such tasks will be represented across cortical areas and networks. Previous electrophysiology studies suggest that task variables are represented in a low-dimensional subspace within the activity space of…
Jiyoung Kang, Chongwon Pae, Hae-Jeong Park, Satoru Hayasaka
The resting-state brain is often considered a nonlinear dynamic system transitioning among multiple coexisting stable states. Despite the increasing number of studies on the multistability of the brain system, the processes of state transitions have rarely been systematically explored. Thus, we investigated the state…
James Barnard Wilsenach, Charlotte M. Deane, Gesine Reinert, Katie Warnaby
Anesthetisia is an important surgical and explorative tool in the study of consciousness. Much work has been done to connect the deeply anesthetized condition with decreased complexity. However, anesthesia-induced unconsciousness is also a dynamic condition in which functional activity and complexity may fluctuate…