Results 1 to 10 of about 153,200 (241)

Decoding Steady-State Visual Evoked Potentials From Electrocorticography [PDF]

Frontiers in Neuroinformatics, 2018
We report on a unique electrocorticography (ECoG) experiment in which Steady-State Visual Evoked Potentials (SSVEPs) to frequency- and phase-tagged stimuli were recorded from a large subdural grid covering the entire right occipital cortex of a human ...
Benjamin Wittevrongel, Elvira Khachatryan, Mansoureh Fahimi Hnazaee, Flavio Camarrone, Evelien Carrette, Leen De Taeye, Alfred Meurs, Paul Boon, Dirk Van Roost, Marc M. Van Hulle   +9 more
doaj   +5 more sources

High‐accuracy machine learning techniques for functional connectome fingerprinting and cognitive state decoding

Human Brain Mapping, 2023
Abstract The human brain is a complex network comprised of functionally and anatomically interconnected brain regions. A growing number of studies have suggested that empirical estimates of brain networks may be useful for discovery of biomarkers of disease and cognitive state.
Andrew Hannum, Mario A. Lopez, Saúl A. Blanco, Richard F. Betzel   +3 more
openaire   +5 more sources

Stimulus Design for Visual Evoked Potential Based Brain-Computer Interfaces

IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2023
Visual stimuli design plays an important role in brain-computer interfaces (BCIs) based on visual evoked potentials (VEPs). Variations in stimulus parameters have been shown to affect both decoding accuracy and subjective perception experience, implying ...
Haoyin Xu, Sheng-Hsiou Hsu, Masaki Nakanishi, Yufan Lin, Tzyy-Ping Jung, Gert Cauwenberghs   +5 more
doaj   +1 more source

Brain decoding of the Human Connectome Project tasks in a dense individual fMRI dataset

NeuroImage, 2023
Brain decoding aims to infer cognitive states from patterns of brain activity. Substantial inter-individual variations in functional brain organization challenge accurate decoding performed at the group level.
Shima Rastegarnia, Marie St-Laurent, Elizabeth DuPre, Basile Pinsard, Pierre Bellec   +4 more
doaj   +1 more source

Deep Convolutional Neural Network for EEG-Based Motor Decoding

Micromachines, 2022
Brain–machine interfaces (BMIs) have been applied as a pattern recognition system for neuromodulation and neurorehabilitation. Decoding brain signals (e.g., EEG) with high accuracy is a prerequisite to building a reliable and practical BMI.
Jing Zhang, Dong Liu, Weihai Chen, Zhongcai Pei, Jianhua Wang   +4 more
doaj   +1 more source

Decoding Multi-Class Motor Imagery and Motor Execution Tasks Using Riemannian Geometry Algorithms on Large EEG Datasets

Sensors, 2023
The use of Riemannian geometry decoding algorithms in classifying electroencephalography-based motor-imagery brain–computer interfaces (BCIs) trials is relatively new and promises to outperform the current state-of-the-art methods by overcoming the noise
Zaid Shuqfa, Abdelkader Nasreddine Belkacem, Abderrahmane Lakas   +2 more
doaj   +1 more source

Decoding Visual fMRI Stimuli from Human Brain Based on Graph Convolutional Neural Network

Brain Sciences, 2022
Brain decoding is to predict the external stimulus information from the collected brain response activities, and visual information is one of the most important sources of external stimulus information.
Lu Meng, Kang Ge
doaj   +1 more source

Smart Tactile Sensing Systems Based on Embedded CNN Implementations

Micromachines, 2020
Embedding machine learning methods into the data decoding units may enable the extraction of complex information making the tactile sensing systems intelligent.
Mohamad Alameh, Yahya Abbass, Ali Ibrahim, Maurizio Valle   +3 more
doaj   +1 more source

Visual units and confusion modelling for automatic lip-reading [PDF]

, 2016
Automatic lip-reading (ALR) is a challenging task because the visual speech signal is known to be missing some important information, such as voicing. We propose an approach to ALR that acknowledges that this information is missing but assumes that it is
Cox, Stephen, Howell, Dominic, Theobald, Barry   +2 more
core   +1 more source

Video Classification With CNNs: Using The Codec As A Spatio-Temporal Activity Sensor [PDF]

, 2017
We investigate video classification via a two-stream convolutional neural network (CNN) design that directly ingests information extracted from compressed video bitstreams.
Abbas, Alhabib, Andreopoulos, Yiannis, Chadha, Aaron   +2 more
core   +2 more sources