Decoding dynamic brain patterns from evoked responses: A tutorial on multivariate pattern analysis applied to time series neuroimaging data
T Grootswagers, SG Wardle… - Journal of cognitive …, 2017 - direct.mit.edu
Multivariate pattern analysis (MVPA) or brain decoding methods have become standard
practice in analyzing fMRI data. Although decoding methods have been extensively applied …
practice in analyzing fMRI data. Although decoding methods have been extensively applied …
Frontal midline theta oscillations during working memory maintenance and episodic encoding and retrieval
LT Hsieh, C Ranganath - Neuroimage, 2014 - Elsevier
Neural oscillations in the theta band (4–8 Hz) are prominent in the human
electroencephalogram (EEG), and many recent electrophysiological studies in animals and …
electroencephalogram (EEG), and many recent electrophysiological studies in animals and …
Deep learning human mind for automated visual classification
What if we could effectively read the mind and transfer human visual capabilities to computer
vision methods? In this paper, we aim at addressing this question by developing the first …
vision methods? In this paper, we aim at addressing this question by developing the first …
Decoding attended information in short-term memory: an EEG study
JJ LaRocque, JA Lewis-Peacock… - Journal of cognitive …, 2013 - direct.mit.edu
For decades it has been assumed that sustained, elevated neural activity—the so-called
active trace—is the neural correlate of the short-term retention of information. However, a …
active trace—is the neural correlate of the short-term retention of information. However, a …
The perils and pitfalls of block design for EEG classification experiments
A recent paper [1] claims to classify brain processing evoked in subjects watching ImageNet
stimuli as measured with EEG and to employ a representation derived from this processing …
stimuli as measured with EEG and to employ a representation derived from this processing …
[HTML][HTML] A representational similarity analysis of the dynamics of object processing using single-trial EEG classification
The recognition of object categories is effortlessly accomplished in everyday life, yet its
neural underpinnings remain not fully understood. In this electroencephalography (EEG) …
neural underpinnings remain not fully understood. In this electroencephalography (EEG) …
[HTML][HTML] Multiple neural states of representation in short-term memory? It'sa matter of attention
JJ LaRocque, JA Lewis-Peacock… - Frontiers in human …, 2014 - frontiersin.org
Short-term memory (STM) refers to the capacity-limited retention of information over a brief
period of time, and working memory (WM) refers to the manipulation and use of that …
period of time, and working memory (WM) refers to the manipulation and use of that …
Generative adversarial networks conditioned by brain signals
Recent advancements in generative adversarial networks (GANs), using deep convolutional
models, have supported the development of image generation techniques able to reach …
models, have supported the development of image generation techniques able to reach …
[HTML][HTML] High-pass filtering artifacts in multivariate classification of neural time series data
Abstract Background Traditionally, EEG/MEG data are high-pass filtered and baseline-
corrected to remove slow drifts. Minor deleterious effects of high-pass filtering in traditional …
corrected to remove slow drifts. Minor deleterious effects of high-pass filtering in traditional …
Decoding the time-course of object recognition in the human brain: From visual features to categorical decisions
EW Contini, SG Wardle, TA Carlson - Neuropsychologia, 2017 - Elsevier
Visual object recognition is a complex, dynamic process. Multivariate pattern analysis
methods, such as decoding, have begun to reveal how the brain processes complex visual …
methods, such as decoding, have begun to reveal how the brain processes complex visual …