13 papers · ranked by Valyu relevance
Guanghui Zhang, Xinran Wang, Steven J. Luck
Regularization has been extensively used in multivariate pattern classification (MVPA; decoding) of EEG data to mitigate the risk of overfitting. N-fold cross-validation is also used to mitigate this risk, and it is often combined with averaging across trials to improve the signal-to-noise ratio. However, the impact of…
Melina Timplalexi, William M. Connelly, Adam Ranson
Binocular rivalry arises when incongruent images are presented to the two eyes, producing stochastic alternations in perceptual dominance. While rivalry has been extensively studied in species with highly developed binocular vision, it is unclear whether similar representational dynamics occur in the mouse, a model…
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)…
Yun Liang, Ke Bo, Sreenivasan Meyyappan, Mingzhou Ding
Multivoxel pattern analysis (MVPA) examines the differences in fMRI activation patterns associated with different cognitive conditions and provides information not possible with the conventional univariate analysis. Support vector machines (SVMs) are the predominant machine learning method in MVPA. SVMs are intuitive…
Carlos Daniel Carrasco, Brett Bahle, Aaron Matthew Simmons, Steven J. Luck
Multivariate pattern analysis approaches can be applied to the topographic distribution of event-related potential (ERP) signals to ‘decode’ subtly different stimulus classes, such as different faces and different orientations. These approaches are extremely sensitive, and it seems possible that they could also be used…
Matthias Guggenmos, Philipp Sterzer, Radoslaw Martin Cichy
Multivariate pattern analysis (MVPA) methods such as decoding and representational similarity analysis (RSA) are growing rapidly in popularity for the analysis of magnetoencephalography (MEG) data. However, little is known about the relative performance and characteristics of the specific dissimilarity measures used to…
Matthew R Whiteway, Bruno Averbeck, Daniel A Butts
Decoding is a powerful approach for measuring the information contained in the activity of neural populations. As a result, decoding analyses are now used across a wide range of model organisms and experimental paradigms. However, typical analyses employ general purpose decoding algorithms that do not explicitly take…
G. Bilodeau, A. Miao, G. Gagnon-Turcotte, C. Ethier + 1 more
Bidirectional interfaces combined with neural de-coding algorithms are essential for closed-loop (CL) neuromodulation, enabling simultaneous neural monitoring and responsive optogenetic stimulation. However, implementing these capabilities in compact wireless headstages for freely moving animals remains challenging, as…
Ladan Yang, Catherine Mikkelsen
The hippocampus has been associated with spatial information processing (30). However, it is also not clear whether such ensemble coding of spatial information extends to other brain regions in the medial temporal lobe. Hence, this study uses various classification techniques to attempt to decode spatial and valence…
Shoeb Shaikh, Rosa So, Tafadzwa Sibindi, Camilo Libedinsky + 1 more
This paper presents a novel sparse ensemble based machine learning approach to enhance robustness of intracortical Brain Machine Interfaces (iBMIs) in the face of non-stationary distribution of input neural data across time. Each classifier in the ensemble is trained on a randomly sampled (with replacement) set of…
Stefan Bode, Elektra Schubert, Hinze Hogendoorn, Daniel Feuerriegel
Multivariate classification analysis for event-related potential (ERP) data is a powerful tool for predicting cognitive variables. However, classification is often restricted to categorical variables and under-utilises continuous data, such as response times, response force, or subjective ratings. An alternative…
Joram Soch, Carsten Allefeld, John-Dylan Haynes
Techniques of multivariate pattern analysis (MVPA) can be used to decode the discrete experimental condition or a continuous modulator variable from measured brain activity during a particular trial. In functional magnetic resonance imaging (fMRI), trial-wise response amplitudes are sometimes estimated from the…
Ethan M. Meyers
Neural decoding is a powerful method to analyze neural activity. However, the code needed to run a decoding analysis can be complex, which can present a barrier to using the method. In this paper we introduce a package that makes it easy to perform decoding analyses in the R programing language. We describe how the…