TY - JOUR
T1 - Paired Trial Classification
T2 - A Novel Deep Learning Technique for MVPA
AU - Williams, Jacob M.
AU - Samal, Ashok
AU - Rao, Prahalada K.
AU - Johnson, Matthew R.
N1 - Publisher Copyright:
© Copyright © 2020 Williams, Samal, Rao and Johnson.
PY - 2020/4/30
Y1 - 2020/4/30
N2 - Many recent developments in machine learning have come from the field of “deep learning,” or the use of advanced neural network architectures and techniques. While these methods have produced state-of-the-art results and dominated research focus in many fields, such as image classification and natural language processing, they have not gained as much ground over standard multivariate pattern analysis (MVPA) techniques in the classification of electroencephalography (EEG) or other human neuroscience datasets. The high dimensionality and large amounts of noise present in EEG data, coupled with the relatively low number of examples (trials) that can be reasonably obtained from a sample of human subjects, lead to difficulty training deep learning models. Even when a model successfully converges in training, significant overfitting can occur despite the presence of regularization techniques. To help alleviate these problems, we present a new method of “paired trial classification” that involves classifying pairs of EEG recordings as coming from the same class or different classes. This allows us to drastically increase the number of training examples, in a manner akin to but distinct from traditional data augmentation approaches, through the combinatorics of pairing trials. Moreover, paired trial classification still allows us to determine the true class of a novel example (trial) via a “dictionary” approach: compare the novel example to a group of known examples from each class, and determine the final class via summing the same/different decision values within each class. Since individual trials are noisy, this approach can be further improved by comparing a novel individual example with a “dictionary” in which each entry is an average of several examples (trials). Even further improvements can be realized in situations where multiple samples from a single unknown class can be averaged, thus permitting averaged signals to be compared with averaged signals.
AB - Many recent developments in machine learning have come from the field of “deep learning,” or the use of advanced neural network architectures and techniques. While these methods have produced state-of-the-art results and dominated research focus in many fields, such as image classification and natural language processing, they have not gained as much ground over standard multivariate pattern analysis (MVPA) techniques in the classification of electroencephalography (EEG) or other human neuroscience datasets. The high dimensionality and large amounts of noise present in EEG data, coupled with the relatively low number of examples (trials) that can be reasonably obtained from a sample of human subjects, lead to difficulty training deep learning models. Even when a model successfully converges in training, significant overfitting can occur despite the presence of regularization techniques. To help alleviate these problems, we present a new method of “paired trial classification” that involves classifying pairs of EEG recordings as coming from the same class or different classes. This allows us to drastically increase the number of training examples, in a manner akin to but distinct from traditional data augmentation approaches, through the combinatorics of pairing trials. Moreover, paired trial classification still allows us to determine the true class of a novel example (trial) via a “dictionary” approach: compare the novel example to a group of known examples from each class, and determine the final class via summing the same/different decision values within each class. Since individual trials are noisy, this approach can be further improved by comparing a novel individual example with a “dictionary” in which each entry is an average of several examples (trials). Even further improvements can be realized in situations where multiple samples from a single unknown class can be averaged, thus permitting averaged signals to be compared with averaged signals.
KW - EEG
KW - MVPA
KW - cognitive neuroscience
KW - deep learning
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85084570987&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85084570987&partnerID=8YFLogxK
U2 - 10.3389/fnins.2020.00417
DO - 10.3389/fnins.2020.00417
M3 - Article
C2 - 32425753
AN - SCOPUS:85084570987
SN - 1662-4548
VL - 14
JO - Frontiers in Neuroscience
JF - Frontiers in Neuroscience
M1 - 417
ER -