MVPA-Light: a classification and regression toolbox for multi-dimensional data

MVPA-Light is a MATLAB toolbox for multivariate pattern analysis (MVPA). It provides native implementations of a range of classifiers and regression models, using modern optimization algorithms. High-level functions allow for the multivariate analysis of multi-dimensional data, including generalization (e.g., time x time) and searchlight analysis. The toolbox performs cross-validation, hyperparameter tuning, and nested preprocessing. It computes various classification and regression metrics and establishes their statistical significance, is modular and easily extendable. Furthermore, it offers interfaces for LIBSVM and LIBLINEAR as well as an integration into the FieldTrip neuroimaging toolbox. After introducing MVPA-Light, example analyses of MEG and fMRI datasets, and benchmarking results on the classifiers and regression models are presented

Direct calculation of out-of-sample predictions in multi-class kernel FDA

After a two-class kernel Fisher Discriminant Analysis (KFDA) has been trained on the full dataset, matrix inverse updates allow for the direct calculation of out-of-sample predictions for different test sets. Here, this approach is extended to the multi-class case by casting KFDA in an Optimal Scoring framework. In simulations using 10-fold cross-validation and permutation tests the approach is shown to be more than 1000x faster than retraining the classifier in each fold. Direct out-of-sample predictions can be useful on large datasets and in studies with many training-testing iterations

Direct calculation of out-of-sample predictions in multi-class kernel FDA

After a two-class kernel Fisher Discriminant Analysis (KFDA) has been trained on the full dataset, matrix inverse updates allow for the direct calculation of out-of-sample predictions for different test sets. Here, this approach is extended to the multi-class case by casting KFDA in an Optimal Scoring framework. In simulations using 10-fold cross-validation and permutation tests the approach is shown to be more than 1000x faster than retraining the classifier in each fold. Direct out-of-sample predictions can be useful on large datasets and in studies with many training-testing iterations

Cross-validation in high-dimensional spaces: a lifeline for least-squares models and multi-class LDA

Least-squares models such as linear regression and Linear Discriminant Analysis (LDA) are amongst the most popular statistical learning techniques. However, since their computation time increases cubically with the number of features, they are inefficient in high-dimensional neuroimaging datasets. Fortunately, for k-fold cross-validation, an analytical approach has been developed that yields the exact cross-validated predictions in least-squares models without explicitly training the model. Its computation time grows with the number of test samples. Here, this approach is systematically investigated in the context of cross-validation and permutation testing. LDA is used exemplarily but results hold for all other least-squares methods. Furthermore, a non-trivial extension to multi-class LDA is formally derived. The analytical approach is evaluated using complexity calculations, simulations, and permutation testing of an EEG/MEG dataset. Depending on the ratio between features and samples, the analytical approach is up to 10,000x faster than the standard approach (retraining the model on each training set). This allows for a fast cross-validation of least-squares models and multi-class LDA in high-dimensional data, with obvious applications in multi-dimensional datasets, Representational Similarity Analysis, and permutation testing

Online detection of error-related potentials boosts the performance of mental typewriters

<p>Abstract</p> <p>Background</p> <p>Increasing the communication speed of brain-computer interfaces (BCIs) is a major aim of current BCI-research. The idea to automatically detect error-related potentials (ErrPs) in order to veto erroneous decisions of a BCI has been existing for more than one decade, but this approach was so far little investigated in online mode.</p> <p>Methods</p> <p>In our study with eleven participants, an ErrP detection mechanism was implemented in an electroencephalography (EEG) based gaze-independent visual speller.</p> <p>Results</p> <p>Single-trial ErrPs were detected with a mean accuracy of 89.1% (AUC 0.90). The spelling speed was increased on average by 49.0% using ErrP detection. The improvement in spelling speed due to error detection was largest for participants with low spelling accuracy.</p> <p>Conclusion</p> <p>The performance of BCIs can be increased by using an automatic error detection mechanism. The benefit for patients with motor disorders is potentially high since they often have rather low spelling accuracies compared to healthy people.</p

Decoding auditory attention to instruments in polyphonic music using single-trial EEG classification

Objective. Polyphonic music (music consisting of several instruments playing in parallel) is an intuitive way of embedding multiple information streams. The different instruments in a musical piece form concurrent information streams that seamlessly integrate into a coherent and hedonistically appealing entity. Here, we explore polyphonic music as a novel stimulation approach for use in a brain–computer interface. Approach. In a multi-streamed oddball experiment, we had participants shift selective attention to one out of three different instruments in music audio clips. Each instrument formed an oddball stream with its own specific standard stimuli (a repetitive musical pattern) and oddballs (deviating musical pattern). Main results. Contrasting attended versus unattended instruments, ERP analysis shows subject- and instrument-specific responses including P300 and early auditory components. The attended instrument can be classified offline with a mean accuracy of 91% across 11 participants. Significance. This is a proof of concept that attention paid to a particular instrument in polyphonic music can be inferred from ongoing EEG, a finding that is potentially relevant for both brain–computer interface and music research