http://doc.ml.tu-berlin.de/bbci/publications/HauMeiGoeDaeHayBlaBie13.pdf

On the interpretation of weight vectors of linear models in multivariate neuroimaging.

Single-Trial Analysis and Classification of ERP Components - a Tutorial

Blink: The Power of Thinking without Thinking / Strangers to Ourselves: Discovering the Adaptive Unconscious

Introduction to machine learning for brain imaging.

Brain–computer interfacing (BCI) is a steadily growing area of research. While initially BCI research was focused on applications for paralyzed patients, increasingly more alternative applications in healthy human subjects are proposed and investigated. In particular, monitoring of mental states and decoding of covert user states have seen a strong rise of interest. Here, we present some examples of such novel applications which provide evidence for the promising potential of BCI technology for non-medical uses. Furthermore, we discuss distinct methodological improvements required to bring non-medical applications of BCI technology to a diversity of layperson target groups, e.g., ease of use, minimal training, general usability, short control latencies.

/pdf/the-berlin-brain-computer-interface-non-medical-uses-of-bci-tftlavzyea.pdf

The Berlin Brain–Computer Interface: Non-Medical Uses of BCI Technology

This review summarizes linear spatiotemporal signal analysis methods that derive their power from careful consideration of spatial and temporal features of skull surface potentials. BCIs offer tremendous potential for improving the quality of life for those with severe neurological disabilities. At the same time, it is now possible to use noninvasive systems to improve performance for time-demanding tasks. Signal processing and machine learning are playing a fundamental role in enabling applications of BCI and in many respects, advances in signal processing and computation have helped to lead the way to real utility of noninvasive BCI.

Spatiotemporal Linear Decoding of Brain State

The timing of a behavioral response, such as a button press in reaction to a visual stimulus, is highly variable across trials. In this paper we describe a methodology for single-trial analysis of electroencephalography (EEG) which can be used to reduce the error in the estimation of the timing of the behavioral response and thus reduce the error in estimating the onset time of the stimulus. We consider a rapid serial visual presentation (RSVP) paradigm consisting of concatenated video clips and where subjects are instructed to respond when they see a predefined target. We show that a linear discriminator, with inputs distributed across sensors and time and chosen via an information theoretic feature selection criterion, can be used in conjunction with the response to yield a lower error estimate of the onset time of the target stimulus compared to the response time. We compare our results to response time and previous EEG approaches using fixed windows in time, showing that our method has the lowest estimation error. We discuss potential applications, specifically with respect to cortically-coupled computer vision based triage of large image databases.

Using single-trial EEG to estimate the timing of target onset during rapid serial visual presentation.

Most visual stimuli we experience on a day-to-day basis are continuous sequences, with spatial structure highly correlated in time. During rapid serial visual presentation (RSVP), this correlation is absent. Here we study how subjects' target detection responses, both behavioral and electrophysiological, differ between continuous serial visual sequences (CSVP), flashed serial visual presentation (FSVP) and RSVP. Behavioral results show longer reaction times for CSVP compared to the FSVP and RSVP conditions, as well as a difference in miss-rate between RSVP and the other two conditions. Using mutual information, we measure electrophysiological differences in the EEG for these three conditions. We find two peaks in the mutual information between EEG and stimulus class (target vs. distractor), with the second peak occurring 30-40ms earlier for the FSVP and RSVP conditions. In addition, we find differences in the persistence of the peak mutual information between FSVP and RSVP conditions. We further investigate these differences using a mutual information based functional connectivity analysis and find significant fronto-parietal functional coupling for RSVP and FSVP but no significant coupling for the CSVP condition. We discuss these findings within the context of attentional engagement, evidence accumulation and short-term visual memory.

/pdf/comparing-neural-correlates-of-visual-target-detection-in-1wigsdnd5n.pdf

Comparing neural correlates of visual target detection in serial visual presentations having different temporal correlations.

We investigated neural correlates of target detection in the electroencephalogram (EEG) during a free viewing search task and analyzed signals locked to saccadic events. We adopted stimuli similar to ones we used previously to study target detection in serial presentations of briefly flashed images. Subjects performed the search task for multiple random scenes while we simultaneously recorded 64 channels of EEG and tracked subjects’ eye position. For each subject we identified target saccades (TS) and distractor saccades (DS). For TS we used saccades which were aimed directly to the target and were followed by a correct behavioral response (button press); for DS, we used saccades in correctly responded trials having no target (these were 28% of the trials). We sampled the sets of TS and DS saccades such that they were equalized/matched for saccade direction and duration, ensuring that no information in the saccade properties themselves was discriminating for their type. We aligned EEG to the saccade onset and used logistic regression (LR), in the space of the 64 electrodes, to identify activity discriminating a TS from a DS on a single-trial basis. Specifically, LR was applied to the signals from 50ms time windows preceding and following saccade onset for varying latencies. We found that there is significant discriminating activity in the EEG both before and after the saccade—average discriminability across 7 subjects was AUC=0.64, 80 ms before the saccade, and AUC=0.68, 60 ms after the saccade (p<0.01 established using bootstrap resampling). Between these time periods we saw substantial reduction in discriminating activity (mean AUC=0.59). We conclude that we can identify neural signatures of detection both before and after the saccade, indicating that subjects anticipate the target before the last saccade which serves to foveate and confirm it target identity.

We find before we Look: Neural signatures of target detection preceding saccades during visual search

We describe a spatio-temporal linear discriminator for single-trial classification of multi-channel electroencephalography (EEG). No prior information about the characteristics of the neural activity is required, i.e., the algorithm requires no knowledge about the timing and spatial distribution of the evoked responses. The algorithm finds a temporal delay/window onset time for each EEG channel and then spatially integrates the channels for each channel-specific onset time. The algorithm can be seen as learning discrimination trajectories defined within the space of EEG channels. We demonstrate the method for detecting auditory-evoked neural activity and discrimination of task difficulty in a complex visual-auditory environment.

An Luo

Papers

Spatiotemporal Linear Decoding of Brain State

Using single-trial EEG to estimate the timing of target onset during rapid serial visual presentation.

Comparing neural correlates of visual target detection in serial visual presentations having different temporal correlations.

We find before we Look: Neural signatures of target detection preceding saccades during visual search

Learning discrimination trajectories in EEG sensor space: application to inferring task difficulty