Target processing in overt serial visual search involves the dorsal attention network: A fixation-based event-related fMRI study.

In serial visual search we shift attention successively from location to location in search for the target. Although such search has been investigated using fMRI, overt attention (i.e., eye movements) was usually neglected or discouraged. As a result, it is unclear what happens in the instant when our gaze falls upon a target as compared to a distractor. In the present experiment, we used a multiple target search task that required eye movements and employed an analysis based on fixations as events of interest to investigate differences between target and distractor processing. Twenty young healthy adults indicated the number of targets (0-3) among distractors in a 20-item display. Compared to distractor fixations, we found that target fixations gave rise to wide-spread activation in the dorsal attention system, as well as in the visual cortex. Targets that were found later during the search activated the left inferior frontal gyrus and the left supramarginal gyrus more strongly than those that were found earlier. Finally, areas associated with visual and verbal working memory showed increased activation with a larger number of targets in the display.

Target probability modulates fixation-related potentials in visual search.

This study investigated the influence of target probability on the neural response to target detection in free viewing visual search. Participants were asked to indicate the number of targets (one or two) among distractors in a visual search task while EEG and eye movements were co-registered. Target probability was manipulated by varying the set size of the displays between 10, 22, and 30 items. Fixation-related potentials time-locked to first target fixations revealed a pronounced P300 at the centro-parietal cortex with larger amplitudes for set sizes 22 and 30 than for set size 10. With increasing set size, more distractor fixations preceded the detection of the target, resulting in a decreased target probability and, consequently, a larger P300. For distractors, no increase of P300 amplitude with set size was observed. The findings suggest that set size specifically affects target but not distractor processing in overt serial visual search.

Composing only by thought: Novel application of the P300 brain-computer interface.

The P300 event-related potential is a well-known pattern in the electroencephalogram (EEG). This kind of brain signal is used for many different brain-computer interface (BCI) applications, e.g., spellers, environmental controllers, web browsers, or for painting. In recent times, BCI systems are mature enough to leave the laboratories to be used by the end-users, namely severely disabled people. Therefore, new challenges arise and the systems should be implemented and evaluated according to user-centered design (USD) guidelines. We developed and implemented a new system that utilizes the P300 pattern to compose music. Our Brain Composing system consists of three parts: the EEG acquisition device, the P300-based BCI, and the music composing software. Seventeen musical participants and one professional composer performed a copy-spelling, a copy-composing, and a free-composing task with the system. According to the USD guidelines, we investigated the efficiency, the effectiveness and subjective criteria in terms of satisfaction, enjoyment, frustration, and attractiveness. The musical participants group achieved high average accuracies: 88.24% (copy-spelling), 88.58% (copy-composing), and 76.51% (free-composing). The professional composer achieved also high accuracies: 100% (copy-spelling), 93.62% (copy-composing), and 98.20% (free-composing). General results regarding the subjective criteria evaluation were that the participants enjoyed the usage of the Brain Composing system and were highly satisfied with the system. Showing very positive results with healthy people in this study, this was the first step towards a music composing system for severely disabled people.

Single Versus Multiple Events Error Potential Detection in a BCI-Controlled Car Game With Continuous and Discrete Feedback.

OBJECTIVE: This work aimed to find and evaluate a new method for detecting errors in continuous brain-computer interface (BCI) applications. Instead of classifying errors on a single-trial basis, the new method was based on multiple events (MEs) analysis to increase the accuracy of error detection. METHODS: In a BCI-driven car game, based on motor imagery (MI), discrete events were triggered whenever subjects collided with coins and/or barriers. Coins counted as correct events, whereas barriers were errors. This new method, termed ME method, combined and averaged the classification results of single events (SEs) and determined the correctness of MI trials, which consisted of event sequences instead of SEs. The benefit of this method was evaluated in an offline simulation. In an online experiment, the new method was used to detect erroneous MI trials. Such MI trials were discarded and could be repeated by the users. RESULTS: We found that, even with low SE error potential (ErrP) detection rates, feasible accuracies can be achieved when combining MEs to distinguish erroneous from correct MI trials. Online, all subjects reached higher scores with error detection than without, at the cost of longer times needed for completing the game. CONCLUSION: Findings suggest that ErrP detection may become a reliable tool for monitoring continuous states in BCI applications when combining MEs. SIGNIFICANCE: This paper demonstrates a novel technique for detecting errors in online continuous BCI applications, which yields promising results even with low single-trial detection rates.