Towards a communication brain computer interface based on semantic relations.

This article investigates a possible Brain Computer Interface (BCI) based on semantic relations. The BCI determines which prime word a subject has in mind by presenting probe words using an intelligent algorithm. Subjects indicate when a presented probe word is related to the prime word by a single finger tap. The detection of the neural signal associated with this movement is used by the BCI to decode the prime word. The movement detector combined both the evoked (ERP) and induced (ERD) responses elicited with the movement. Single trial movement detection had an average accuracy of 67%. The decoding of the prime word had an average accuracy of 38% when using 100 probes and 150 possible targets, and 41% after applying a dynamic stopping criterium, reducing the average number of probes to 47. The article shows that the intelligent algorithm used to present the probe words has a significantly higher performance than a random selection of probes. Simulations demonstrate that the BCI also works with larger vocabulary sizes, and the performance scales logarithmically with vocabulary size.

Detecting semantic priming at the single-trial level.

Semantic priming is usually studied by examining ERPs over many trials and subjects. This article aims at detecting semantic priming at the single-trial level. By using machine learning techniques it is possible to analyse and classify short traces of brain activity, which could, for example, be used to build a Brain Computer Interface (BCI). This article describes an experiment where subjects were presented with word pairs and asked to decide whether the words were related or not. A classifier was trained to determine whether the subjects judged words as related or unrelated based on one second of EEG data. The results show that the classifier accuracy when training per subject varies between 54% and 67%, and is significantly above chance level for all subjects (N  = 12) and the accuracy when training over subjects varies between 51% and 63%, and is significantly above chance level for 11 subjects, pointing to a general effect.

Introducing the tactile speller: an ERP-based brain-computer interface for communication.

In this study, a tactile speller was developed and compared with existing visual speller paradigms in terms of classification performance and elicited event-related potentials (ERPs). The fingertips of healthy participants were stimulated with short mechanical taps while electroencephalographic activity was measured. The letters of the alphabet were allocated to different fingers and subjects could select one of the fingers by silently counting the number of taps on that finger. The offline and online performance of the tactile speller was compared to the overt and covert attention visual matrix speller and the covert attention Hex-o-Spell speller. For the tactile speller, binary target versus non-target classification accuracy was 67% on average. Classification and decoding accuracies of the tactile speller were lower than the overt matrix speller, but higher than the covert matrix speller, and similar to Hex-o-Spell. The average maximum information transfer rate of the tactile speller was 7.8 bits min(-1) (1.51 char min(-1)), with the best subject reaching a bit-rate of 27 bits min(-1) (5.22 char min(-1)). An increased amplitude of the P300 ERP component was found in response to attended stimuli versus unattended stimuli in all speller types. In addition, the tactile and overt matrix spellers also used the N2 component for discriminating between targets and non-targets. Overall, this study shows that it is possible to use a tactile speller for communication. The tactile speller provides a useful alternative to the visual speller, especially for people whose eye gaze is impaired.

Dense codes at high speeds: varying stimulus properties to improve visual speller performance.

This paper investigates the effect of varying different stimulus properties on performance of the visual speller. Each of the different stimulus properties has been tested in previous literature and has a known effect on visual speller performance. This paper investigates whether a combination of these types of stimuli can lead to a greater improvement. It describes an experiment aimed at answering the following questions. (i) Does visual speller performance suffer from high stimulus rates? (ii) Does an increase in stimulus rate lead to a lower training time for an online visual speller? (iii) What aspect of the difference in the event related potential to a flash or a flip stimulus causes the increase in accuracy. (iv) Can an error-correcting (dense) stimulus code overcome the reduction in performance associated with decreasing target-to-target intervals? We found that higher stimulus rates can improve the visual speller performance and can lead to less time required to train the system. We also found that a proper stimulus code can overcome the stronger response to rows and columns, but cannot greatly improve speller performance.

The brain-computer interface cycle.

Brain-computer interfaces (BCIs) have attracted much attention recently, triggered by new scientific progress in understanding brain function and by impressive applications. The aim of this review is to give an overview of the various steps in the BCI cycle, i.e., the loop from the measurement of brain activity, classification of data, feedback to the subject and the effect of feedback on brain activity. In this article we will review the critical steps of the BCI cycle, the present issues and state-of-the-art results. Moreover, we will develop a vision on how recently obtained results may contribute to new insights in neurocognition and, in particular, in the neural representation of perceived stimuli, intended actions and emotions. Now is the right time to explore what can be gained by embracing real-time, online BCI and by adding it to the set of experimental tools already available to the cognitive neuroscientist. We close by pointing out some unresolved issues and present our view on how BCI could become an important new tool for probing human cognition.