Study overview
Brain-computer interfaces (BCIs) is an umbrella term for the systems that allow measuring brain activity and its consecutive translation into the computer-readable signals. Thus, they can be used as therapeutical tools in the situations where the dirrect communication between brain and computer (i.e. environment) is desirable. So called locked-in syndrome (where patient cannot move and verbally communicate due to progressive paralysis of nearly all voluntary muscles while his/her thinking, understanding, perceptive and memory processes are complete perserved) is extreme but illustrative clinical example where BCIs systems can be applied. The communication is mediated for instance through mental and motor imagery processes. Patients provide the imagination of certain activity (e.g. hand movement) that is associated with particular environmental outcome (e.g. forward movement of the wheelchair). The imagination is accompanied with specific brain activity, the classification algorithm reads these brain signals and tries to correctly classify and estimate what they represent so that computer may execute the desirable action.
There are two important factors that influence if the BCIs will be beneficial for the patient so that he/she can profit from it. First factor is how well the person understands and conducts the mental imagery tasks as before he/she may perform them correctly they need to be instructed and trained in them because badly performed task cannot be properly distinguished. In the current BCI literature still lacks consensus which way of instruction presentation is the best for the users. In the current study we will thus assess the effect of two different instruction presentation modes to the user performance. Second important factor for the overall performance is the ability of the classifying algorithms to correctly discriminate the individual types of mental imagery tasks. The current classifiers don't have absolute accuracy in the classification. Thus, in our study we will examine the classification accuracy of two different classification approaches.
There are two important factors that influence if the BCIs will be beneficial for the patient so that he/she can profit from it. First factor is how well the person understands and conducts the mental imagery tasks as before he/she may perform them correctly they need to be instructed and trained in them because badly performed task cannot be properly distinguished. In the current BCI literature still lacks consensus which way of instruction presentation is the best for the users. In the current study we will thus assess the effect of two different instruction presentation modes to the user performance. Second important factor for the overall performance is the ability of the classifying algorithms to correctly discriminate the individual types of mental imagery tasks. The current classifiers don't have absolute accuracy in the classification. Thus, in our study we will examine the classification accuracy of two different classification approaches.
What you will do?
You will have the opportunity to meet as in person, as to participate in the TIC study you need to be in person in Groningen, the Netherlands. In the first phase of the study, you will be instructed either by text or videos on how to perform mental imagery. Then you will be requested to fill in some questionnaires. In the second phase of the study, you will wear an electroencephalography (EEG) cap. Then you will be asked to perform certain exercises in a computer task.
You can take part in our experiment if you:
► are older than 18 years
► speak fluently English, German, or Dutch
► speak fluently English, German, or Dutch