Design and study of two applications controlled by a Brain-Computer Interface exploiting Steady-State Somatosensory-Evoked Potentials

Abstract: Brain-Computer Interfaces (BCI) allow users to interact with machines without involving muscles. Patients with heavy motor impairment can benefit from these systems. Different states of mind of a user are discriminated to translate them into basic commands (left, right, etc.). But traditional BCI are mainly based on visual attention, and users can be quickly tired (eye fatigue, repetitive tasks, etc.). In some cases, the sight is not available for a relevant BCI, while the sense of touch can remain usable.We have implemented an electroencephalography-based BCI using the user's sense of touch. This paper describes the design and study of two BCI applications controlled by an Steady-State Somatosensory-Evoked Potentials (SSSEP), over a control group of 10 healthy subjects.We use two mechanical vibrators (C2 tactors) taped to each wrist. When they vibrate at a specific frequency, SSSEP are measurable on the electroencephalography. A somatosensory gating is the capacity of the brain to filter out stimuli perceived as redundant or irrelevant during a goal-oriented activity. This phenomenon is exploited in our BCI by asking users to imagine themselves moving their arms (motor imagery) while they are perceiving vibrations on them. These intentions are detectable in 4 possible classes (idle, left arm, right arm, and both arms simultaneous).The first application is a 2-level puzzle called SokoBCI, in which the user controls the motion of a 3D avatar and have to reach different locations to plant trees. The second application consists of driving a go-kart around a figure-of-eight track. For both applications, the main instruction given to the users is to perform the task as fast as possible. The major difference between these applications is inertia, present in the kart model, and not in SokoBCI. SokoBCI levels design and the road circuit are designed to balance the use of turns and forward commands. The "idle" command is used as a default state of mind command, therefore, it should be avoided during active control of the application. The rhythm of the command during a block, and therefore the presence of mechanical stimulations, as well as the feedback and breaks, are cued using a three-colour light.Each participant performed 4 blocks of recordings, for each application. Each block lasts around 7 minutes, except in SokoBCI if the levels are finished before, and have a fixed Sham feedback accuracy. Sham feedback is used to artificially fix the performance of the system. The accuracy is fixed during a block and could take the values: 45%, 60%, 75% and 90%. The four possible accuracies are tested once for each application during the session. Applications and tested accuracies order are pseudo-random. The applications (design and goal), as well as the given instructions are designed so that one or two (max) commands are "good" at each step, depending on the current state of the applications.We measure two kinds of data:- Quantitative: at the beginning and end of each series of 4 blocks, i.e. beginning and end of one application, a questionnaire about the awakeness, fatigue, mood orientation or experienced pain level from the stimulation of the vibration is given to the participant.In addition, at the end of every block, a NASA Task Load Index and System Usability Scale questionnaire are filled up by the users.- Qualitative: at the end of the session, a short interview is also conducted.We collect the user's perception of the experiment and Sham feedback.Using the SUS scores (0-100), we build a model of the relationship between the accuracy of the system and the perceived usability of each application. Therefore, we can predict a range of accuracies to reach, in order to achieve a specific degree of perceived usability. For example, thanks to our model, an accuracy of 80% is matched to a SUS Score of 70, which itself could be qualified as "Good", according to the well-known mapping between SUS Score and adjectives.

Keywords: BCI, SSSEP, CHI, brain, health, vibration

DOI: 10.54941/ahfe1002787

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