Bio-signal-based geometric modeling application for physically disabled users

Abstract

This paper discusses the challenges in achieving bio-signal-based design environments. While the main motivation of this paper was to provide a user interface for physically disabled people to express their artistic natures, a special emphasis is given on graphical user interface design where bio-signals are the single input source. Among three bio-signal sources investigated—electromyography, electrooculography and electroencephalography (EEG)—stimulus-based human–computer interaction design (EEG feature extraction method) is found to be the most promising for achieving design environments to perform complex tasks. In the proposed stimulus-based brain–computer-interaction application, the user communication with a computer is achieved by coupling intended functionalities with stimuli signals on the computer screen. Constant focus on the intended command stimulates the brain. In return, the brain releases a response signals (steady state visual evoked potential). In theory, brain’s response signals and the stimulus signals are identical. Once successfully identified, the presence of a signal pattern that is identical to the one of the alternative stimulus signals (paired with a command in a user interface) indicates the intention of a user. Since each option is associated with a unique signal pattern, multiple options can simultaneously be offered to users. The main challenge of working with stimulus signals is that the response signals are weak and they are buried inside of highly polluted EEG signals that include brain’s natural activities. In this paper, we introduce a signal processing algorithm based on Lorenz systems of differential equations for identifying the source of stimulus signals. Our experiments strongly suggest that bio-signal-based design environments to perform complex tasks, including geometric modeling can be achieved by utilizing stimulus-based signal processing methodology.

Appendices

Appendix 1: Calibration of baseline

The baseline $\left( B \right)$ is determined offline from $z\left( t \right)$ which is the output of the Lorenz system for $\lambda _i =0,\forall i$. In its chaos state ($\lambda _i =0,\forall i)$, Lorenz system output along z direction produces a feature as a sudden increase from its lowest values (highlighted area in Fig. 5). A baseline $\left( B \right)$ is determined in such a way that all peak values $\left( {z^{*}} \right)$ of $z\left( t \right)$ that consist of Lorenz system features at its chaos state are above the selected line. Initial value is determined empirically by observation from the fluctuation of $z\left( t \right)$ data.

Appendix 2: Calibration of the threshold for T

The threshold for $T\left( {u_t } \right)$ is determined during initializations at the beginning of experiments. Threshold is used to determine if there exists an external stimulus signal to the Lorenz system. Once a threshold value for B is determined, a series of T are identified from $z\left( t \right)$ which is the output of Lorenz system in its chaos state. Consequently a threshold value $\left( {T^{*}} \right)$ is identified from T. Experiments showed that average of $T \, \left( {\mu _T } \right)$ leads to a strong control capability as a threshold in our case. Consequently, we used $T^{*}=\mu _T$ in our experiments.