Bio-Signal Based Human-Computer Interface for Geometric Modeling

Modern techniques drive the development of bio-signal controlled assistive devices such as prosthetics, wheelchairs, etc. The control of these devices needs the accurate acquisition of bio-signal features and the accomplishment of multiple control intentions. The limited bio-signal sources limit the amount of available bio-signal features. Therefore, the accomplishment of multiple control intentions in some cases can’t only depend on the classification of bio-signals. In this thesis, we develop a bio-signal controlled real-time 3D geometric modeling design platform, which focus on the study of two aspects: 1) Identifying the control capabilities of bio-signals, 2) Developing 3D geometric modeling. In the study of control capabilities of bio-signals, we propose three efficient bio-signal feature extraction approaches and develop logic control panels which are used to achieve the multiple control intentions. In this thesis, four main original contributions are made in developing bio-signal controlled geometric modeling design platform as follows: First, three types of bio-signal controlled Human-Computer Interface(HCI), Electromyography(EMG) based HCI, Electrooculography(EOG) based HCI and Electroencephalography(EEG) based HCI are designed, in which the bipolar electrodes are used for bio-signal acquisition and the bio-signal sources that can generate strong signal patterns are identified. The identified bio-signal sources maintain the acquired bio-signals with a relative high Signal-to-Noise Ratio (SNR), thus simplifying the signal feature extraction methods. Second, in order to achieve multiple control intentions, an approach of logic control panels is proposed in EMG and EOG based HCI systems. The logic control panels are designed with two advantages. One advantage is that it accomplishes the control intentions; the other is that it reduces the fatigue of the bio-signal sources so that the accuracies and stabilities of the control from the bio-signals are maintained well. Third, a new approach is proposed to extract signal features based on a Steady-State Visual Evoked Potential (SSVEP). Due to the periodic feature of the stimulation signals, the scientific research indicates that the same periodic features exist in EEG responses. Hence, in time domain a weak periodic signal detection algorithm (WPSDA) is proposed, which aims at detecting the brain’s weak responses to the visual periodic stimulation signals under heavy noisy background. This algorithm is depicted by Lorenz system which describes a nonlinear dynamic system. Such a nonlinear dynamic system is sensitive to its system parameters. Once the parameters are carefully calibrated, Lorenz system equations can detect the input waves (stimulation signal patterns) inside of the response waves (EEG signals) if brain positively responds to the stimulation. Last, the control accuracy of the extracted signal features was verified on the corresponding bio-signal controlled geometric modeling systems. The geometric modeling systems are formed mainly by free-form parametric splines, parametric surfaces and rotation geometries. Through online tests, the control accuracy rate up to 100% was obtained for the EMG and EOG based HCI systems and up to 75% control accuracy rate was obtained for the EEG based BCI system.