| dc.description.abstract | Brain computer interface (BCI) is an emerging technology for paralyzed patients to communicate with external environments. Among current BCIs, the steady-state visual evoked potential (SSVEP)-based BCI has drawn great attention due to its characteristics of easy preparation, high information transfer rate (ITR), high accuracy, and low cost. Due to the amplitude-frequency characteristic of SSVEP, the flickering frequency of an SSVEP-based BCI is typically lower than 20 Hz to achieve high signal-to-noise ratio (SNR). However, a visual flicker with a flashing frequency below the critical flicker-fusion frequency often makes subjects feel flicker jerky, and causes visual discomfort. In addition, electroencephalogram (EEG) signals are electrophysiological responses reflecting the underlying neural activities which are dependent upon subject’s physiological states (e.g., emotion, attention, etc.) and usually variant among different individuals. The development of classification approaches to account for each individual’s difference in SSVEP is needed but was seldom reported. To overcome the above two problems, hence, the dissertation is divided into two studies. In the first study, we present a novel technique using high duty-cycle visual flicker to decrease user’s visual discomfort. The proposed design uses light emitting diodes (LEDs) flashing at 13.16 Hz, driven by flickering sequences consisting of repetitive stimulus cycles with a duration of T (T = 76 ms). Each stimulus cycle included an ON state with a duration TON and an OFF state with a duration TOFF (T = TON + TOFF), and the duty cycle, defined as TON/T, varied from 10.5% to 89.5%. This study also includes a questionnaire survey, and analyzes the SSVEPs induced by different duty-cycle flickers. An 89.5% duty-cycle flicker, reported as a comfortable flicker, was adopted in a phase-tagged SSVEP system. Six subjects were asked to sequentially input a sequence of cursor commands with 25.08 bits/min ITR. In the second study, a multiclass support vector machine (SVM)-based classification approach is proposed for gaze-target detections in a phase-tagged SSVEP-based BCI. In the training steps, the amplitude and phase features of SSVEP from off-line recordings were used to train a multiclass SVM for each subject. In the on-line application study, effective epochs which contained sufficient SSVEP information of gaze targets were first determined using Kolmogorov-Smirnov (K-S) test, and the amplitude and phase features of effective epochs were subsequently inputted to the multiclass SVM to recognize user’s gaze targets. The on-line performance using the proposed approach has achieved high accuracy (89.88 ± 4.76 %), fast responding time (effective epoch length = 1.13 ± 0.02 s), and the ITR was 50.91 ± 8.70 bits/min. The multiclass SVM-based classification approach has been successfully implemented to improve the classification accuracy in a phase-tagged SSVEP-based BCI. The present study has shown the multiclass SVM can be effectively adapted to each subject’s SSVEPs to discriminate SSVEP phase information from gazing at different gazed targets. | en_US |