Abstract

Brain machine interfaces (BMIs) provide one of the important means to interact with external world for a patient who lost motor function by using brain electrical signals such as scalp electroencephalogram (EEG). The main function of BMIs is to detect, analyze and classify the modulation of EEG according to a user's intent and to decode it to demonstrate to others. Most of conventional BMIs with relatively high speed and high classification accuracy have a requisite that a user can open and move his/her eyes toward a target corresponding to user's intent and gaze at it. Locked-in patients, who have lost almost all motor function including facial muscle, cannot control the eye-movement as well as gaze. The present study aimed at developing a BMI which was independent of the eye-movement and eye-gaze as a final goal. In this paper, we discuss the feasibility of binary class of BMI available with eyes-closed condition using a recent our newly observation that the steady-state visual evoked potential (SSVEP) could be modulated by performing the mental tasks even in the eyes-close state. Temporal evolution of the modulation of SSVEP was investigated using the amplitude of SSVEP and Kullback-Leibler (KL) divergence. The amplitudes of fundamental SSVEP (10 Hz) averaged across 11 participants showed significant difference between the mental focusing task and relaxed runs from the onset of the flicker stimuli with the frequency of 10 Hz and the intensity of 5 lx, whereas those of the second harmonic SSVEP (20 Hz) gave only small difference without statistical significance. For the image recalling task, the amplitude differences between the fundamental SSVEPs in the task and relaxed conditions did not show significant difference, whereas the amplitude differences of the second harmonic SSVEP showed the main effect of the image recall task. The KL divergences for the fundamental SSVEP amplitude showed large differences at the onset of flicker stimuli and gradual decrease with time at P4 and O2 for the mental focus task, yet no such characteristic evolution was not seen in KL divergence for the second harmonic SSVEP. On the other hand, the KL divergences during the image recalling were increased with time for both the fundamental and second harmonic SSVEPs at most of the electrode sites. The classification accuracies of the proposed BMI exceeded 75 % in average across the subjects for both tasks using Fisher's linear discriminant analysis.

Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call