Effect of coupled graphene oxide on the sensitivity of surface plasmon resonance detection.

We investigated graphene-oxide-(GO-) coupled surface plasmon resonance (SPR) detection sensitivity for sandwiched antigen-antibody interaction between human and antihuman immunoglobulin G molecules. GO was prepared in a Langmuir-Blodgett solution on gold and dielectric surfaces. Theoretical and experimental data suggest that an increased dielectric spacer thickness reduces resonance shifts for GO-coupled SPR detection as dielectric properties of GO appear to prevail. In general, a metal-enhanced structure was shown to provide a larger resonance shift by plasmonic field enhancement. The far-field properties were described in terms of near-field overlap. The peak resonance shift that was obtained with GO-coupled SPR detection was enhanced to 113% of the resonance shift obtained by conventional thin-film-based SPR detection and may further be improved by GO stacking.

Performance evaluation of a motor-imagery-based EEG-Brain computer interface using a combined cue with heterogeneous training data in BCI-Naive subjects.

BACKGROUND: The subjects in EEG-Brain computer interface (BCI) system experience difficulties when attempting to obtain the consistent performance of the actual movement by motor imagery alone. It is necessary to find the optimal conditions and stimuli combinations that affect the performance factors of the EEG-BCI system to guarantee equipment safety and trust through the performance evaluation of using motor imagery characteristics that can be utilized in the EEG-BCI testing environment. METHODS: The experiment was carried out with 10 experienced subjects and 32 naive subjects on an EEG-BCI system. There were 3 experiments: The experienced homogeneous experiment, the naive homogeneous experiment and the naive heterogeneous experiment. Each experiment was compared in terms of the six audio-visual cue combinations and consisted of 50 trials. The EEG data was classified using the least square linear classifier in case of the naive subjects through the common spatial pattern filter. The accuracy was calculated using the training and test data set. The p-value of the accuracy was obtained through the statistical significance test. RESULTS: In the case in which a naive subject was trained by a heterogeneous combined cue and tested by a visual cue, the result was not only the highest accuracy (p < 0.05) but also stable performance in all experiments. CONCLUSIONS: We propose the use of this measuring methodology of a heterogeneous combined cue for training data and a visual cue for test data by the typical EEG-BCI algorithm on the EEG-BCI system to achieve effectiveness in terms of consistence, stability, cost, time, and resources management without the need for a trial and error process.