Dong Ok Won

Effect of higher frequency on the classification of steady-state visual evoked potentials

OBJECTIVE Most existing brain-computer interface (BCI) designs based on steady-state visual evoked potentials (SSVEPs) primarily use low frequency visual stimuli (e.g., <20 Hz) to elicit relatively high SSVEP amplitudes. While low frequency stimuli could evoke photosensitivity-based epileptic seizures, high frequency stimuli generally show less visual fatigue and no stimulus-related seizures. The fundamental objective of this study was to investigate the effect of stimulation frequency and duty-cycle on the usability of an SSVEP-based BCI system. APPROACH We developed an SSVEP-based BCI speller using multiple LEDs flickering with low frequencies (6-14.9 Hz) with a duty-cycle of 50%, or higher frequencies (26-34.7 Hz) with duty-cycles of 50%, 60%, and 70%. The four different experimental conditions were tested with 26 subjects in order to investigate the impact of stimulation frequency and duty-cycle on performance and visual fatigue, and evaluated with a questionnaire survey. Resting state alpha powers were utilized to interpret our results from the neurophysiological point of view. MAIN RESULTS The stimulation method employing higher frequencies not only showed less visual fatigue, but it also showed higher and more stable classification performance compared to that employing relatively lower frequencies. Different duty-cycles in the higher frequency stimulation conditions did not significantly affect visual fatigue, but a duty-cycle of 50% was a better choice with respect to performance. The performance of the higher frequency stimulation method was also less susceptible to resting state alpha powers, while that of the lower frequency stimulation method was negatively correlated with alpha powers. SIGNIFICANCE These results suggest that the use of higher frequency visual stimuli is more beneficial for performance improvement and stability as time passes when developing practical SSVEP-based BCI applications.

A BCI speller based on SSVEP using high frequency stimuli design

We developed and studied a Steady-State Visual Evoked Potential (SSVEP) based BCI system using a high frequency visual stimuli (>25Hz) design for reducing visual fatigue. Existing SSVEP based BCI designs primarily use low frequency visual stimuli (<;20Hz) for eliciting relatively higher SSVEP signal, while the low frequency stimuli can provocate photosensitivity epileptic seizure. On the other hand, high frequency stimuli are visually more comfortable and cause less visual fatigue and seizure. To detect the weak high frequency SSVEP signal, we used multi-channel EEG and introduced canonical correlation analysis to identify the elicited SSVEP frequency. We designed and built a 30-character SSVEP BCI speller system without calibration and evaluated the performance metrics including classification accuracy and subjective fatigue ratings, in both high-frequency and low frequency SSVEP modes. The result indicates that the high frequency stimuli system archieved higher classification accuracy (averaged 80% in the 30-class classification) comparable to that by the low frequency system. Moreover, no subjects rated the visual feeling as unacceptable or uncomfortable with the high frequency system.

Network Properties in Transitions of Consciousness during Propofol-induced Sedation

Reliable electroencephalography (EEG) signatures of transitions between consciousness and unconsciousness under anaesthesia have not yet been identified. Herein we examined network changes using graph theoretical analysis of high-density EEG during patient-titrated propofol-induced sedation. Responsiveness was used as a surrogate for consciousness. We divided the data into five states: baseline, transition into unresponsiveness, unresponsiveness, transition into responsiveness, and recovery. Power spectral analysis showed that delta power increased from responsiveness to unresponsiveness. In unresponsiveness, delta waves propagated from frontal to parietal regions as a traveling wave. Local increases in delta connectivity were evident in parietal but not frontal regions. Graph theory analysis showed that increased local efficiency could differentiate the levels of responsiveness. Interestingly, during transitions of responsive states, increased beta connectivity was noted relative to consciousness and unconsciousness, again with increased local efficiency. Abrupt network changes are evident in the transitions in responsiveness, with increased beta band power/connectivity marking transitions between responsive states, while the delta power/connectivity changes were consistent with the fading of consciousness using its surrogate responsiveness. These results provide novel insights into the neural correlates of these behavioural transitions and EEG signatures for monitoring the levels of consciousness under sedation.

Spatio-temporal dynamics of multimodal EEG-fNIRS signals in the loss and recovery of consciousness under sedation using midazolam and propofol

On sedation motivated by the clinical needs for safety and reliability, recent studies have attempted to identify brain-specific signatures for tracking patient transition into and out of consciousness, but the differences in neurophysiological effects between 1) the sedative types and 2) the presence/absence of surgical stimulations still remain unclear. Here we used multimodal electroencephalography–functional near-infrared spectroscopy (EEG–fNIRS) measurements to observe electrical and hemodynamic responses during sedation simultaneously. Forty healthy volunteers were instructed to push the button to administer sedatives in response to auditory stimuli every 9–11 s. To generally illustrate brain activity at repetitive transition points at the loss of consciousness (LOC) and the recovery of consciousness (ROC), patient-controlled sedation was performed using two different sedatives (midazolam (MDZ) and propofol (PPF)) under two surgical conditions. Once consciousness was lost via sedatives, we observed gradually increasing EEG power at lower frequencies (<15 Hz) and decreasing power at higher frequencies (>15 Hz), as well as spatially increased EEG powers in the delta and lower alpha bands, and particularly also in the upper alpha rhythm, at the frontal and parieto-occipital areas over time. During ROC from unconsciousness, these spatio-temporal changes were reversed. Interestingly, the level of consciousness was switched on/off at significantly higher effect-site concentrations of sedatives in the brain according to the use of surgical stimuli, but the spatio-temporal EEG patterns were similar, regardless of the sedative used. We also observed sudden phase shifts in fronto-parietal connectivity at the LOC and the ROC as critical points. fNIRS measurement also revealed mild hemodynamic fluctuations. Compared with general anesthesia, our results provide insights into critical hallmarks of sedative-induced (un)consciousness, which have similar spatio-temporal EEG-fNIRS patterns regardless of the stage and the sedative used.

Classification of wakefulness and anesthetic sedation using combination feature of EEG and ECG

There have been lots of trials to classify a depth of anesthesia using diverse physiological indices. In this study, we classified wakefulness and propofol-induced sedation using combined electroencephalography (EEG) and electrocardiography (ECG) features for better classification performance. We extract each spectral band of EEG and very low frequency (VLF) of heart rate variability using spectrogram and low-pass filter, respectively. We used combined feature of EEG spectral bands and VLF and shrinkage-regularized linear discriminant analysis as a classifier. Our results show that combination of EEG spectral power and VLF can improve the classification performance between wakefulness and sedation from 95.1±5.3% to 96.4±4.2%.

Classification of left and right foot movement intention based on steady-state somatosensory evoked potentials

Recently, steady-state somatosensory evoked potentials (SSSEPs) which are brain responses to tactile stimulation of specific frequency in somatosensory have been researched in brain-computer interface (BCI) groups. Classification of both feet is important in gait control system. Previous SSSEP studies have mainly researched a feasibility of discrimination by stimulator attached on upper limb (e.g., finger or arm). However, SSSEP-based classification of both feet could be useful in BCI-based gait rehabilitation system. Hence, we investigate a possibility of discrimination of both feet using SSSEP. To this end, we obtain optimal stimuli frequencies in the screening session. In subsequence test session, the optimal stimuli were attached on the left and right foot, respectively. Six healthy subjects conducted the task which was the subjects concentrate on the tactile stimuli following by random visual cue. The classification results show 72.6% and 72.2% in two methods (i.e., common spatial pattern (CSP) and power spectral density (PSD)). Furthermore, we analyzed differences of spatial and spectral features for reliable BCI performance. These results suggest that classification both feet can be available in SSSEP-based BCI for gait rehabilitation.

Shifting stimuli for brain computer interface based on rapid serial visual presentation

Most of event-related potential (ERP)-based brain-computer interface (BCI) spellers are limited practical value for paralyzed patients with severe oculomotor impairments. Recently, a gaze-independent BCI speller was proposed that uses rapid serial visual presentation (RSVP), but it is difficult to recognize targets because of the rapid presentation of characters. We developed two ERP-based BCI spellers using RSVP with motion, and non-motion stimulation. We evaluated the effect of the two different stimulus conditions on the performance of the speller system with eight participants. The stimulation methods that employ motion stimulation inside the foveal vision demonstrate not only gaze-independence but also higher performance than method that uses non-motion stimulation (88.9% for non-motion RSVP, 90.3% for motion RSVP). The performance of the different stimulation methods was susceptible to ERP latency and amplitudes. As a result, motion-type RSVP stimulation condition (i.e., motion RSVP) had shorter latency and higher amplitudes than the non-motion RSVP stimulation condition. It is expected that the proposed motion RSVP stimulation method could be used for developing a gaze independent BCI system with high performance.

Analysis of steady state visual evoked potentials based on viewing distance changes for brain-machine interface speller

Recently, steady-state visual evoked potential (SSVEP)-based brain-machine interface (BMI) speller systems have shown a great performance increase with high information transfer rate (ITR) and short response time. In previous BMI speller systems, however, users should utilize the systems at fixed viewing distance environment for evoking SSVEP signals because a variation of the SSVEP signals according to changes of viewing distance was not considered during system design process. For a real-world application of BMI speller, reliable speller systems which are robust to various viewing distance environment are needed. In this study, hence, we investigate the effects of viewing distance on SSVEP by changing distance between a user and visual stimuli. Here, we used four visual stimuli which have different frequencies using LED monitor. In the subsequent analysis, we present classification results with several methods. Our analysis and results show a possibility that SSVEP under various viewing distance environment could be facilitated.