Performance enhancement of a brain-computer interface using high-density multi-distance NIRS

Abstract

This study investigated the effectiveness of using a high-density multi-distance source-detector (SD) separations in near-infrared spectroscopy (NIRS), for enhancing the performance of a functional NIRS (fNIRS)-based brain-computer interface (BCI). The NIRS system that was used for the experiment was capable of measuring signals from four SD separations: 15, 21.2, 30, and 33.5 mm, and this allowed the measurement of hemodynamic response alterations at various depths. Fifteen participants were asked to perform mental arithmetic and word chain tasks, to induce task-related hemodynamic response variations, or they were asked to stay relaxed to acquire a baseline signal. To evaluate the degree of BCI performance enhancement by high-density channel configuration, the classification accuracy obtained using a typical low-density lattice SD arrangement, was compared to that obtained using the high-density SD arrangement, while maintaining the SD separation at 30 mm. The analysis results demonstrated that the use of a high-density channel configuration did not result in a noticeable enhancement of classification accuracy. However, the combination of hemodynamic variations, measured by two multi-distance SD separations, resulted in the significant enhancement of overall classification accuracy. The results of this study indicated that the use of high-density multi-distance SD separations can likely provide a new method for enhancing the performance of an fNIRS-BCI.

Comparison of Feature Vector Compositions to Enhance the Performance of NIRS-BCI-Triggered Robotic Hand Orthosis for Post-Stroke Motor Recovery

“…Channel-preserved feature vector compositions generated better performance than channel-averaged compositions in terms of classification accuracy and detection latency. Classification after averaging all channels have reportedly produced accuracies of 78% [22] and 65-75% [23]. This approach might be effective and useful for a BCI system with fewer channels that is only concerned with one cortical area.…”

“…Although our single source-detector separation (SDS) of 3 cm could not filter out those extracerebral influences, it is unlikely that the inclusion of those extracerebral influences might compromise our study result. This is because the BCI study with a high-density multi-distance NIRS [23] showed that the use of multi-distance SDS NIRS improved accuracy by 5.2% compared to that of single SDS NIRS. Accordingly, it is inferred that our study results could be improved with a technique against the extracerebral contamination.…”

Performance Improvement of Near-Infrared Spectroscopy-Based Brain-Computer Interface Using Regularized Linear Discriminant Analysis Ensemble Classifier Based on Bootstrap Aggregating

“…the market and their usefulness in NIRS-BCIs has been verified (Shin et al, 2017a;Kim et al, 2018;Kwon et al, 2018;Lancia et al, 2018). However, most of the new form factors adopted by the recent NIRS systems do not possess general applicability because they are designed to record hemodynamic changes from the prefrontal area only.…”

Architecture of a Wheelchair Control System for Disabled People: Towards Multifunctional Robotic Solution with Neurobiological Interfaces

“…However, in some cases it is not possible to achieve the required speed or accuracy of the control. Thus, existing neural interfaces based on electroencephalogram or fNIRS have a time resolution measured in seconds under optimal conditions [3], and are not suitable for direct control. To restore some possibilities of vision, the devices, that verbally describe objects appeared in front of a person, are recently developed [4].…”