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The SVM classifier achieved an accuracy of Spatial activation patterns could be distinguished significantly above chance level, assessed by permutation testing, in four out of 18 participants indicated with a symbol in the top plot in Figure 9. The average rate of correct detection of MD was Figure 9. Decoding accuracies of individual participants and the sample mean obtained with the single-trial light-colored bars and the multi-trial dark-colored bars multivariate approach.

The upper plot shows decoding accuracies of the SVM classifier, the lower plot shows decoding accuracies of the SVM classifier. The SVM classif ier achieved an accuracy of Spatial activation patterns could be distinguished significantly above chance level, assessed by permutation testing, in seven out of 18 participants indicated with a symbol in the bottom plot in Figure 9.

The average decoding accuracy of these eight participants was Regression analyses with accuracy as the criterion variable and SNR as the predictor variable revealed the following results see Supplementary Figure S4. The last run shows lowered although still acceptable comfort scores 6. Not a single participant indicated a comfortability score lower than 5 during the experiment.

Figure Mean comfortability rating over time fNIRS runs. The ten fNIRS runs are depicted in the order they were conducted in the experiment. The last two runs, MD2 and SN2, were localizer runs block 2. Error bars reflect standard deviations. Overall both tasks were deemed easy and pleasant.

On average the SN task was considered more difficult to perform 6. Ease and pleasantness ratings correlated significantly with the accuracy of the SVM analysis, whereas all other correlations were not significant see Table 3 for all correlations. Table 3. Correlation table of ease and pleasantness ratings with the eight accuracy outcomes variables. We presented a novel binary communication paradigm that aimed to exploit spatiotemporal characteristics of fNIRS signals evoked by differently timed mental imagery tasks.

The paradigm involved minimal training and a sparse optode setup of only nine optodes three sources, six detectors. The applied goal was to test decoding success and feasibility of the current paradigm compared to previous paradigms. Answers were decoded in simulated real-time using a set of predefined fNIRS channels and a univariate analysis approach. We also performed an explorative multivariate analysis on the data from all channels to investigate the differentiability of the two mental tasks based solely on spatial fNIRS signal features.

We hypothesized that relatively frontal optodes covered brain regions commonly associated with motor imagery, whereas posterior optodes covered brain areas associated with SN imagery see section fNIRS Data Acquisition. On a group level, we found that frontal optodes were selected most often, irrespective of the type of task.

However, note that a channel exclusion step was performed before the channel selection step, thus one should interpret these findings with caution. On an individual level, spatially different channels were selected as COI for each task in most participants. The absence of a spatial encoding aspect i. Our paradigm aimed at exploiting spatial as well as temporal characteristics of fNIRS signals. The incorporation of both spatial and temporal features is an experimental safeguard in the presented fNIRS paradigm.

The single-trial GLM approach, with average decoding accuracies of In the fNIRS literature, no univariate single-trial accuracies have been previously reported. Multiple trials seem to be necessary at the current time, unfortunately at the cost of a lower information transfer rate. The multi-trial GLM approach resulted in higher group decoding accuracies in comparison to the single-trial approach.

Average multi-trial decoding accuracy was higher in HbO The similar individual decoding results across HbO and HbR were an unexpected finding. In line with this, it has been demonstrated that HbO signal is more robust than HbR for motor imagery specific activation Mihara et al. Likewise, Rezazadeh Sereshkeh et al.

Despite this previous work, here we find individual HbR multi-trial decoding accuracies that are similar to the ones seen in the HbO signal. It could be that the negative effect of the low SNR of the HbR signal is compensated by the relatively low sensitivity to physiological noise, i.

In the current study we could not correct for physiological noise, which might have been a disadvantage for the HbO signal especially. Therefore, in line with Pinti et al. The multi-trial approach enabled effective communication in six participants in the HbO signal, i. When taking the HbO and HbR results together, effective communication was reached in half of our participants.

Our multi-trial accuracies of This could be due to our sparse approach of a single session. Other studies encompassed multiple sessions Rezazadeh Sereshkeh et al. More training of our participants and more experimental trials could have resulted in better BCI performance Kaiser et al. Our paradigm is the first to attempt using two active mental tasks to differentiate two answer options. This finding questions the effective contribution of the spatial navigation task in our univariate analyses.

Efforts have been made to investigate SN in naturalistic environments McKendrick et al. Future studies should investigate this mental task more thoroughly using an extended optode setup, as it is possible that our optode setup was not suited for SN.

Alternatively, other promising mental imagery tasks can be explored. The multivariate analysis explored the possibility of distinguishing the spatial patterns induced by MD vs. SN, disregarding any temporal information. From a clinical perspective, we compared the classifier results for both a limited localizer block 1 and a full localizer block 1 and 2 training set. Both our single-trial decoding accuracies, However, the limited amount of trials in the current study should be noted, whereas other studies have trained and tested their classifiers on a significantly higher number of trials.

In addition, our setup of nine optodes is quite sparse in comparison to previous work Sitaram et al. Note that the correct detection of the MD and SN tasks was more balanced, as compared to the univariate analyses. Correct detection of MD ranged from This implies effective contribution of both mental imagery tasks in our multivariate analyses.

Interestingly, in the current experiment, a simplistic majority voting approach applied on the single-trial SVM decisions, resulted in heightened accuracies of A limitation of our multivariate approach is that the two mental imagery tasks never co-occurred within localizer runs. In hindsight, it would have been better to perform both mental tasks within a run, as has been done by e.

Comparisons between the univariate and multivariate results should be drawn with caution given the fundamentally different nature of the methods. In the univariate analyses, the data from four channels-of-interest were considered, whereas all channels were considered in the multivariate analyses. Each analysis approach has its drawbacks for future BCI use, with the SVM approach requiring more measurement points and the GLM approach being dependent on a small subset of channels.

Despite similar average decoding accuracies across uni- and multivariate analyses, accuracies varied largely within an individual participant. In Figures 5 , 8 , one cannot recognize the expected hemodynamic response positive HbO deflection and negative HbR deflection or any other response in the signal of participant In addition, suboptimal channel selection due to our sparse optode setup might have contributed to these findings. Nevertheless, when looking at the multivariate results of participant 17 and 18, we see responses above chance level.

These diverging results between uni- and multivariate analyses imply that our general linear model approach, with its focus on a single channel-of-interest for each task, was not well suited to disentangle the differential spatial features of the fNIRS signal in certain participants. The inter-subject variability in our sample was substantial, both in terms of signal quality and accuracy outcomes. We have explored a few subject-specific factors that potentially influence the fNIRS signal quality and accuracy, such as hair and skin features fNIRS suitability questionnaire and subjective ease and pleasantness ratings of the mental tasks.

Note however that the fNIRS suitability questionnaire administered in the current study is an exploratory instrument and further work is needed to establish its validity and reliability. In addition, it should be noted that we used common optode holders, as opposed to spring-loaded optode holders, in the current experiment.

Common optode holders are thought to be more sensitive to signal disturbance due to hair than spring-loaded optode holders. It is thus expected that the established relationship between physical features and fNIRS signal quality will weaken in an experimental set-up with spring-loaded optode holders.

However, given the participant discomfort they often cause Lloyd-Fox et al. Ideally one would determine a suitability criterion that ensures sufficient SNR and thus enables detection of intentional brain activation. Our participants generally experienced the fNIRS setup as comfortable.

Despite the average decrease of comfortability across time, participants still felt comfortable in the last fNIRS runs and not a single participant indicated discomfort at any point. Participants considered the MD significantly easier to perform than the SN. In addition the SN task was considered less pleasant than the MD task.

Despite a clear trend, this difference did not reach significance. In line with these observations, ease and pleasantness correlated significantly with the SVM accuracies in the current study see Table 3.

In half of our participants, the paradigm did not enable effective communication. Given the general recognition of substantial inter-subject variability, the current challenge in fNIRS-based BCI research is to investigate what enables certain participants to use the BCI successfully but also what factors are hindering BCI success in other participants.

Lastly, there is notable inter-subject variability in brain activation patterns elicited by certain mental tasks Power et al. Therefore, an individualized combination of two tasks may be most effective for controlling a binary BCI in individual users. In the current study, three left-handed participants, i.

Given the established hemispheric asymmetry related to handedness Maruff et al. When excluding these three participants from our univariate analyses, single-trial accuracies rose to Multi-trial accuracies rose to The signal quality in the current data set may have been limited by our use of non-spring loaded optode holders.

Recently the use of spring loaded optode holders is on the rise, as they are known to improve signal quality. Unfortunately the type of optode holders is not systematically reported in fNIRS studies, thereby limiting systematic comparison. Nevertheless, given the discomfort they often cause Lloyd-Fox et al.

Therefore the current data might be representative for data we might encounter in patient population. It is known that the signal-to-noise ratio of fNIRS measurement remains a challenge in ecologically valid environments Zephaniah and Kim, ; Pinti et al. Our presented fNIRS suitability questionnaire should be developed further and would ideally identify those participants with an insufficient SNR before the start of the experiment. Given this information, efforts can be made to ensure good signal quality by for example tracking the optode-to-scalp coupling in real-time Pollonini et al.

Another drawback of the current study is the absence of additional physiological measures. Taking measures of blood pressure, respiration and heart rate Bauernfeind et al. Moreover, given the absence of short-separation channels in the current study, we could not remove the influence of extra-cerebral tissue changes on the fNIRS signal Brigadoi and Cooper, Methods such as the global component removal by Zhang et al. Mayer waves might thus have occurred in our dataset and have possibly reduced our decoding accuracies Yucel et al.

This might be especially the case for HbO as compared to HbR, given its higher sensitivity to physiological noise Kirilina et al. Future studies should incorporate short-separation channels, as this can result in a significant improvement in both accuracy and reliability of fNIRS measurements Brigadoi and Cooper, We advise future studies that employ a similar paradigm to focus on multi-trial decoding accuracies, as these proved most promising in our univariate analysis.

This general linear model approach using a small set of fNIRS channels has enabled effective communication in half of our participants in either HbO or HbR signal. The good HbR decoding accuracies were an unexpected finding and we thus advise future experiments to report both HbO and HbR signal outcomes.

In addition, future experiments should perform online, real-time, analysis. This would enable direct within-session feedback, which may heighten motivation in the participants and subsequently BCI performance Kleih et al. The presented binary communication paradigm aimed to exploit spatiotemporal characteristics of fNIRS-signals evoked by differently timed mental imagery tasks.

In various univariate analyses, the group average decoding accuracy was limited and did not exceed previously reported paradigms. The mental drawing imagery mainly drove our decoding results in the univariate analyses. Spatial navigation imagery should be explored more extensively in the context of fNIRS. The multivariate results showed potential spatial discernibility in a subset of participants.

Integration of the single-trial multivariate outcomes using a majority voting approach resulted in encouraging decoding accuracies. The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation, to any qualified researcher. The participants provided their written informed consent to participate in this study. NR and BS conceived and designed the study as well as obtained the data.

LN-C wrote the first draft of the manuscript. All authors contributed to manuscript revision, read and approved the submitted version. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Abdalmalak, A. Lawrence, K.

Assessing the feasibility of time-resolved fNIRS to detect brain activity during motor imagery. SPIE Can time-resolved NIRS provide the sensitivity to detect brain activity during motor imagery consistently? Express 8, — Single-session communication with a locked-in patient by functional near-infrared spectroscopy. Neurophotonics Balardin, J. Imaging brain function with functional near-infrared spectroscopy in unconstrained environments.

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Improving optical contact for functional near-infrared brain spectroscopy and imaging with brush optodes. Express 3, — Kirilina, E. The physiological origin of task-evoked systemic artefacts in functional near infrared spectroscopy. Neuroimage 61, 70— Kleih, S. Motivation modulates the P amplitude during brain—computer interface use.

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Kovalenko, B. Trauma Treat. Kubler, A. BCI meeting workshop on clinical issues and applications. IEEE Trans. Neural Syst. Laureys, S. The neural correlate of un awareness: lessons from the vegetative state. Trends Cogn. Lee, S. The difference in cortical activation pattern for complex motor skills: a functional near-infrared spectroscopy study. Leff, D. Assessment of the cerebral cortex during motor task behaviours in adults: a systematic review of functional near infrared spectroscopy fNIRS studies.

Bright Rijeka: InTech. Leuthardt, E. A brain—computer interface using electrocorticographic signals in humans. Lloyd-Fox, S. Illuminating the developing brain: the past, present and future of functional near infrared spectroscopy. Maruff, P. Asymmetries between dominant and non-dominanthands in real and imagined motor task performance. Neuropsychologia 37, — McKendrick, R. Into the wild: neuroergonomic differentiation of hand-held and augmented reality wearable displays during outdoor navigation with functional near infrared spectroscopy.

Mellinger, J. Neuroimage 36, — Mihara, M. Neurofeedback using real-time near-infrared spectroscopy enhances motor imagery related cortical activation. PLoS One 7:e Monti, M. Neuroimaging and the vegetative state: resolving the behavioral assessment dilemma? Willful modulation of brain activity in disorders of consciousness.

Naci, L. Making every word count for nonresponsive patients. JAMA Neurol. Nagels-Coune, L. Yes or no? Binary brain-based communiction utilizing motor imagery and fNIRS. Naito, M. A communication means for totally locked-in ALS patients based on changes in cerebral blood volume measured with near-infrared light. ED, — NIIF as a potential major financier can be a game changer. Historical rates Currencies constantly move up and down against each other as financial markets change.

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