As-measured Traces

Both static and adaptive cases used knowledge of the task for production of the functional traces used as input classifier features. The as-measured traces, as introduced in section 2.1.4, produced the best classification accuracy of all cases for which no information about the task being performed is used. As presented in Table 3, this result was 70.2% +/- 11.3% when averaged across all seven participants. This processing method achieved significance for the functional feature type only (t(6) = 6.978, p<0.0005). The accuracy for each participant is presented in Figure 15.

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Table 3. State prediction accuracy for various fNIRS processing methods and features Accuracy was averaged across seven participants. The number of classifier features is shown in square brackets. Those significantly greater than chance prediction are marked with daggers.

feature type: functional [6] PCA [6] MMSE [6] RWCC [6] processing method: % accuracy SD % accuracy SD % accuracy SD % accuracy SD as-measured * 70.2 †4 11.3 62.6 11.1 56.1 8.3 - - static 71.8 †4 14.8 65.1 †1 12.6 54.1 3.4 52.3 2.9 physiological * 65.1 †3 12.3 - - - - adaptive 83.8 †4 5.7 79.9 †4 5.8 77.0 †4 7.2 56.0 †4 2.4 pco static * 64.7 †3 7.4 56.1 7.1 52.5 2.4 - - pco adaptive * 62.0 †1 9.5 59.2 8.8 59.9 11.0 - - * no task knowledge used pco: physiology-cleaned only

†1 p<0.01 †2 p<0.001 †3 p<0.005 †4 p<0.0005

Table 4. Statistical significance of accuracy for fNIRS classification results

Accuracy was averaged across seven participants. One-tailed paired t-tests were used. The number of classifier input features is shown in square brackets.

feature type: functional [6] PCA [6] MMSE [6] RWCC [6] processing method: t(6) p < t(6) p < t(6) p < t(6) p < as-measured * 6.978 0.0005 3.006 0.025 1.928 0.1 - - static 6.109 0.0005 3.190 0.01 3.124 0.025 2.102 0.05 physiological * 3.963 0.005 - - - - adaptive 20.584 0.0005 13.536 0.0005 9.936 0.0005 6.526 0.0005 pco static * 5.231 0.005 2.275 0.05 2.751 0.025 - - pco adaptive * 3.317 0.01 2.760 0.025 2.382 0.025 - -

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Table 5. SVM parameters for fNIRS classification for functional and physiological features Parameters are shown for various fNIRS processing methods for each of the seven participants.

participant processing method:

as-

measured static physiological adaptive

pco static pco adaptive 15 c 1 1 0.01 1 0.1 1 g 0.1 0.5 0.1 0.5 0.1 0.5 16 c 0.01 0.001 0.001 0.01 0.01 0.001 g 0.1 0.1 0.1 0.1 0.1 0.1 17 c 0.01 0.1 0.01 0.01 0.1 1 g 0.5 0.1 0.1 0.5 0.5 0.1 18 c 0.01 0.01 0.1 0.1 0.001 1 g 0.1 0.1 0.1 0.5 0.5 0.5 19 c 0.01 0.01 0.1 0.1 0.001 0.001 g 0.1 0.1 0.5 0.5 0.1 0.1 20 c 0.01 0.1 0.001 0.1 0.1 0.01 g 0.1 0.1 0.1 0.5 0.1 0.1 21 c 0.001 0.1 1 0.001 0.001 0.01 g 0.5 0.1 0.5 0.5 0.5 0.1

pco: physiology-cleaned only

4.4.2 Static Regression

The functional traces produced via static regression, as described in section 4.3.2 and as presented in Table 3, generated classification accuracy of 71.8% +/- 14.8% when averaged across all seven participants. Significance was achieved (t(6) = 6.109, p<0.0005), but with an increase of less than 2% over the as-measured case. The accuracy for each participant is presented in Figure 15. Exemplary plots of prediction data and the corresponding classifier outputs at every time instance for one run are presented in Figure 16 and Figure 17.

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Figure 15. State prediction accuracy for various fNIRS processing methods Accuracy is shown for cases using functional traces as features, for seven participants.

Processing methods which did not use knowledge of the task were the as-measured (of section 4.4.1), and physiological cases (of section 4.4.3).

Figure 16. Prediction data and classifier outputs for one run, static case (a)

Prediction accuracy was 74.8% for this run, and three training runs were used. In the top panel, black markers at +1 indicate a classifier output of a more engaged or ‘working’ state, while those at -1 indicate a less engaged or ‘resting’ state. A prediction is made at every instance. The dotted black line indicates the probability estimates of the classifier predictions. In the bottom panel, the six classifier input features are plotted. Truth labels are indicated by the green task indicator function in both the top and bottom panels.

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Figure 17. Prediction data and classifier outputs for one run, static case (b)

Prediction accuracy was 50.9% for this run, and three training runs were used. In the top panel, black markers at +1 indicate a classifier output of a more engaged or ‘working’ state, while those at -1 indicate a less engaged or ‘resting’ state. A prediction is made at every instance. The dotted black line indicates the probability estimates of the classifier predictions. In the bottom panel, the six classifier input features are plotted. Truth labels are indicated by the green task indicator function in both the top and bottom panels.

We hypothesized that both the beta fit parameters for the two shallow physiological regressors would be relatively high independent of the species of the measured trace being cleaned. Regression was not performed on the shallow trace channels themselves. The beta fit parameters were greater than zero for both species of the shallow nuisance regressors. These values are shown in Figure 18.

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Figure 18. Beta fit parameters for the static regression fNIRS processing

Parameters are given for the two regions, two Hb species, six optode channels and four runs, after averaging across seven participants. DLPFC channel 4 and MFG channel 2 were used for the shallow traces. Regression was not performed on the shallow trace channels.

4.4.3 Physiological Traces as Classifier Input Features

Task-evoked effects in the superficial tissue are clearly real and have been reported elsewhere (Kirilina 2012), but were not found to be reliable across participants. This case, as described in section 4.3.3, generated classification accuracy of 65.1% +/- 12.3% when averaged across all seven participants. This result was significantly greater than chance accuracy

(t(6)=3.963, p<0.005). The accuracy for each participant is presented in Figure 15. The best c and g SVM parameters are given in Table 5.

These physiological traces were produced using no information about the task being performed, and this case represents the second-best accuracy for cases where no task knowledge was used. For one outlying participant, classification accuracy averaged 86.5% +/- 1.3%. The four [Hb] traces and output state predictions at every time point for run 1 are shown in Figure 19.

DLPFC [HbO]

MFG [HbO] MFG [HbR]

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Figure 19. Four shallow physiological traces used as classifier input features

Prediction accuracy was 84.5% for this run, and three training runs were used. Traces for two Hb species per region are shown. In the top panel, black markers at +1 indicate a classifier output of a more engaged or ‘working’ state, while those at -1 indicate a less engaged or ‘resting’ state. A prediction is made at every instance. The dotted black line indicates the probability estimates of the classifier predictions. In the bottom panel, the four classifier input features are plotted (red: DLPFC [HbO], orange: MFG [HbO], green: DLPFC [HbR], blue: MFG [HbR]). Truth labels are indicated by the green task indicator function in both the top and bottom panels.