1 Introduction
1.1 Single-trial classification of fNIRS data
1.2 Motor imagery as mental task
2 Materials and methods
2.1 Subjects
2.2 Experimental protocol
2.2.1 Motor imagery (MI) tasks
-
MI-simple: subjects were asked to imagine a simple finger-tapping task by repetitively pressing button 'zero' (0) of a number keyboard using their thumb of the right hand with a frequency of approximately 3 Hz. The start of the trial was indicated by a visual stimulus 'GO - 0' on the screen.
-
MI-complex: subjects were asked to imagine a complex sequential finger-tapping task by repetitively pressing a predefined sequence on the keyboard using all fingers of their right hand with the same frequency as in MI-simple. The sequence was presented at the start of the trial on the screen: e.g. 'GO - 2-2-5-3-4'. The number stimuli symbolized the numbered fingers of a hand, 1 = thumb, 2 = index finger, 3 = middle finger, 4 = ring finger and 5 = little finger. For example, the sequence 2-2-5-3-4 indicated the following task: index finger twice, little finger once, middle finger once, and ring finger once. Five sequences of similar complexity were presented in a randomized order each comprising five tapping acts. This task is similar to that used in various fMRI studies of stroke and stroke recovery [18‐21].
2.2.2 Control motor execution (ME) measurements
-
ME-simple: same as MI-simple, but subjects were asked to actually perform the simple task by pressing button 'zero' (0) on the keyboard repetitively using their thumb over the whole stimulation phase with a frequency of approximately 3 Hz.
-
ME-complex: same as MI-simple, but subjects were asked to actually perform the complex task by pressing five buttons on the keyboard using all fingers in the same predefined sequences and frequency as presented in MI-complex.
2.3 fNIRS measurements
2.4 EMG measurements
3 Data analysis
3.1 Data pre-processing
3.2 Single-trial classification of MI signals
-
○ Mean: average signal amplitude.
-
○ Variance: measure of signal spread.
-
○ Skewness: measure of the asymmetry of signal values around its mean relative to a normal distribution.
-
○ Kurtosis: measure of the degree of peakedness of a distribution of signal values relative to a normal distribution.
4 Results
4.1 Control ME measurements
4.2 MI tasks
Mean Δ[O2Hb] Δ[HHb] Overall-subjects | Channel 1 | Channel 2 | Channel 3 | Overall channels |
---|---|---|---|---|
MI-simple
| ||||
Δ[O2Hb] μmol/l | 0.101 ± 0.013 | 0.054 ± 0.014 | 0.038 ± 0.011 | 0.064 ± 0.012 |
Δ[HHb] μmol/l | -0.017 ± 0.002 | -0.0130 ± 0.003 | -0.011 ± 0.003 | -0.014 ± 0.003 |
Δ[O2Hb] SNR | 0.99 | 0.54 | 1.08 | 0.87 |
MI-complex
| ||||
Δ[O2Hb] μmol/l | 0.192 ± 0.012 | 0.095 ± 0.012 | 0.065 ± 0.010 | 0.118 ± 0.011 |
Δ[HHb] μmol/l | -0.008 ± 0.006 | -0.010 ± 0.003 | -0.007 ± 0.003 | -0.009 ± 0.003 |
Δ[O2Hb] SNR | 1.04 | 1.23 | 1.55 | 1.27 |
Inter-task paired t-test [simple vs complex]
|
Channel 1
|
Channel 2
|
Channel 3
|
Overall channels
|
Δ[O2Hb] (p-values) | Δ[O2Hb] (p-values) | Δ[O2Hb] (p-values) | Δ[O2Hb] (p-values) | |
Overall-subjects
| ≤ 0.001* | 0.018* | 0.064 | ≤ 0.001* |
Subject 1
| ≤ 0.001* | ≤ 0.001* | ≤ 0.001* | ≤ 0.001* |
Subject 2
| 0.341 | ≤ 0.001* | ≤ 0.001* | ≤ 0.001* |
Subject 3
| 1.000 | 0.003* | ≤ 0.001* | 0.032* |
Subject 4
| 1.000 | ≤ 0.001* | ≤ 0.001* | ≤ 0.001* |
Subject 5
| ≤ 0.001* | ≤ 0.001* | ≤ 0.001* | ≤ 0.001* |
Subject 6
| 0.105 | 0.007* | 0.002* | 0.046* |
Subject 7
| ≤ 0.001* | 0.023* | ≤ 0.001* | ≤ 0.001* |
Subject 8
| 0.086 | ≤ 0.001* | 0.004* | 0.002* |
Subject 9
| ≤ 0.001* | ≤ 0.001* | ≤ 0.001* | ≤ 0.001* |
Subject 10
| 0.976 | ≤ 0.001* | ≤ 0.001* | ≤ 0.001* |
Subject 11
| 0.181 | ≤ 0.001* | ≤ 0.001* | ≤ 0.001* |
Subject 12
| 0.324 | 0.026* | ≤ 0.001* | 0.039* |
4.3 Classification of MI signals
Best-performing combination | ||||
---|---|---|---|---|
Subject No. | Channel | Time interval | Optimal feature set | Classification accuracy |
1 | 3 | 9-15 s | Δ[O2Hb] mean, variance, skewness, kurtosis | 91.7% |
2 | 2 | 5-15 s | Δ[O2Hb] mean, variance | 79.2% |
3 | 3 | 9-15 s | Δ[O2Hb] variance, skewness, kurtosis | 79.2% |
4 | 2 | 8-14 s | Δ[O2Hb] mean, variance | 75.0% |
5 | 3 | 9-15 s | Δ[O2Hb] mean | 75.0% |
6 | 3 | 7-15 s | Δ[O2Hb] mean, variance, skewness | 91.7% |
7 | 1 | 8-14 s | Δ[O2Hb] skewness | 70.8% |
8 | 2 | 7-12 s | Δ[O2Hb] mean, variance | 75.0% |
9 | 1 | 5-15 s | Δ[O2Hb] mean, variance | 83.3% |
10 | 3 | 5-15 s | Δ[O2Hb]variance, skewness, kurtosis | 87.5% |
11 | 3 | 7-15 s | Δ[O2Hb]variance, kurtosis | 87.5% |
12 | 2 | 11-15 s | Δ[O2Hb] mean, variance, skewness, kurtosis | 79.2% |
Overall
|
81.3 ± 7.0%
|
5 Discussion
5.1 Channels selected for classification
5.2 Analysis time intervals selected for classification
5.3 Δ[O2Hb] signal features selected for classification
-
Δ[O2Hb] variance (N = 10 (83%)): This feature was selected most frequently indicating that our data contained a large variation in variance between individual signals and between the two task conditions, MI-simple and MI-complex. However, the value of the variance within an individual signal was relatively stable from trial-to-trial, therefore serving a suitable feature for discrimination between the two tasks. Overall subjects, the averaged value of Δ[O2Hb] variance revealed a significant negative correlation with the classification accuracies in both conditions, i.e. classification rates improved with decreasing variance (MI-simple: r = -0.688*, p = 0.028; MI-complex: r = -0.701*, p = 0.024) (Figure 6). This finding is in line with the tendency that has been observed for the selection of channels (section 5.1), i.e. channels with larger SNR (in particular channel 3) revealed higher classification accuracies.
-
Δ[O2Hb] mean amplitude (N = 8 (66%)): The mean amplitude as feature reflected those individual time intervals in which both a significant increase within a given condition and a significant difference between the two conditions was found. As shown by the previous studies the mean amplitude is a reliable feature selected for classification, in particular for classification of two different conditions as in our case. In our study, as again discussed for the selection of channels (section 5.1), there was a slight tendency that smaller mean amplitudes did reveal higher classification accuracies, but no significant correlations were found.
-
Δ[O2Hb] skewness (N = 6 (12%)): Classification rates also improved in relation to skewness. However, the relationship differed between the two conditions. Skewness of signals in response to MI-simple were negatively correlated with increasing accuracy (r = -0.850*, p = 0.032), i.e. the smaller the value of the skewness the higher the accuracy of classification in a given subject. In contrast, in MI-complex a positive correlation was observed (r = 0.854*, p = 0.031), i.e. the higher the skewness the higher the accuracy of classification in a given subject (Figure 6). This finding may reflect differences in the shape of the signal between the simple and the complex imagery task. While in response to the simple task, higher accuracies may have favoured a slower signal increase, i.e. the tail on the left side of the probability density function was longer than the right side and the bulk of the values was located to the right of the peak; contrary, in response to the complex task a faster signal increase may have been favoured reflected by a positive skewness, i.e. the tail on the right side was longer than on the left side.
-
Δ[O2Hb] kurtosis (N = 5 (41%)): The last feature was selected only in a few subjects, but was relevant in these to achieve the reported classification accuracies. No correlations were found with the classification accuracy.