5.3 Case Two: Classification with brain sources
5.3.2 Classification
MLE
Figure 5.4: Studio three: Case two using sources estimated with MLE: Accuracy for each participant (left). Empirical chance level (right).
The classification results using features from the brain sources estimated using MLE are shown in fig. 5.4. The mean accuracy for the no movement class was of 98.82 ± 1.37%, for the movement intention class was of 99.25 ± 0.88% and the global mean accuracy was of 99.03 ± 0.77%. From the accuracy boxplot (left) it is easy to appreciate the high accuracy obtained with this approach. For every participant the accuracy was higher than the 90%, and for some it was even obtained of almost 100%. By observing the empirical chance level test (right) it can be observed that for participant 11 there was a sign that the test did not result in randomness by obtaining a classification accuracy higher than 60%. Therefore, the classification results for participant 11 can be reduced to approximately around the 90%.
In previous studies and cases of this thesis, the MLE method had yielded good results with the only problem being the possibility of finding a singular matrix. For the next SLMs similar results are expected.
MNE
The classification results when using features from the brain sources estimated with MNE are shown in fig. 5.5. The mean accuracy for each class was of 98.67 ± 1.57% for the no movement class, 99.18 ± 1.18% for the movement intention class and a global mean accuracy of 98.92 ± 0.93%. This method, just as MLE, has yielded great results in the classification of movement intention with real EEG data. This time, however, the empirical chance level test (right) for participant 11 resulted in randomness. The classification accuracy (left) yielded fewer participants obtaining 100% than before, which in a technical way makes more sense.
Figure 5.5: Studio three: Case two using sources estimated with MNE: Accuracy for each participant (left). Empirical chance level (right).
wMNE
Figure 5.6: Studio three: Case two using sources estimated with wMNE: Accuracy for each participant (left). Empirical chance level (right).
The classification results are shown in fig. 5.6. The mean accuracy for the no movement class was of 99.19 ± 1.39%. For the movement intention class was obtained a mean accuracy of 99.47 ± 0.89%. The global mean accuracy obtained was of 99.33 ± 0.83%. This time the classification accuracy obtained for each participant was closer to the 100% than before.
A comparison of each of these 5 sub-cases is shown in table 5.1. It was expected to achieve a greater classification accuracy using features from the brain sources than just using features from the EEG. However, it was not expected to achieve such great accuracies so close
to the 100%. One could say they are too good to be true. Still, the empirical chance level is there to help determine the randomness level of the classifier and to help determine how high the precision is over the chance level. All the results obtained in this third study point to the improvement obtained when using brain sources for class classification over the use of the EEG alone.
Study 3: Results
Features Class 0 Accuracy Class 1 Accuracy Global Accuracy EEG (time-domain) 50.03 ± 50.02% 49.86 ± 50% 46.21 ± 2.9%
EEG (frequency domain 55.16 ± 21.85% 60.62 ± 22.02% 56.16 ± 6.67%
MLE sources 98.82 ± 1.37% 99.25 ± 0.88% 99.03 ± 0.77%
MNE sources 98.67 ± 1.57% 99.18 ± 1.18% 98.92 ± 0.93%
wMNE sources 99.19 ± 1.39% 99.47 ± 0.89% 99.33 ± 0.83%
Table 5.1: Study 3: Classification results. Using the sources estimated with wMNE yielded the best classification results.
Conclusion
In this thesis, EEG-SLMs for the decoding of motor information were studied. EEG-SL is mostly used in neuro-prosthesis and it is believed that is a promising technique that may be able to help this kind of prosthesis to make the next jump in classification a multitude of conditions with more degrees of freedom. This work was divided into three studies focused in different aspects of source localization of the EEG. The first study was carried out in a simulated environment where the behavior of the EEG-SLMs was observed using synthetic EEG signals. This study confirmed the behavior of the methods and helped lay the foundation for further studies.
The second study evaluated the performance of the EEG-SLMs when using real EEG signals which were taken from an experiment with motor tasks. The experiment from which the EEG was taken consisted of moving either the right or left arm to perform one of two actions: reaching for and lifting a cup or reaching and pressing a button on the cup. Partic-ipants could choose at what time to start the movement and also which arm to move when the action to be performed was specified or choose which action to do when it was specified which arm to use. With this study several of the most representative brain areas involved in the performance of motor tasks were found in the estimations. Areas such as BAs 4 and 6 that are responsible for movement were present in the SL, as well as areas responsible for locating objects in space such as BAs 1, 2, 3, 5 and 7. This second study demonstrated the great ability of the EEG-SLM to find active brain areas. Other areas of great importance given the activities of the experiment from which the EEG was taken were BAs 8, responsible for planning complex movements; BAs 9 and 10, involved in cognitive processes; BA 11, in-volved in decision-making; and BAs 21, 22, 40, 44, 45 and 47, inin-volved in the contemplation of distances. Despite finding areas that were not directly linked to movement, such as BAs 39 and 46 that are related to memory and attention, these areas are related to the development of the experiment. The results obtained were highly significant for the third study.
The third and last study consisted of evaluating the performance of a support vector machine (SVM) in two cases: the first using features extracted directly from the EEG, and the second using features extracted from the neural sources responsible for generating the EEG which were estimated using one of the chosen SLMs. The first case performed classification using features from the EEG from the time-domain and from the frequency-domain. It is well known that it is not possible to classify using features from the time-domain of the EEG, however, in this study it helped to pave the procedure that it would be used in the entire study.
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The results obtained were, as expected, not better than chance, obtaining a mean accuracy of 46.21 ± 2.9%. When using features extracted from the frequency-domain of the EEG the results reached acceptable levels obtaining a mean accuracy of 56.16 ± 6.67%. The second case were carried out using features extracted from the neural sources estimated with the EEG-SLMs MLE, MNE and wMNE. The results obtained exceeded expectations, with mean accuracies of 99.03 ± 0.77%, 98.92 ± 0.93% and 99.33 ± 0.83% respectively.
EEG-SLMs demonstrated their ability to estimate satisfactorily the neuronal sources responsible for the generation of the EEG both in a simulated environment under different circumstances and with real EEG signals. They proved to be a powerful tool that replicated very closely the EEG and estimated the responsible brain activity with great fidelity. Also, they were shown to be able to give a great deal of information about brain activity during real motor tasks. Finally, it seems that the information obtained with these techniques is able to help machine learning algorithms easily differentiate between different classes. Time did not allow further exploration to explain why the latest results were so good, so further research in this area will be carried out in future work. However, based on the results of the other two studies, using EEG-SLMs for the study and decoding of motor information shows to be a promising area of study.
Studio One: Figures and tables
Figure A.1: Studio one: Case one: From left to right the value for α goes α = 0.0001 for the left column, α = 0.1 for the middle column and α = 1 for the bottom column. (Top) EEG calculated using the estimated dipoles using MNE. (Bottom) EEG calculated using the estimated dipoles using wMNE.
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Figure A.2: Studio one: Case two: Simulation of the brain activity (top row) for the scenarios with r = 3.5 cm (left), 4.5 cm (middle) and r = 4.5 cm (right). EEG produces by the simulated dipoles (bottom row). Radius of activity increasing from left to right.
Figure A.3: Studio one: Case two: SL using MLE for the scenarios with r = 3.5 cm (left), 4.5 cm (middle) and r = 4.5 cm (right). Estimated brain activity (top row). Corresponding EEG (bottom row). Radius of activity increasing from left to right.
Figure A.4: Studio one: Case two: SL using MNE for the scenarios with r = 3.5 cm (left), 4.5 cm (middle) and r = 4.5 cm (right). Estimated brain activity (top row). Corresponding EEG (bottom row). Radius of activity increasing from left to right.
Figure A.5: Studio one: Case two: SL using wMNE for the scenarios with r = 3.5 cm (left), 4.5 cm (middle) and r = 4.5 cm (right). Estimated brain activity (top row). Corresponding EEG (bottom row). Radius of activity increasing from left to right.
Figure A.6: Studio one: Case two: EEGs for the lowest radius of activity (top row) and the no boundaries (bottom row) scenarios. MLE (left). MNE (middle). wMNE (right).
Figure A.7: Studio one: Case three: EEGs for the scenario with the source in the left hemi-sphere. EEG of the SL using MLE (left), MNE (middle) and wMNE (right).
Figure A.8: Studio one: Case three: Real activity in the right hemisphere. The max dipole was chosen to be at a random position in the right hemisphere of the BA 6 (left). The EEG produced by the given dipole configuration (right).
Figure A.9: Studio one: Case three: Source in the right hemisphere. EEG-SL using MLE (left), MNE (middle) and wMNE (right).
Figure A.10: Studio one: Case three: EEGs for the scenario with the source in the right hemisphere. EEG of the SL using MLE (left), MNE (middle) and wMNE (right).
Qualitative metrics
Real MLE MNE wMNE
Hemisphere and BA:
6 right
6 right 6 right 6 right
Grid position (mm):
[40 0 50]
[55 0 50] [55 0 50] [50 -5 50]
Dipole momentum (mA): [57.74 57.74 57.74]
[15.14 13.84 35.08] [15.14 13.84 35.08] [17.61 18.66 24.04]
Dipole magnitude (mA): 100
40.64 40.64 35.16
Table A.1: Studio one: Case three: Qualitative metrics for the scenario with the source in the right hemisphere.
Quantitative metrics
Metric MLE MNE wMNE
(rmax)[mm] 15 15 12.2474
(drmax)[mA] 65.2231 65.2247 65.3658
(||drmax||)[%] 59.3614 59.3639 64.8428
(dI)[mA] -20.7694 -20.769 -26.8562
(||d||)[%] 2.7677 2.7677 3.1289
G[mV ] 4.57e-10 0.018 0.004
Table A.2: Studio one: Case three: Quantitative metrics for the scenario with the source in the right hemisphere.
Figure A.11: Studio one: Case four: EEG of the SL using MLE (left), MNE (middle) and wMNE (right).
Qualitative metrics
Second source Third source Fourth source
Hemisphere and BA 7 left 4 right 30 right
Grid position (mm) [-35 -60 50] [65 -10 30] [10 -40 0]
Dipole momentum (mA)
[47.84 47.84 47.84]
[35.96 35.96 35.96]
[30.68 30.68 30.68]
Dipole magnitude (mA)
82.86 62.28 53.14
Table A.3: Studio one: Case four: Qualitative metrics for the three additional sources.
Figure A.12: Studio one: Case five: EEG-SL with a low SNR. The top corresponds to a SN R = −3, the middle row to a SN R = −2 and the bottom to a SN R = −1. The left column shows the dipole estimation in each case with MLE, the middle column shows the estimation with MNE and the right column shows the estimation with wMNE.
Figure A.13: Studio one: Case five: EEG-SL with a low SNR. The top corresponds to a SN R = −3, the middle row to a SN R = −2 and the bottom to a SN R = −1. The left column shows the EEG in each case produced by the estimation of MLE, the middle column shows the EEG for MNE and the right column shows the EEG for wMNE.
Figure A.14: Studio one: Case five: EEG-SL with a high SNR. The top corresponds to a SN R = 1, the middle to a SN R = 2 and the bottom row to a SN R = 3. The left column shows the dipole estimation in each case with MLE, the middle column shows the estimation with MNE and the right column shows the estimation with wMNE.
Figure A.15: Studio one: Case five: EEG resulting from the SL with a high SNR. The top corresponds to a SN R = 1, the middle row to a SN R = 2and the bottom row to a SN R = 3.
The left column shows the dipole estimation in each case with MLE, the middle column shows the estimation with MNE and the right column shows the estimation with wMNE.
Figure A.16: Studio one: Case six: Electrode setup for each scenario.
Figure A.17: Studio one: Case six: SL for the scenarios with 8 electrodes (top row), 16 electrodes (second row), 24 electrodes (third row) and 32 electrodes (bottom row). We used MLE for the left column, MNE for the middle column and wMNE for the right column.
Figure A.18: Studio one: Case six: EEG for the scenarios with 8 electrodes (top row), 16 electrodes (second row), 24 electrodes (third row) and 32 electrodes (bottom row). We used MLE for the left column, MNE for the middle column and wMNE for the right column.
Figure A.19: Studio one: Case six: SL for the scenarios with 40 electrodes (top row), 48 electrodes (second row), 56 electrodes (third row) and 62 electrodes (bottom row). We used MLE for the left column, MNE for the middle column and wMNE for the right column.
Figure A.20: Studio one: Case six: EEG for the scenarios with 40 electrodes (top row), 48 electrodes (second row), 56 electrodes (third row) and 62 electrodes (bottom row). We used MLE for the left column, MNE for the middle column and wMNE for the right column.
Studio Two: Figures and tables
Left Arm Movement
Time -2 -1.5 -1 -0.5 0 0.5 1 1.5 2
sLORETA BA 7 38 10 11 11 7 47 6 47
MLE BA 22 47 22 44 19 22 47 47 22
MNE BA 22 47 22 44 19 22 47 47 22
wMNE BA 21 45 21 21 22 22 47 45 22
Right Arm Movement
Time -2 -1.5 -1 -0.5 0 0.5 1 1.5 2
sLORETA BA 11 7 7 6 39 18 10 38 6
MLE BA 22 10 22 47 7 47 47 47 47
MNE BA 22 10 22 47 7 47 47 47 47
wMNE BA 42 10 22 45 7 45 45 45 45
Table B.1: Study two: Case one: Brodmann areas located with source localization for partic-ipant 2.
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Left Arm Movement
Time -2 -1.5 -1 -0.5 0 0.5 1 1.5 2
sLORETA BA 21 20 19 7 38 38 38 7 11
MLE BA 6 6 47 47 44 47 6 47 47
MNE BA 6 6 47 47 44 47 6 47 47
wMNE BA 6 6 47 45 21 45 6 47 47
Right Arm Movement
Time -2 -1.5 -1 -0.5 0 0.5 1 1.5 2
sLORETA BA 20 20 20 6 38 38 10 40 20
MLE BA 47 47 47 47 47 47 47 40 6
MNE BA 47 47 47 47 47 47 47 40 6
wMNE BA 47 47 47 47 45 47 45 40 6
Table B.2: Study two: Case one: Brodmann areas located with source localization for partic-ipant 3.
Left Arm Movement
Time -2 -1.5 -1 -0.5 0 0.5 1 1.5 2
sLORETA BA 20 38 6 11 4 38 38 11 38
MLE BA 47 22 19 47 22 47 22 22 47
MNE BA 47 22 19 47 22 47 22 22 47
wMNE BA 45 22 22 45 22 45 22 22 20
Right Arm Movement
Time -2 -1.5 -1 -0.5 0 0.5 1 1.5 2
sLORETA BA 7 38 38 20 7 38 20 20 4
MLE BA 22 47 6 22 47 47 22 47 22
MNE BA 22 47 6 22 47 47 22 47 22
wMNE BA 22 45 6 22 45 45 22 45 22
Table B.3: Study two: Case one: Brodmann areas located with source localization for partic-ipant 4.
Left Arm Movement
Time -2 -1.5 -1 -0.5 0 0.5 1 1.5 2
sLORETA BA 19 7 47 7 2 7 7 19 4
MLE BA 22 22 47 47 47 47 47 47 22
MNE BA 22 22 47 47 47 47 47 47 22
wMNE BA 45 22 45 45 45 45 45 45 22
Right Arm Movement
Time -2 -1.5 -1 -0.5 0 0.5 1 1.5 2
sLORETA BA 47 10 19 21 3 11 7 11 11
MLE BA 22 7 47 47 46 47 47 47 22
MNE BA 22 7 47 47 46 47 47 47 22
wMNE BA 42 8 45 45 45 45 45 45 22
Table B.4: Study two: Case one: Brodmann areas located with source localization for partic-ipant 5.
Left Arm Movement
Time -2 -1.5 -1 -0.5 0 0.5 1 1.5 2
sLORETA BA 38 38 47 38 38 38 4 6 19
MLE BA 19 47 21 47 47 47 47 47 47
MNE BA 19 47 21 47 47 47 47 47 47
wMNE BA 20 47 20 47 47 47 47 47 47
Right Arm Movement
Time -2 -1.5 -1 -0.5 0 0.5 1 1.5 2
sLORETA BA 38 8 20 20 38 38 38 38 38
MLE BA 47 47 47 47 47 47 47 47 47
MNE BA 47 47 47 47 47 47 47 47 47
wMNE BA 47 47 47 47 47 47 47 47 47
Table B.5: Study two: Case one: Brodmann areas located with source localization for partic-ipant 7.
Left Arm Movement
Time -2 -1.5 -1 -0.5 0 0.5 1 1.5 2
sLORETA BA 4 38 7 10 20 47 10 38 38
MLE BA 6 47 9 47 39 47 47 47 46
MNE BA 6 47 9 47 39 47 47 47 46
wMNE BA 6 47 44 45 39 45 45 45 45
Right Arm Movement
Time -2 -1.5 -1 -0.5 0 0.5 1 1.5 2
sLORETA BA 11 10 11 38 10 10 20 6 38
MLE BA 47 47 47 46 8 47 46 47 47
MNE BA 47 47 47 46 8 47 46 47 47
wMNE BA 47 47 20 45 9 47 45 45 45
Table B.6: Study two: Case one: Brodmann areas located with source localization for partic-ipant 8.
Left Arm Movement
Time -2 -1.5 -1 -0.5 0 0.5 1 1.5 2
sLORETA BA 10 10 10 9 10 10 10 10 21
MLE BA 9 9 9 22 21 9 9 9 21
MNE BA 9 9 9 22 21 9 9 9 21
wMNE BA 9 9 9 9 21 9 9 9 21
Right Arm Movement
Time -2 -1.5 -1 -0.5 0 0.5 1 1.5 2
sLORETA BA 10 10 18 10 18 18 18 18 18
MLE BA 9 9 9 9 18 18 18 9 9
MNE BA 9 9 9 9 18 18 18 9 9
wMNE BA 9 9 9 9 18 18 18 9 9
Table B.7: Study two: Case one: Brodmann areas located with source localization for partic-ipant 9.
Left Arm Movement
Time -2 -1.5 -1 -0.5 0 0.5 1 1.5 2
sLORETA BA 10 10 10 10 10 10 10 10 10
MLE BA 10 10 10 10 10 10 10 10 10
MNE BA 10 10 10 10 10 10 10 10 10
wMNE BA 10 10 10 10 10 10 10 10 10
Right Arm Movement
Time -2 -1.5 -1 -0.5 0 0.5 1 1.5 2
sLORETA BA 10 10 10 19 10 10 10 18 10
MLE BA 10 10 10 19 10 10 10 22 10
MNE BA 10 10 10 19 10 10 10 22 10
wMNE BA 10 10 10 19 10 10 10 22 10
Table B.8: Study two: Case one: Brodmann areas located with source localization for partic-ipant 10.
Left Arm Movement
Time -2 -1.5 -1 -0.5 0 0.5 1 1.5 2
sLORETA BA 10 10 10 10 10 10 10 10 10
MLE BA 9 9 9 9 9 9 9 9 9
MNE BA 9 9 9 9 9 9 9 9 9
wMNE BA 9 9 9 9 9 9 9 9 9
Right Arm Movement
Time -2 -1.5 -1 -0.5 0 0.5 1 1.5 2
sLORETA BA 10 10 10 10 10 10 10 10 10
MLE BA 9 9 9 9 9 9 9 9 9
MNE BA 9 9 9 9 9 9 9 9 9
wMNE BA 9 9 9 9 9 9 9 9 9
Table B.9: Study two: Case one: Brodmann areas located with source localization for partic-ipant 12.
Left Arm Movement
Time -2 -1.5 -1 -0.5 0 0.5 1 1.5 2
sLORETA BA 10 10 10 7 10 10 10 10 10
MLE BA 10 10 10 7 22 10 10 10 10
MNE BA 10 10 10 7 22 10 10 10 10
wMNE BA 10 7 10 7 22 10 10 10 10
Right Arm Movement
Time -2 -1.5 -1 -0.5 0 0.5 1 1.5 2
sLORETA BA 10 11 7 7 7 7 7 7 10
MLE BA 40 47 7 7 7 7 7 7 10
MNE BA 40 47 7 7 7 7 7 7 10
wMNE BA 40 45 7 7 7 7 7 7 10
Table B.10: Study two: Case one: Brodmann areas located with source localization for par-ticipant 13.
Left Arm Movement
Time -2 -1.5 -1 -0.5 0 0.5 1 1.5 2
sLORETA BA 8 8 8 39 8 8 8 8 39
MLE BA 8 39 39 39 8 8 8 8 39
MNE BA 8 39 39 39 8 8 8 8 39
wMNE BA 6 39 6 39 6 6 6 6 39
Right Arm Movement
Time -2 -1.5 -1 -0.5 0 0.5 1 1.5 2
sLORETA BA 8 8 39 39 39 39 8 39 8
MLE BA 8 8 39 39 39 39 39 39 8
MNE BA 8 8 39 39 39 39 39 39 8
wMNE BA 6 6 39 39 39 39 39 39 6
Table B.11: Study two: Case one: Brodmann areas located with source localization for par-ticipant 14.
Left Arm Movement
Time [-2,-1.5) [-1.5,-1) [-1,-0.5) [-0.5,0) [0,0.5) [0.5,1) [1,1.5) [1.5,2)
sLORETA BA 10 11 7 11 7 5 8 7
MLE BA 22 22 22 22 10 10 10 47
MNE BA 22 22 22 22 10 10 10 47
wMNE BA 44 21 21 21 21 45 10 45
Right Arm Movement
Time [-2,-1.5) [-1.5,-1) [-1,-0.5) [-0.5,0) [0,0.5) [0.5,1) [1,1.5) [1.5,2)
sLORETA BA 10 11 7 11 8 5 10 5
MLE BA 47 22 47 10 47 47 22 47
MNE BA 47 22 47 10 47 47 22 47
wMNE BA 47 22 45 45 45 45 22 45
Table B.12: Study two: Case two: Brodmann areas located with source localization for par-ticipant 2.
Left Arm Movement
Time [-2,-1.5) [-1.5,-1) [-1,-0.5) [-0.5,0) [0,0.5) [0.5,1) [1,1.5) [1.5,2)
sLORETA BA 10 10 10 10 7 11 8 7
MLE BA 47 47 47 46 47 40 47 47
MNE BA 47 47 47 46 47 40 47 47
wMNE BA 47 20 47 45 47 40 47 47
Right Arm Movement
Time [-2,-1.5) [-1.5,-1) [-1,-0.5) [-0.5,0) [0,0.5) [0.5,1) [1,1.5) [1.5,2)
sLORETA BA 10 10 6 7 7 7 7 7
MLE BA 47 46 47 47 47 47 47 47
MNE BA 47 46 47 47 47 47 47 47
wMNE BA 47 45 47 47 47 47 45 47
Table B.13: Study two: Case two: Brodmann areas located with source localization for par-ticipant 3.
Left Arm Movement
Time [-2,-1.5) [-1.5,-1) [-1,-0.5) [-0.5,0) [0,0.5) [0.5,1) [1,1.5) [1.5,2)
sLORETA BA 4 4 7 7 7 7 40 22
MLE BA 22 22 47 47 47 47 22 47
MNE BA 22 22 47 47 47 47 22 47
wMNE BA 45 22 45 45 45 45 22 45
Right Arm Movement
Time [-2,-1.5) [-1.5,-1) [-1,-0.5) [-0.5,0) [0,0.5) [0.5,1) [1,1.5) [1.5,2)
sLORETA BA 6 7 7 7 7 21 7 2
MLE BA 47 46 47 47 47 47 47 47
MNE BA 47 46 47 47 47 47 47 47
wMNE BA 47 45 47 47 47 47 45 47
Table B.14: Study two: Case two: Brodmann areas located with source localization for par-ticipant 4.
Left Arm Movement
Time [-2,-1.5) [-1.5,-1) [-1,-0.5) [-0.5,0) [0,0.5) [0.5,1) [1,1.5) [1.5,2)
sLORETA BA 6 11 9 6 10 11 7 7
MLE BA 22 47 47 22 47 22 47 22
MNE BA 22 47 47 22 47 22 47 22
wMNE BA 22 45 45 22 45 22 45 21
Right Arm Movement
Time [-2,-1.5) [-1.5,-1) [-1,-0.5) [-0.5,0) [0,0.5) [0.5,1) [1,1.5) [1.5,2)
sLORETA BA 6 11 9 6 7 11 7 19
MLE BA 46 47 47 47 47 47 47 47
MNE BA 46 47 47 47 47 47 47 47
wMNE BA 45 45 45 45 45 45 45 45
Table B.15: Study two: Case two: Brodmann areas located with source localization for par-ticipant 5.
Left Arm Movement
Time [-2,-1.5) [-1.5,-1) [-1,-0.5) [-0.5,0) [0,0.5) [0.5,1) [1,1.5) [1.5,2)
sLORETA BA 47 4 46 8 8 39 6 19
MLE BA 47 47 19 47 47 47 47 19
MNE BA 47 47 19 47 47 47 47 19
wMNE BA 47 47 20 47 47 47 47 20
Right Arm Movement
Time [-2,-1.5) [-1.5,-1) [-1,-0.5) [-0.5,0) [0,0.5) [0.5,1) [1,1.5) [1.5,2)
sLORETA BA 6 6 8 4 3 4 5 7
MLE BA 47 47 47 47 47 47 47 47
MNE BA 47 47 47 47 47 47 47 47
wMNE BA 47 47 47 47 47 47 47 47
Table B.16: Study two: Case two: Brodmann areas located with source localization for par-ticipant 7.
Left Arm Movement
Time [-2,-1.5) [-1.5,-1) [-1,-0.5) [-0.5,0) [0,0.5) [0.5,1) [1,1.5) [1.5,2)
sLORETA BA 6 11 7 19 10 38 3 10
MLE BA 7 47 46 47 39 47 47 47
MNE BA 7 47 46 47 39 47 47 47
wMNE BA 5 45 44 47 20 45 45 45
Right Arm Movement
Time [-2,-1.5) [-1.5,-1) [-1,-0.5) [-0.5,0) [0,0.5) [0.5,1) [1,1.5) [1.5,2)
sLORETA BA 47 10 7 7 6 45 5 10
MLE BA 21 47 46 9 47 47 10 47
MNE BA 21 47 46 9 47 47 10 47
wMNE BA 20 47 45 9 47 45 47 45
Table B.17: Study two: Case two: Brodmann areas located with source localization for par-ticipant 8.
Left Arm Movement
Time [-2,-1.5) [-1.5,-1) [-1,-0.5) [-0.5,0) [0,0.5) [0.5,1) [1,1.5) [1.5,2)
sLORETA BA 9 10 10 10 10 10 21 21
MLE BA 9 9 9 21 9 9 21 22
MNE BA 9 9 9 21 9 9 21 22
wMNE BA 9 9 9 21 9 9 21 22
Right Arm Movement
Time [-2,-1.5) [-1.5,-1) [-1,-0.5) [-0.5,0) [0,0.5) [0.5,1) [1,1.5) [1.5,2)
sLORETA BA 10 18 18 18 18 18 18 18
MLE BA 9 9 9 18 18 18 18 18
MNE BA 9 9 9 18 18 18 18 18
wMNE BA 9 9 9 18 18 18 18 18
Table B.18: Study two: Case two: Brodmann areas located with source localization for par-ticipant 9.
Left Arm Movement
Time [-2,-1.5) [-1.5,-1) [-1,-0.5) [-0.5,0) [0,0.5) [0.5,1) [1,1.5) [1.5,2)
sLORETA BA 10 10 10 10 10 10 10 10
MLE BA 10 10 10 10 10 10 10 10
MNE BA 10 10 10 10 10 10 10 10
wMNE BA 10 10 10 10 10 10 10 10
Right Arm Movement
Time [-2,-1.5) [-1.5,-1) [-1,-0.5) [-0.5,0) [0,0.5) [0.5,1) [1,1.5) [1.5,2)
sLORETA BA 10 10 10 10 10 10 10 10
MLE BA 10 10 10 19 10 10 10 10
MNE BA 10 10 10 19 10 10 10 10
wMNE BA 10 10 10 19 10 10 10 10
Table B.19: Study two: Case two: Brodmann areas located with source localization for par-ticipant 10.
Left Arm Movement
Time [-2,-1.5) [-1.5,-1) [-1,-0.5) [-0.5,0) [0,0.5) [0.5,1) [1,1.5) [1.5,2)
sLORETA BA 10 10 10 10 10 10 10 10
MLE BA 9 9 9 9 9 9 9 9
MNE BA 9 9 9 9 9 9 9 9
wMNE BA 9 9 9 9 9 9 9 9
Right Arm Movement
Time [-2,-1.5) [-1.5,-1) [-1,-0.5) [-0.5,0) [0,0.5) [0.5,1) [1,1.5) [1.5,2)
sLORETA BA 10 10 10 10 10 10 10 10
MLE BA 9 9 9 9 9 9 9 9
MNE BA 9 9 9 9 9 9 9 9
wMNE BA 9 9 9 9 9 9 9 9
Table B.20: Study two: Case two: Brodmann areas located with source localization for par-ticipant 12.
Left Arm Movement
Time [-2,-1.5) [-1.5,-1) [-1,-0.5) [-0.5,0) [0,0.5) [0.5,1) [1,1.5) [1.5,2)
sLORETA BA 7 10 10 7 10 10 7 10
MLE BA 7 10 10 7 10 10 7 10
MNE BA 7 10 10 7 10 10 7 10
wMNE BA 7 45 10 7 10 10 7 10
Right Arm Movement
Time [-2,-1.5) [-1.5,-1) [-1,-0.5) [-0.5,0) [0,0.5) [0.5,1) [1,1.5) [1.5,2)
sLORETA BA 10 7 7 7 7 10 7 7
MLE BA 10 7 7 7 7 21 7 7
MNE BA 10 7 7 7 7 21 7 7
wMNE BA 10 7 7 7 7 21 7 7
Table B.21: Study two: Case two: Brodmann areas located with source localization for par-ticipant 13.
Left Arm Movement
Time [-2,-1.5) [-1.5,-1) [-1,-0.5) [-0.5,0) [0,0.5) [0.5,1) [1,1.5) [1.5,2)
sLORETA BA 8 8 39 8 39 8 8 8
MLE BA 39 8 39 8 39 8 8 8
MNE BA 39 8 39 8 39 8 8 8
wMNE BA 39 6 39 6 39 6 6 39
Right Arm Movement
Time [-2,-1.5) [-1.5,-1) [-1,-0.5) [-0.5,0) [0,0.5) [0.5,1) [1,1.5) [1.5,2)
sLORETA BA 8 8 39 8 39 39 39 8
MLE BA 39 8 39 8 39 39 39 8
MNE BA 39 8 39 8 39 39 39 8
wMNE BA 6 6 39 6 39 39 39 6
Table B.22: Study two: Case two: Brodmann areas located with source localization for par-ticipant 14.
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