**5. Results and discussions**

In research, the neural-machine interfaces can have a control foundation of either a single modality or via hybrid activity. This present study dwells on capturing hemodynamic responses from the human brain and generating the control command that can be translated to activate a prosthetic arm for transhumeral amputees. The results found by this particular research are discussed as follows.

These hemodynamic states are mapped using nirsLAB in **Figure 8**. The color bar represents the concentration of oxygenation.

#### **5.1 Channel selection**

The changes in oxygenated hemoglobin ΔHbO for all 20 channels and six activities of subject 6 are demonstrated in **Figure 9**. All of the optodes were not capturing the true concentration changes while brain activity was performed by the subject. Nevertheless, it was observed that similar channels were active when identical motions were executed while signal acquisition.

The channel outputs in **Figure 8** serve to highlight the need for choosing good channels for recognizing true brain activities. According to our channel choice standard, signal averaging was used. It is understandable by human brain studies that when the right side of the human body is in motion, the left hemisphere of

#### **Figure 8.**

*The color bar represents the concentration of oxygenation. Brain activity is shown in (a) the illustration represents condition 1 i.e. motion. It can be seen that the motor region is "hot" when compared to the color bar. It depicts motion state whereas the (b) shows that the brain is undergoing significantly low or minimal hemodynamic changes hence the "cool" values depicting the condition 2 i.e. rest. The red and yellow dots seen in the motor cortex region are representations of optodes and they were positioned according to the international system.*

**63**

human brain.

**Figure 9.**

in decreased calculation time.

**5.2 Classification accuracy**

the subjects are shown in **Figure 10**.

tagged as uncounted or undetected.

*Control of a Prosthetic Arm Using fNIRS, a Neural-Machine Interface*

the human brain is activated. As in our case, the subjects were asked to move their right hand, it is obvious that the hemodynamic patterns occurring in the right hemisphere are merely noise. It can be seen in **Figure 8** that all the channels of right hemisphere i.e. channels 1–10 do not show significant activity and later they were discarded while classification. The left side of the motor cortex was however active and the channels from 11 to 20 were used to extract the statistical features which took part in the classification. Features were computed spatially which allowed the overall brain activity on the left side of the motor cortex region of the

Window sizing of diverse spans has been utilized in several studies for the detection of fNIRS features [50, 51]. It is intended to minimize the window size to generate a fast response for real-time applications. So, the time spans of 0–0.5, 0–1, and 0–2 second windows were selected. These split seconds were employed for investigation of hemodynamic features to secure the best window size that will aid

The stated performance outcomes were statistically evaluated based on the number of correctly predicted samples while the activity was completed during the period of 0–10 seconds. These actions were assessed by MATLAB® implementing the 10-fold cross-validation course. Student's t-test was performed to establish the statistical significance of the obtained results. The confidence interval was set to 95% (p < 0.05). The quantitative comparison between healthy subjects and amputees was not possible due to a limited number of amputees. The computed p-value was 0.0337 considering a 95% confidence interval. The classification accuracies of

A confusion matrix is illustrated in **Table 3** where it is evident that the wrist pronation and supination cause the most confusion. From time to time the subject executes the actions and sometimes put a break to them. In so doing the muscle intention power descend below the threshold and is not detected, and subsequently

In common practices, in addition to muscular fatigue, mental fatigue could likewise show up. This would affect the unwavering quality of the fNIRS signal. Frequent use of nicotine substances involving tea or coffee and weak eyesight are

*DOI: http://dx.doi.org/10.5772/intechopen.93565*

*Optode-wise hemodynamic status visualization.*

*Control of a Prosthetic Arm Using fNIRS, a Neural-Machine Interface DOI: http://dx.doi.org/10.5772/intechopen.93565*

**Figure 9.** *Optode-wise hemodynamic status visualization.*

*Data Acquisition - Recent Advances and Applications in Biomedical Engineering*

**5. Results and discussions**

**5.1 Channel selection**

represents the concentration of oxygenation.

motions were executed while signal acquisition.

these classifiers have different parameters and methodologies. They are not compared with each other here but they are implemented to grasp a comprehensive idea of how these different brain hemodynamic intentions can be evaluated. LDA and ANN were both applied separately and the outcomes are discussed in next section.

In research, the neural-machine interfaces can have a control foundation of either a single modality or via hybrid activity. This present study dwells on capturing hemodynamic responses from the human brain and generating the control command that can be translated to activate a prosthetic arm for transhumeral amputees.

These hemodynamic states are mapped using nirsLAB in **Figure 8**. The color bar

The changes in oxygenated hemoglobin ΔHbO for all 20 channels and six activities of subject 6 are demonstrated in **Figure 9**. All of the optodes were not capturing the true concentration changes while brain activity was performed by the subject. Nevertheless, it was observed that similar channels were active when identical

The channel outputs in **Figure 8** serve to highlight the need for choosing good channels for recognizing true brain activities. According to our channel choice standard, signal averaging was used. It is understandable by human brain studies that when the right side of the human body is in motion, the left hemisphere of

*The color bar represents the concentration of oxygenation. Brain activity is shown in (a) the illustration represents condition 1 i.e. motion. It can be seen that the motor region is "hot" when compared to the color bar. It depicts motion state whereas the (b) shows that the brain is undergoing significantly low or minimal hemodynamic changes hence the "cool" values depicting the condition 2 i.e. rest. The red and yellow dots seen in the motor cortex region are representations of optodes and they were positioned according to the international* 

The results found by this particular research are discussed as follows.

**62**

*system.*

**Figure 8.**

the human brain is activated. As in our case, the subjects were asked to move their right hand, it is obvious that the hemodynamic patterns occurring in the right hemisphere are merely noise. It can be seen in **Figure 8** that all the channels of right hemisphere i.e. channels 1–10 do not show significant activity and later they were discarded while classification. The left side of the motor cortex was however active and the channels from 11 to 20 were used to extract the statistical features which took part in the classification. Features were computed spatially which allowed the overall brain activity on the left side of the motor cortex region of the human brain.

Window sizing of diverse spans has been utilized in several studies for the detection of fNIRS features [50, 51]. It is intended to minimize the window size to generate a fast response for real-time applications. So, the time spans of 0–0.5, 0–1, and 0–2 second windows were selected. These split seconds were employed for investigation of hemodynamic features to secure the best window size that will aid in decreased calculation time.

#### **5.2 Classification accuracy**

The stated performance outcomes were statistically evaluated based on the number of correctly predicted samples while the activity was completed during the period of 0–10 seconds. These actions were assessed by MATLAB® implementing the 10-fold cross-validation course. Student's t-test was performed to establish the statistical significance of the obtained results. The confidence interval was set to 95% (p < 0.05). The quantitative comparison between healthy subjects and amputees was not possible due to a limited number of amputees. The computed p-value was 0.0337 considering a 95% confidence interval. The classification accuracies of the subjects are shown in **Figure 10**.

A confusion matrix is illustrated in **Table 3** where it is evident that the wrist pronation and supination cause the most confusion. From time to time the subject executes the actions and sometimes put a break to them. In so doing the muscle intention power descend below the threshold and is not detected, and subsequently tagged as uncounted or undetected.

In common practices, in addition to muscular fatigue, mental fatigue could likewise show up. This would affect the unwavering quality of the fNIRS signal. Frequent use of nicotine substances involving tea or coffee and weak eyesight are

#### *Data Acquisition - Recent Advances and Applications in Biomedical Engineering*

#### **Figure 10.**

*A representation of subject-wise accuracies. Healthy subjects are presented as S1–S15 whereas amputees are labeled A1–A3. All concerning classifying technique as LDA and ANN.*


#### **Table 3.**

*Confusion matrix of motion prediction.*

accounted for in such reliability. The motion prediction accuracy of an individual can be changed under the influence of such conditions.

#### **5.3 Control command generation**

After the results from the classifier are returned, they are translated into a control command. The motions are assigned abbreviations identical as used in Section 2. They are listed here as in **Table 4**.

While testing, the machine responds as the variables illustrated in **Table 4** that are assigned to each class motion. The controllers further single-out one definitive motion which can be analyzed while testing the data.

These control commands can be further translated to motor action via a controller such as Arduino, Raspberry Pi, Odroid, etc. as the program routine is written in a language supported by all these controllers. A three degree of freedom device can be actuated using this neural-machine interface scheme.

To eliminate the channel selection part, manufacturers are working on bundled optodes. Using these bundled optodes will not only eliminate the channel selection

**65**

*Control of a Prosthetic Arm Using fNIRS, a Neural-Machine Interface*

**Motion Notation** Extension of elbow EE Flexion of elbow EF Pronation of wrist WP Supination of wrist WS Flexion of wrist WF

complications but it will also help in physical attributes as the whole head will not be covered. It will be easy to wear the head cap because of a smaller number of

Also, using a different feature set may aid in the increase in accuracy. Rather than extracting three or four features, only one optimal feature can be evaluated. Hybridization of bio-signals can be done using advanced probability models or neural networks can be trained and implemented to hybridize different

fNIRS signals were acquired using the NIRSport machine developed by NIRx technology. These signals were recorded for six motions i.e. elbow extension (EE), elbow flexion (EF), wrist supination (WS), wrist pronation (WP), wrist extension (WE) and wrist flexion (WF) and were further analyzed. Mean and peak feature was extracted from the hemodynamic response of the brain. Also, minimum values were extracted for channel selection. The hemodynamic responses acquired from the brain were trained and tested by two widely used classifiers in pattern recognition i.e. LDA and ANN. The highest value of accuracy for an individual subject was recorded at 85% which is not yet achieved with six control commands employed by fNIRS. Both the classifiers were also active for real-time analysis. As a result of such high value of training accuracy, 8 out of 10 motions were correctly predicted in real-time setting. Possible extension of this work could be to hybridize these fNIRS signals together with another signal modality to not only increase the accuracy but also the number of control commands. Arm movement pattern for different age groups can be further explored. The number of amputed subjects could be increased to acquire data which will aid in better understanding of hemodynamic behavior of human brain and how it can be used to predict the

We would like to mention the funding body, i.e. Higher Education Commission (HEC) of Pakistan who awarded the grant under NRPU project number 10702. We would also like to show gratitude to the friends who connected us with the

*DOI: http://dx.doi.org/10.5772/intechopen.93565*

optodes and wires going around.

modalities.

**Table 4.** *Class labels.*

**6. Conclusion**

arm motions.

amputees.

**Acknowledgements**

*Control of a Prosthetic Arm Using fNIRS, a Neural-Machine Interface DOI: http://dx.doi.org/10.5772/intechopen.93565*


**Table 4.** *Class labels.*

*Data Acquisition - Recent Advances and Applications in Biomedical Engineering*

accounted for in such reliability. The motion prediction accuracy of an individual

**Output class 1 2 3 4 5 6**

*A representation of subject-wise accuracies. Healthy subjects are presented as S1–S15 whereas amputees are* 

0 0.0%

12 0.3%

**3648 73%**

630 12.6%

0 0.0%

0 0.0%

0 0.0%

0 0.0%

720 14.4%

**3617 72.1%**

0 0.0%

23 0.5%

0 0.0%

0 0.0%

0 0.0%

0 0.0%

**5001 99.8%**

> 9 0.2%

0 0.0%

0 0.0%

0 0.0%

0 0.0%

0 0.0%

**4978 99.3%**

0 0.0%

**4978 99.3%**

0 0.0%

20 0.4%

0 0.0%

0 0.0%

After the results from the classifier are returned, they are translated into a control command. The motions are assigned abbreviations identical as used in Section

While testing, the machine responds as the variables illustrated in **Table 4** that are assigned to each class motion. The controllers further single-out one definitive

These control commands can be further translated to motor action via a controller such as Arduino, Raspberry Pi, Odroid, etc. as the program routine is written in a language supported by all these controllers. A three degree of freedom device can

To eliminate the channel selection part, manufacturers are working on bundled optodes. Using these bundled optodes will not only eliminate the channel selection

can be changed under the influence of such conditions.

motion which can be analyzed while testing the data.

be actuated using this neural-machine interface scheme.

**5.3 Control command generation**

**Target class**

**Figure 10.**

1 **5010**

2 0

3 0

4 0

5 0

6 0

*Confusion matrix of motion prediction.*

**Table 3.**

**100.0%**

*labeled A1–A3. All concerning classifying technique as LDA and ANN.*

0.0%

0.0%

0.0%

0.0%

0.0%

2. They are listed here as in **Table 4**.

**64**

complications but it will also help in physical attributes as the whole head will not be covered. It will be easy to wear the head cap because of a smaller number of optodes and wires going around.

Also, using a different feature set may aid in the increase in accuracy. Rather than extracting three or four features, only one optimal feature can be evaluated. Hybridization of bio-signals can be done using advanced probability models or neural networks can be trained and implemented to hybridize different modalities.
