**6. Conclusion**

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 arm motions.

#### **Acknowledgements**

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 amputees.

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